U.S. patent application number 12/987801 was filed with the patent office on 2011-06-09 for control device and system for controlling an actuated prosthesis.
This patent application is currently assigned to Victhom Human Bionics, Inc.. Invention is credited to Stephane Bedard.
Application Number | 20110137429 12/987801 |
Document ID | / |
Family ID | 31950541 |
Filed Date | 2011-06-09 |
United States Patent
Application |
20110137429 |
Kind Code |
A1 |
Bedard; Stephane |
June 9, 2011 |
CONTROL DEVICE AND SYSTEM FOR CONTROLLING AN ACTUATED
PROSTHESIS
Abstract
A device and system for controlling an actuated prosthesis. The
device includes a data signal input for each of the main artificial
proprioceptors. Also, means for obtaining a first and a second
derivative signal for at least some of the data signals, and means
for obtaining a third derivative signal for at least one of the
data signals. A set of first state machines, and means for
generating the phase of locomotion portion using the states of the
main artificial proprioceptors, and a second state machine as also
included. The system includes a plurality of main artificial
proprioceptors as well as means for obtaining a first and a second
derivative signal for at least some of the data signals, and means
for obtaining a third derivative signal for at least one of the
data signals. Also, a set of first state machines, a second state
machine, means for storing a lookup table, means for determining
actual coefficient values from the lookup table, means for
calculating at least one dynamic parameter value of the actuated
prosthesis using the lookup table and at least some of the data
signals, and means for converting the dynamic parameter value into
an output signal to control the actuated prosthesis.
Inventors: |
Bedard; Stephane; (Quebec,
CA) |
Assignee: |
Victhom Human Bionics, Inc.
Quebec
CA
|
Family ID: |
31950541 |
Appl. No.: |
12/987801 |
Filed: |
January 10, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11270684 |
Nov 9, 2005 |
7867284 |
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12987801 |
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|
10600725 |
Jun 20, 2003 |
7147667 |
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11270684 |
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|
60405281 |
Aug 22, 2002 |
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60424261 |
Nov 6, 2002 |
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60453556 |
Mar 11, 2003 |
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Current U.S.
Class: |
623/24 ;
623/39 |
Current CPC
Class: |
A61F 2002/762 20130101;
A61F 2002/6614 20130101; A61F 2002/7645 20130101; A61F 2/70
20130101; A61F 2002/705 20130101; A61F 2002/7625 20130101; A61F
2002/7635 20130101; A61F 2002/704 20130101; A61F 2/6607 20130101;
A61F 2002/763 20130101; A61F 2002/607 20130101; A61F 2/644
20130101; A61F 2002/7685 20130101; A61F 2002/701 20130101 |
Class at
Publication: |
623/24 ;
623/39 |
International
Class: |
A61F 2/48 20060101
A61F002/48; A61F 2/62 20060101 A61F002/62 |
Claims
1.-41. (canceled)
42. A motorized prosthetic device comprising: a joint member; a
limb member operatively coupled with the joint member; a pressure
sensor configured to indicate an interaction between the motorized
prosthetic device and ground; a kinematic sensor configured to
measure torque at the joint member; a controller configured to
receive data from at least the pressure sensor and kinematic sensor
and output a control signal based at least on the received data;
and an electrical motor configured to receive the control signal
and operate an actuator in accordance with the received control
signal.
43. The motorized prosthetic device of claim 42, wherein operation
of the actuator induces movement of the limb member.
44. The motorized prosthetic device of claim 42, wherein the
controller is further configured to calculate a trajectory of the
motorized prosthetic device using at least the data from the
kinematic sensor.
45. The motorized prosthetic device of claim 44, wherein the
controller is further configured to output the control signal based
at least on the calculated trajectory of the motorized prosthetic
device.
46. The motorized prosthetic device of claim 42, wherein the joint
member comprises a knee joint.
47. The motorized prosthetic device of claim 42, wherein the limb
member comprises a trans-tibial member.
48. The motorized prosthetic device of claim 42, wherein the
controller is further configured to determine a state of the
pressure sensor using at least a first derivative of at least a
portion of the data from the pressure sensor.
49. The motorized prosthetic device of claim 42, wherein the
controller is further configured to determine a phase of locomotion
using at least a portion of the data from the pressure sensor.
50. The motorized prosthetic device of claim 42, wherein the
controller is further configured to determine a portion of
locomotion using at least a portion of the data from the pressure
sensor.
51. A prosthetic controller configured to control a motorized
prosthetic device, the controller comprising: a first data signal
input configured to receive a pressure data signal indicative of an
interaction between a motorized prosthetic device and ground; a
second data signal input configured to receive a kinematic data
signal indicative of a torque at a joint of the prosthetic device;
and a processor configured to calculate a control signal based at
least on the pressure data signal and the kinematic data signal and
further configured to transmit the control signal to an electrical
motor, wherein the electrical motor operates an actuator in
accordance with the control signal.
52. The prosthetic controller of claim 51, wherein operation of the
actuator induces movement in a limb member of the motorized
prosthetic device.
53. The prosthetic controller of claim 51, wherein the processor is
further configured to calculate a trajectory of the motorized
prosthetic device using at least the data from the kinematic
signal.
54. The prosthetic controller of claim 53, wherein the processor is
further configured to calculate the control signal based at least
on the calculated trajectory of the motorized prosthetic
device.
55. The prosthetic controller of claim 51, wherein the joint is a
knee joint.
56. A method for controlling a motorized prosthetic device, the
method comprising: receiving a pressure data signal indicative of
an interaction between a prosthetic device and ground; receiving a
kinematic data signal indicative of a torque at a joint of the
prosthetic device; calculating, using one or more processors, a
control signal based at least on a portion of the pressure data
signal and at least a portion of the kinematic data signal;
transmitting the control signal to an electrical motor; and
operating an actuator in accordance with the calculated control
signal.
57. The method of claim 56, wherein operation of the actuator
induces movement in the motorized prosthetic device
58. The method of claim 56, further comprising calculating a
trajectory of the motorized prosthetic device using at least the
kinematic data signal.
59. The method of claim 58, wherein the control signal is
calculated based at least on the calculated trajectory of the
motorized prosthetic device.
60. The method of claim 56, further comprising determining a state
of a pressure sensor associated with the pressure data signal using
at least a first derivative of at least a portion of the pressure
data signal.
61. A prosthetic controller configured to control a motorized
prosthetic device, the controller comprising: a first layer
configured to calibrate a prosthetic device to a user; and a second
layer comprising at least one pressure sensor input and at least
one kinematic sensor input and configured to determine a phase of
locomotion using the at least one pressure sensor input and the at
least one kinematic sensor input, and further configured to operate
an actuator using an electrical motor based on the determined phase
of locomotion.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation of Ser. No.
11/270,684 filed Nov. 9, 2005 entitled "CONTROL DEVICE AND SYSTEM
FOR CONTROLLING AN ACTUATED PROSTHESIS," which is a divisional of
Ser. No. 10/600,725 filed Jun. 20, 2003, now issued as U.S. Pat.
No. 7,147,667, which claims the benefit of U.S. provisional patent
applications No. 60/405,281 filed Aug. 22, 2002; No. 60/424,261
filed Nov. 6, 2002; and No. 60/453,556 filed Mar. 11, 2003, all of
which are hereby incorporated by reference in their entirety.
TECHNICAL FIELD
[0002] The present invention relates to a control system and a
method for controlling an actuated prosthesis. This invention is
particularly well adapted for controlling an actuated leg
prosthesis for above-knee amputees.
BACKGROUND OF THE INVENTION
[0003] As is well known to control engineers, the automation of
complex mechanical systems is not something easy to achieve. Among
such systems, conventional powered artificial limbs, or myoelectric
prostheses, as they are more commonly referred to, are notorious
for having control problems. These conventional prostheses are
equipped with basic controllers that artificially mobilize the
joints without any interaction from the amputee and are only
capable of generating basic motions. Such basic controllers do not
take into consideration the dynamic conditions of the working
environment, regardless of the fact that the prosthesis is required
to generate appropriate control within a practical application.
They are generally lacking in predictive control strategies
necessary to anticipate the artificial limb's response as well as
lacking in adaptive regulation enabling the adjustment of the
control parameters to the dynamics of the prosthesis. Because human
limb mobility is a complex process including voluntary, reflex and
random events at the same time, conventional myoelectric prostheses
do not have the capability to interact simultaneously with the
human body and the external environment in order to have minimal
appropriate functioning.
[0004] For example, in the case of artificial leg prostheses for
above-knee amputees, the complexity of human locomotion resulted in
that the technical improvements of conventional leg prostheses have
until now been focused on passive mechanisms. This proved to be
truly detrimental to the integration of motorized leg prostheses
onto the human body. According to amputees, specific conditions of
use of conventional leg prostheses, such as repetitive movements
and continuous loading, typically entail problems such as increases
in metabolic energy expenditures, increases of socket pressure,
limitations of locomotion speeds, discrepancies in the locomotion
movements, disruptions of postural balance, disruptions of the
pelvis-spinal column alignment, and increases in the use of
postural clinical rehabilitation programs.
[0005] The major problem remains that the energy used during
mobility mainly stems from the user because conventional leg
prostheses are not equipped with servomechanisms that enable
self-propulsion. This energy compensation has considerable short
and long-term negative effects resulting from the daily use of such
prostheses. Accordingly, the dynamic role played by the stump
during locomotion renders impossible the prolonged wearing of the
prostheses as it may create, among other things, several skin
problems such as folliculitis, contact dermatitis, edema, cysts,
skin shearing, scarring and ulcers. Although these skin problems
may be partially alleviated by using a silicone sheath, a complete
suction socket, or powder, skin problems remain one of the major
preoccupations today.
[0006] As well, the passive nature of the conventional leg
prostheses typically leads to movement instability, disrupted
movement synchronism and reduced speed of locomotion. Recent
developments in the field of energy-saving prosthetic components
have partially contributed to improve energy transfer between the
amputee and the prosthesis. Nevertheless, the problem of energy
expenditure is still not fully resolved and remains the major
concern.
[0007] Considering this background, it clearly appears that there
was a need to develop an improved control system and a new method
for controlling an actuated prosthesis in order to fulfill the
needs of amputees, in particular those of above-knee amputees.
SUMMARY OF THE INVENTION
[0008] In accordance with one aspect of the present invention,
there is provided a method for determining a portion of locomotion
and a phase of locomotion portion in view of controlling an
actuated prosthesis in real time, the method comprising:
[0009] providing a plurality of main artificial proprioceptors;
[0010] receiving a data signal from each of the main artificial
proprioceptors;
[0011] obtaining a first and a second derivative signal for each
data signal;
[0012] obtaining a third derivative signal for at least one of the
data signals;
[0013] using a set of a first state machines to select one state
among a plurality of possible states for each main artificial
proprioceptor with the corresponding data and derivative
signals;
[0014] generating the phase of locomotion portion using the states
of the main artificial proprioceptors; and
[0015] using a second state machine to select the portion of
locomotion among a plurality of possible portions of locomotion
using events associated to the data signals.
[0016] In accordance with another aspect of the present invention,
there is provided a method for controlling an actuated prosthesis
in real time, the method comprising:
[0017] providing a plurality of main artificial proprioceptors;
[0018] receiving a data signal from each of the main artificial
proprioceptors;
[0019] obtaining a first and a second derivative signal for each
data signal;
[0020] obtaining a third derivative signal for at least one of the
data signals;
[0021] using a set of first state machines to select one state
among a plurality of possible states for each main artificial
proprioceptor with the corresponding data and derivative
signals;
[0022] generating the phase of locomotion portion using the states
of the main artificial proprioceptors;
[0023] using a second state machine to select the portion of
locomotion among a plurality of possible portions of locomotion
using events associated to the data signals;
[0024] calculating a locomotion speed value;
[0025] determining coefficient values from a lookup table using at
least the phase of locomotion portion, the portion of locomotion
and the locomotion speed value;
[0026] calculating at least one dynamic parameter value of the
actuated prosthesis using the coefficient values from the lookup
table; and
[0027] converting the dynamic parameter value into an output signal
to control the actuated prosthesis.
[0028] In accordance with a further aspect of the present
invention, there is provided a device for determining a portion of
locomotion and a phase of locomotion portion in view of controlling
an actuated prosthesis in real time using a plurality of main
artificial proprioceptors, the device comprising:
[0029] a data signal input for each of the main artificial
proprioceptors;
[0030] means for obtaining a first and a second derivative signal
for each data signal;
[0031] means for obtaining a third derivative signal for at least
one of the data signals;
[0032] a set of first state machines, the first state machines
being used to select one state among a plurality of possible states
for each main artificial proprioceptor with the corresponding data
and derivative signals;
[0033] means for generating the phase of locomotion portion using
the states of the main artificial proprioceptors; and
[0034] a second state machine, the second state means being used to
select the portion of locomotion among a plurality of possible
portions of locomotion using events associated to the data
signals.
[0035] In accordance with a further aspect of the present
invention, there is provided a control system for controlling an
actuated prosthesis in real time, the system comprising:
[0036] a plurality of main artificial proprioceptors;
[0037] means for obtaining a first and a second derivative signal
for each data signal;
[0038] means for obtaining a third derivative signal for at least
one of the data signals;
[0039] a set of first state machines, the first state machines
being used to select one state among a plurality of possible states
for each main artificial proprioceptor with the corresponding data
and derivative signals;
[0040] means for generating the phase of locomotion portion using
the states of the main artificial proprioceptors;
[0041] a second state machine, the second state machine being used
to select the portion of locomotion among a plurality of possible
portions of locomotion using events associated to data signals;
[0042] means for calculating a locomotion speed value;
[0043] means for storing a lookup table comprising coefficient
values with reference to at least phases of locomotion, portions of
locomotion and locomotion speed values;
[0044] means for determining actual coefficient values from the
lookup table using at least the phase of locomotion portion, the
portion of locomotion and the locomotion speed value;
[0045] means for calculating at least one dynamic parameter value
of the actuated prosthesis using the coefficient values from the
lookup table; and
[0046] means for converting the dynamic parameter value into an
output signal to control the actuated prosthesis.
[0047] These and other aspects of the present invention are
described in or apparent from the following detailed description,
which description is made in conjunction with the accompanying
figures.
BRIEF DESCRIPTION OF THE FIGURES
[0048] FIG. 1 is a block diagram showing the control system in
accordance with a preferred embodiment of the present
invention;
[0049] FIG. 2 is a perspective view of an example of an actuated
prosthesis with a front actuator configuration;
[0050] FIG. 3 is a perspective view of an example of an actuated
prosthesis with a rear actuator configuration;
[0051] FIG. 4 is an upper schematic view of an insole provided with
plantar pressure sensors;
[0052] FIG. 5 is a cross sectional view of a sensor shown in FIG.
4;
[0053] FIG. 6 is an example of a state machine diagram for the
selection of the portion of locomotion;
[0054] FIG. 7 is an example of the phases of locomotion portion
within one portion of locomotion (BTW) in the state machine diagram
shown in FIG. 6;
[0055] FIGS. 8a to 8d are examples of four data signals using
plantar pressure sensors during typical walking on flat ground;
[0056] FIGS. 9a to 9d give an example of a data signal obtained
from a plantar pressure sensor at the calcaneus region and its
first three differentials;
[0057] FIGS. 10a to 10d give an example of a data signal obtained
from a plantar pressure sensor at the metatarsophalangeal (MP)
region and its first three differentials;
[0058] FIGS. 11a to 11d give an example of the states of a plantar
pressure sensor with reference to the data signal and its three
first differentiations for a plantar pressure sensor at the
calcaneous region;
[0059] FIGS. 12a to 12c give an example of the states of a plantar
pressure sensor with reference to the data signal and its three
first differentiation for a plantar pressure sensor at the
metatarsophalangeal (MP)region;
[0060] FIG. 13 is an example of a state machine diagram for the
selection of the state of the plantar pressure sensors for the
calcaneous region;
[0061] FIG. 14 is an example of a state machine diagram for the
selection of the state of the plantar pressure sensors at the
metatarsophalangeal (MP) region;
[0062] FIG. 15 is an overall block diagram of the Phase Recognition
Module (PRM);
[0063] FIG. 16 is a block diagram showing the zero calibration;
[0064] FIG. 17 is a block diagram showing the subject's weight
calibration;
[0065] FIG. 18 is a block diagram of the Trajectory Generator
(TG);
[0066] FIG. 19 is a block diagram showing the creation of the
Trajectory Generator (TG) lookup table;
[0067] FIG. 20 is a graph showing an example of curve representing
a kinematic or kinetic variable for a given portion of locomotion,
phase of locomotion portion and subject's speed; and
[0068] FIG. 21 is an enlarged representation of FIG. 20.
ACRONYMS
[0069] The detailed description and figures refer to the following
technical acronyms:
[0070] A/D Analog/Digital
[0071] BDW "Downward Inclined Walking-Beginning path" portion of
locomotion
[0072] BGD "Going Down Stairs-Beginning path" portion of
locomotion
[0073] BGU "Going Up Stairs-Beginning path portion of
locomotion
[0074] BTW "Linear Walking-Beginning path" portion of
locomotion
[0075] BTW_SWING Detection of typical walking
g.sub.r.sub.--.sub.leg during leg swing
[0076] BUW "Upward Inclined Walking-Beginning path" portion of
locomotion
[0077] CDW "Downward Inclined Walking-Cyclical path" portion of
locomotion
[0078] CGD "Going Down Stairs-Cyclical path" portion of
locomotion
[0079] CGU "Going Up Stairs-Cyclical path" portion of
locomotion
[0080] CTW "Linear Walking-Cyclical path" portion of locomotion
[0081] CUW "Upward Inclined Walking-Cyclical path" portion of
locomotion
[0082] ECW "Curve Walking Path" portion of locomotion
[0083] EDW "Downward Inclined Walking-Ending path" portion of
locomotion
[0084] EGD "Going Down Stairs-Ending path" portion of
locomotion
[0085] EGU "Going Up Stairs-Ending path" portion of locomotion
[0086] ETW "Linear Walking-Ending path" portion of locomotion
[0087] EUW "Upward Inclined Walking-Ending path" portion of
locomotion
[0088] FR_BIN.sub.x Detection of a positive f.sub.rx
[0089] FRfst_BINz.sub.x Detection of positive first differentiation
of f.sub.rx
[0090] FRsec_BIN.sub.x Detection of positive second differentiation
of f.sub.rx
[0091] FRtrd_BIN.sub.x Detection of positive third differentiation
of f.sub.rx
[0092] FR_HIGH.sub.x Detection of f.sub.rx level above the STA
envelope
[0093] FR_LOW.sub.x Detection of f.sub.rx level between the zero
envelope and the STA envelope
[0094] FSR Force Sensing Resistor
[0095] GR_POS.sub.y Detection of a positive g.sub.ry
[0096] MIN_SIT Detection of a minimum time in portion SIT
[0097] MP Metatarsophalangeal
[0098] PID Proportional-Integral-Differential
[0099] PKA_SDWSit down knee angle
[0100] PKA_ETWEnd walking knee angle
[0101] PKA_STA Stance knee angle
[0102] PKA_SIT Sit down knee angle
[0103] PKA_SUP_RAMPStanding up knee angle
[0104] PPMV Plantar Pressure Maximal Variation
[0105] PPS Plantar Pressure Sensor
[0106] PRM Phase Recognition Module
[0107] REG Regulator
[0108] RF Radio Frequency
[0109] SDW "Sitting down" portion of locomotion
[0110] SIT "Sitting" portion of locomotion
[0111] STA "Stance of feet" portion of locomotion
[0112] STA BIN Detection of a static evolution of all f.sub.rx
[0113] STATIC_GR.sub.y Detection of g.sub.ry level below the zero
angular speed envelope and the zero acceleration envelope
[0114] sum.sub.a Localized plantar pressure signal of left foot
[0115] sum.sub.b Localized plantar pressure signal of right
foot
[0116] sum.sub.c Localized plantar pressure signal of both
calcaneus
[0117] sum.sub.d Localized plantar pressure signal of both MP
[0118] sum.sub.e Localized plantar pressure signal of both feet
[0119] SUMBIN.sub.y Non-Zero of sum.sub.y
[0120] SUP "Standing Up" portion of locomotion
[0121] SVD Singular Values Decomposition
[0122] SWING.sub.y Detection of a swing prior to a foot strike
[0123] TG Trajectory Generator
[0124] XHLSB Heel Loading State Bottom (X=Left (L) or Right
(R))
[0125] XHLSM Heel Loading State Middle (X=Left (L) or Right
(R))
[0126] XHLST Heel Loading State Top (X=Left (L) or Right (R))
[0127] XHSTA Heel STAtic state (X=Left (L) or Right (R))
[0128] XHUSB Heel Unloading State Bottom (X=Left (L) or Right
(R))
[0129] XHUST Heel Unloading State Top (X=Left (L) or Right (R))
[0130] XHZVS Heel Zero Value State (X=Left (L) or Right (R))
[0131] XMLSM MP Loading State Middle (X=Left (L) or Right (R))
[0132] XMLST MP Loading State Top (X=Left (L) or Right (R))
[0133] XMSTA MP STAtic state (X=Left (L) or Right (R))
[0134] XMUSB MP Unloading State Bottom (X=Left (L) or Right
(R))
[0135] XMUST MP Unloading State Top (X=Left (L) or Right (R))
[0136] XMZVS MP Zero Value State (X=Left (L) or Right (R))
[0137] ZV_FRfst.sub.x Threshold to consider the first
differentiation of f.sub.rx to be positive.
[0138] ZV_FRsec.sub.x Threshold to consider the second
differentiation of f.sub.rx to be positive.
[0139] ZV_FRtrd.sub.x Threshold to consider the third
differentiation of f.sub.rx to be positive.
[0140] ZV_FR.sub.x Threshold to consider f.sub.rs to be
positive
[0141] ZV_SUMfst Threshold to consider the absolute value of the
1.sup.st diff. of sum.sub.y to be positive.
[0142] ZV_SUMsec Threshold to consider the absolute value of the
2.sup.nd diff. of sum.sub.y to be positive
DETAILED DESCRIPTION OF THE INVENTION
[0143] The appended figures show a control system (10) in
accordance with the preferred embodiment of the present invention.
It should be understood that the present invention is not limited
to the illustrated implementation since various changes and
modifications may be effected herein without departing from the
scope of the appended claims.
[0144] FIG. 1 shows the control system (10) being combined with an
autonomous actuated prosthesis for amputees. It is particularly
well adapted for use with an actuated leg prosthesis for above-knee
amputees, such as the prostheses (12) shown in FIGS. 2 and 3.
Unlike conventional prostheses, these autonomous actuated
prostheses (12) are designed to supply the mechanical energy
necessary to move them by themselves. The purpose of the control
system (10) is to provide the required signals allowing to control
an actuator (14). To do so, the control system (10) is interfaced
with the amputee using artificial proprioceptors (16) to ensure
proper coordination between the amputee and the movements of the
actuated prosthesis (12). The set of artificial proprioceptors (16)
captures information, in real time, about the dynamics of the
amputee's movement and provides that information to the control
system (10). The control system (10) is then used to determine the
joint trajectories and the required force or torque that must be
applied by the actuator (14) in order to provide coordinated
movements.
[0145] FIG. 2 shows an example of an actuated leg prosthesis (12)
for an above-knee amputee. This prosthesis (12) is powered by a
linear actuator (14). The actuator (14) moves a knee member (20)
with reference to a trans-tibial member (22), both of which are
pivotally connected using a first pivot axis. More sophisticated
models may be equipped with a more complex pivot or more than one
pivot at that level.
[0146] An artificial foot (24) is provided under a bottom end of
the trans-tibial member (22). The knee member (20) comprises a
connector (25) to which a socket (26) can be attached. The socket
(26) is used to hold the sump of the amputee. The design of the
knee member (20) is such that the actuator (14) has an upper end
connected to another pivot on the knee member (20). The bottom end
of the actuator (14) is then connected to a third pivot at the
bottom end of the trans-tibial member (22). In use, the actuator
(14) is operated by activating an electrical motor therein. This
rotates, in one direction or another, a screw (28). The screw (28)
is then moved in or out with reference to a follower (30), thereby
changing the relative angular position between the two movable
parts, namely the knee member (20) and the trans-tibial member
(22).
[0147] FIG. 3 shows an actuated leg prosthesis (12) in accordance
to a rear actuator configuration. This embodiment is essentially
similar to that of FIG. 2 and is illustrated with a different model
of actuator (14).
[0148] It should be noted that the present invention is not limited
to the mechanical configurations illustrated in FIGS. 2 and 3. The
control system (10) may be used with a leg prosthesis having more
than one joint. For instance, it can be used with a prosthesis
having an ankle joint, a metatarsophalangeal joint or a hip joint
in addition to a knee joint. Moreover, instead of a conventional
socket a osseo-integrated devices could also be used, ensuring a
direct attachment between the mechanical component of the
prosthesis and the amputee skeleton. Other kinds of prostheses may
be used as well.
[0149] Referring back to FIG. 1, the information provided by the
artificial proprioceptors (16) are used by the control system (10)
to generate an output signal. These output signals are preferably
sent to the actuator (14) via a power drive (32) which is itself
connected to a power supply (34), for instance a battery, in order
to create the movement. The power drive (32) is used to control the
amount of power being provided to the actuator (14). Since the
actuator (14) usually includes an electrical motor, the power drive
(32) generally supplies electrical power to the actuator (14) to
create the movement.
[0150] Preferably, feedback signals are received from sensors (36)
provided on the prosthesis (12). In the case of an actuated leg
prosthesis (12) such as the one illustrated in FIGS. 2 and 3, these
feedback signals may-indicate the relative position measured
between two movable parts and the torque between them. This option
allows the control system (10) to adequately adjust the output
signal. Other types of physical parameters may be monitored as
well.
[0151] The control system (10) shown in FIG. 1 comprises an
interface (40) through which data signals coming from the
artificial proprioceptors (16) are received. They may be received
either from an appropriate wiring or from a wireless transmission.
In the case of actuated leg prostheses for above-knee amputees,
data signals from the artificial proprioceptors (16) provided on a
healthy leg are advantageously sent through the wireless
transmission using an appropriate RF module. For example, a simple
off-the-shelf RF module with a dedicated specific frequency, such
as 916 MHz, may be used. For a more robust implementation though,
the use of a RF module with a spread spectrum or frequency hopper
is preferable. Of course, other configurations may be used as well,
such as a separate A/D converter, different resolution or sampling
values and various combinations of communication link technologies
such as wired, wireless, optical, etc.
[0152] The control system (10) further comprises a part called
"Phase Recognition Module" or PRM (42). The PRM (42) is a very
important part of the control system (10) since it is used to
determine two important parameters, namely the portion of
locomotion and the phase of locomotion portion. These parameters
are explained later in the text. The PRM (42) is connected to a
Trajectory Generator, or TG (44), from which dynamic parameters
required to control the actuated prosthesis (12) are calculated to
create the output signal. A lookup table (6) is stored in a memory
connected to the TG (44). Moreover, the control system (10)
comprises a regulator (48) at which the feedback signals are
received and the output signal can be adjusted.
[0153] Software residing on an electronic circuit board contains
all the above mentioned algorithms enabling the control system (10)
to provide the required signals allowing to control the actuator
(14). More specifically, the software contains the following three
modules: the Phase Recognition Module (PRM), the Trajectories
Generator (TG) and the Regulator (REG). Of course, any number of
auxiliary modules may be added.
[0154] The artificial proprioceptors (16) preferably comprise main
artificial proprioceptors and auxiliary artificial proprioceptors.
The main artificial proprioceptors are preferably localized plantar
pressure sensors which measure the vertical plantar pressure of a
specific underfoot area, while the auxiliary artificial
proprioceptors are preferably a pair of gyroscopes which measure
the angular speed of body segments of the lower extremities and a
kinematic sensor which measures the angle of the prosthesis knee
joint. The plantar pressure sensors are used under both feet,
including the artificial foot. It could also be used under two
artificial feet if required. One of the gyroscopes is located at
the shank of the normal leg while the other is located on the upper
portion of the prosthesis above the knee joint. As for the
kinematic sensor, it is located at the prosthesis knee joint. Other
examples of artificial proprioceptors (16) are neuro-sensors which
measure the action potential of motor nerves, myoelectrical
electrodes which measure the internal or the external myoelectrical
activity of muscles, needle matrix implants which measure the
cerebral activity of specific region of the cerebrum cortex such as
motor cortex or any other region indirectly related to the somatic
mobility of limbs or any internal or external kinematic and/or
kinetic sensors which measure the position and the torque at any
joints of the actuated prosthesis. Of course, depending on the
application, additional types of sensors which provide information
about various dynamics of human movement may be used.
[0155] FIG. 4 shows a right insole (10) provided with two plantar
pressure sensors (16) positioned at strategic locations. Their size
and position were defined in accordance with the stability and the
richness (intensity) of the localized plantar pressure signals
provided by certain underfoot areas during locomotion.
Experimentation provided numerous data concerning the spatial
distribution of foot pressures and more specifically on the Plantar
Pressure Maximal Variation (PPMV) during locomotion. The PPMV,
denoted .DELTA..sub.maxf.sub.r,ij, was defined as the maximum
variation of the plantar pressure at a particular point (underfoot
area of coordinate i,j) during locomotion. The X-Y axis (52) in
FIG. 4 was used to determine the i,j coordinates of each underfoot
area.
[0156] A PPMV of a given underfoot area of coordinates i,j during a
given step denoted event x, is defined as stable, through a set of
N walking steps, if the ratio of the absolute difference between
this PPMV and the average PPMV over the set is inferior to a
certain value representing the criteria of stability, thus:
( .DELTA. max f r , ij x - n = 0 N .DELTA. max f r , ij n N n = 0 N
.DELTA. max f r , ij n N ) 100 % .ltoreq. ( S % ) Equation 1
##EQU00001## [0157] where .DELTA..sub.maxf.sub.r,ij|.sub.x is the
PPMV localized at underfoot area of coordinates i, j during the
event x, thus [0158]
.DELTA..sub.maxf.sub.r,ij|.sub.x=f.sub.r,ij.sup.max(k)|.sub.k.fwdarw.0
to K for the event x [0159] K is the number of samples (frames),
[0160] N is the number of steps in the set, [0161] S is the chosen
criteria to define if a given PPMV is stable.
[0162] A PPMV of a given underfoot area of coordinates i,j during a
given step denoted event x, is defined as rich in information,
through a set of N walking steps, if the ratio between the PPMV and
the average PPMV of the set is superior to certain value
representing the criteria of richness thus:
.DELTA. max f r , ij x .gtoreq. ( R % ) ( n = 0 N .DELTA. max f r ,
ij n N ) max i , j Equation 2 ##EQU00002## [0163] where
.DELTA..sub.maxf.sub.r,ij|.sub.x is the PPMV localized at underfoot
area of coordinates i, j during the event x, thus [0164]
.DELTA..sub.maxf.sub.r,ij|.sub.x-f.sub.r,ij.sup.max(k)|.sub.k.fwdarw.0
to K-f.sub.r,ij.sup.min(k)|.sub.k.fwdarw.0 to K for the event x
[0165] K is the number of samples (frames), [0166] N is the number
of steps in the set, [0167] R is the chosen criteria to define if a
given PPMV is rich in information.
[0168] It was found by experimentation that the size and the
position of plantar pressure sensor are well defined when the
criteria are set at 5% and 10% for the stability and the richness
PPMV respectively. As a result, it was found that the calcaneus and
the Metatarsophalangeal (MP) regions are two regions of the foot
sole where the PPMV may be considered as providing a signal that is
both stable and rich in information.
[0169] In FIG. 4, the plantar pressure sensors (16) are provided in
a custom-made insole (10), preferably in the form of a standard
orthopedic insole, that is modified to embed the two sensors (16)
for the measurement of two localized plantar pressures. Each sensor
(16), as shown in FIG. 5, is preferably composed of a thin
Force-Sensing Resistor (FSR) polymer cell (54) directly connected
to the interface (40) or indirectly using an intermediary system
(not shown), for instance a wireless emitter. Mechanical adapters
may be used if FSR cells of appropriate size are not available. The
FSR cell (54) has a decreasing electrical resistance in response to
an increasing force applied perpendicularly to the surface thereof.
Each cell (54) outputs a time variable electrical signal for which
the intensity is proportional to the total vertical plantar
pressure over its surface area.
[0170] The normalized position of the pressure sensors and their
size are shown in Table 1, where the length L and the width W are
respectively the length and the width of the subject's foot. The
coefficients in Table 1 have been obtained by experimentation. A
typical diameter for the plantar pressure sensors (16) is between
20 and 30 mm.
TABLE-US-00001 TABLE 1 Normalized position and size of pressure
sensors Area Position (X, Y) Size (diameter) Calcaneus (0.51 W,
0.14 L) 0.29 {square root over (L W)} MP (0.7 W, 0.76 L) 0.24
{square root over (L W)}
[0171] In use, the PRM (42) ensures, in real-time, the recognition
of the phase of locomotion portion and the portion of locomotion of
an individual based on the information provided by the artificial
proprioceptors (16). The PRM (42) is said to operate in real time,
which means that the computations and other steps are performed
continuously and with almost no delay.
[0172] In accordance with the present invention, it was found that
data signals received from individual artificial proprioceptors
(16) can provide enough information in order to control the
actuator (14) of an actuated prosthesis (12). For instance, in the
case of plantar pressure sensors, it has been noticed
experimentally that the slope (first derivative), the sign of the
concavity (second derivative) and the slope of concavity (third
derivative) of the data signals received from plantar pressure
sensors, and of combinations of those signals, give highly accurate
and stable information on the human locomotion. The PRM (42) is
then used to decompose of the human locomotion into three levels,
namely the states of each artificial proprioceptor (16), the phase
of locomotion portion and the portion of locomotion. This breakdown
ensures the proper identification of the complete mobility dynamics
of the lower extremities in order to model the human
locomotion.
[0173] The actual states of each main artificial proprioceptor
depict the first level of the locomotion breakdown. This level is
defined as the evolution of the main artificial proprioceptors'
sensors during the mobility of the lower extremities. Each sensor
has its respective state identified from the combination of its
data signal and its first three differential signals. For the main
artificial proprioceptors of the preferred embodiment, which
provide information about localized plantar pressures, it has been
discovered experimentally that the localized plantar pressures
signals located at the calcaneous and at the metatarsophalangeal
(MP) regions may be grouped into seven and six states
respectively.
[0174] For the sensors at the calcaneous regions, the states are
preferably as follows:
[0175] XHLSB Heel Loading State Bottom (X=Left (L) or Right
(R))
[0176] XHLSM Heel Loading State Middle (X=Left (L) or Right
(R))
[0177] XHLST Heel Loading State Top (X=Left (L) or Right (R))
[0178] XHSTA Heel STAtic State (X=Left (L) or Right (R))
[0179] XHUSB Heel Unloading State Bottom (X=Left (L) or Right
(R))
[0180] XHUST Heel Unloading State Top (X=Left (L) or Right (R))
[0181] XHZVS Heel Zero Value State (X=Left (L) or Right (R))
[0182] For the sensors at the MP regions, the states are preferably
as follows:
[0183] XMLSB MP Loading State Bottom (X=Left (L) or Right (R))
[0184] XMLST MP Loading State Top (X=Left (L) or Right (R))
[0185] XMSTA MP STAtic State (X=Left (L) or Right (R))
[0186] XMUSB MP Unloading State Bottom (X=Left (L) or Right
(R))
[0187] XMUST MP Unloading State Top (X=Left (L) or Right (R))
[0188] XMZVS MP Zero Value State (X=Left (L) or Right (R))
[0189] Identifying the states at each sensor allows to obtain the
second level of the locomotion breakdown, referred to as the phase
of locomotion portion. The phase of locomotion portion is defined
as the progression of the subject's mobility within the third level
of locomotion breakdown, namely the portion of locomotion. This
third level of the locomotion breakdown defines the type of
mobility the subject is currently in, such as, for example,
standing, sitting or climbing up stairs. Each locomotion portion
contains a set of sequential phases illustrating the progression of
the subject's mobility within that locomotion portion. The phase
sequence mapping for each locomotion portion has been identified by
experimentation according to the evolution of the state of the
localized plantar pressures throughout the portion.
[0190] The portions of locomotion are preferably as follows:
[0191] BDW "Downward Inclined Walking-Beginning path"
[0192] BGD "Going Down Stairs-Beginning path"
[0193] BGU "Going Up Stairs-Beginning path
[0194] BTW "Linear Walking-Beginning path"
[0195] BUW "Upward Inclined Walking Beginning path"
[0196] CDW "Downward Inclined Walking-Cyclical path"
[0197] CGD "Going Down Stairs-Cyclical path"
[0198] CGU "Going Up Stairs-Cyclical path"
[0199] CTW "Linear Walking-Cyclical path"
[0200] CUW "Upward Inclined Walking-Cyclical path"
[0201] ECW "Curve Walking Path"
[0202] EDW "Downward Inclined Walking-Ending path"
[0203] EGD "Going Down Stairs-Ending path"
[0204] EGU "Going Up Stairs-Ending path"
[0205] ETW "Linear Walking-Ending path"
[0206] EUW "Upward Inclined Walking-Ending path"
[0207] SDW "Sitting down"
[0208] SIT "Sitting"
[0209] STA "Stance of feet"
[0210] SUP "Standing Up"
[0211] FIG. 6 illustrates an example of the state machine
concerning these various portions of locomotion.
[0212] FIG. 7 shows an example of a phase sequence mapping, BTW_1
to BTW_25, for the Beginning Path of Linear Walking (BTW) portion
of locomotion. All locomotion portions have similar patterns of
phase sequence mapping, though the number of phases may vary from
one locomotion portion to another. The number of phases depends on
the desired granularity of the decomposition of the locomotion
portion. The phases are determined experimentally by observing the
states of the four localized plantar pressures at specific time
intervals, which are determined by the desired granularity. Since a
phase is the combination of the states of the four localized
plantar pressures, the phase boundary conditions are therefore
defined as the combination of each localized plantar pressure state
boundary conditions.
[0213] For the selection of the portion of locomotion the subject
is in, the algorithm uses the state machine approach. For this
purpose, the algorithm uses a set of events which values define the
conditions, or portion boundary conditions, to pass from one
locomotion portion to another. These events are identified by
experimentation according to the evolution of the localized plantar
pressure signals, the complementary signals and their first three
differentials, as well as the signals from the auxiliary artificial
proprioceptors, when the subject passes from one locomotion portion
to another.
[0214] Having determined the states of the main artificial
proprioceptors' sensors, the phase of locomotion portion and
portion of locomotion of the subject, the TG (44) can be used to
calculate one or more dynamic parameter values to be converted to
an output signal for the control of the actuator. Examples of
dynamic parameter values are the angular displacement and the
torque (or moment of force) at the knee joint of the actuated leg
prosthesis (12). Since these values are given in real time, they
provide what is commonly referred to as the "system's trajectory".
At any time k during the subject's locomotion, a mathematical
relationship is selected according to the state of the whole
system, that is the states of the main artificial proprioceptors,
the phase of locomotion portion, the portion of locomotion and the
walking speed. Following which, the angular displacement
.theta..sub.kn and the moment of force m.sub.kn are then computed
using simple time dependant equations and static characteristics
associated with the state of the system, thereby providing the
joint's trajectory to the knee joint member. This process is
repeated throughout the subject's locomotion.
[0215] FIGS. 8a to 8d show examples of data signals from the four
localized plantar pressure sensors (16) during a standard walking
path at 109,5 steps/minute. The four signals, f.sub.r1(t),
f.sub.r2(t), f.sub.r3(t) and f.sub.r4(t), correspond to the
variation in time of the localized plantar pressure at the
calcaneus region of the left foot (FIG. 8a), the MP region of the
left foot (FIG. 8b), the calcaneus region of the right foot (FIG.
8c), and the MP region of the right foot (FIG. 8d).
[0216] In accordance with the present invention, the PRM (42) uses
the first, the second and the third differentials of each of those
four localized plantar pressure signals in order to determine the
sensors' state. From there, the PRM (42) will be able to determine
the phase of locomotion portion and portion of locomotion of the
subject.
[0217] FIGS. 9a to 9d and 10a to 10d show examples of graphs of
localized plantar pressures, as well as their first, second and
third differentials, at the calcaneus and MP regions respectively,
for a linear walking path of 109,5 steps/minute.
[0218] FIGS. 11a to 11d show graphically the state boundary
conditions for a typical localized plantar pressure signal, and its
first three differentials, at the calcaneous region, while FIGS.
12a to 12c do so for the localized plantar pressure signal, and its
first two differentials, at the MP region. This shows the
relationships between the various data and derivative signals, and
the states.
[0219] In use, for the detection of the state of the four localized
plantar pressures, denoted f.sub.rx where x=[1, 4], the PRM (42)
uses a set of first state machines to select, at each increment in
time, the current state of each sensor. For this purpose, the
algorithm uses a set of events whose values define the conditions
to pass from one state to another for each of the localized plantar
pressures. Table 2 lists the events:
TABLE-US-00002 TABLE 2 List of events used to evaluate the state
boundary condition of a localized plantar pressure Event Acronym
Description Non-Zero of f.sub.rx FR_BIN.sub.x Detection of a
positive f.sub.rx First Differentiation FRfst_BIN.sub.x Detection
of positive first of f.sub.rx differentiation of f.sub.rx Second
Differentiation FRsec_BIN.sub.x Detection of positive second of
f.sub.rx differentiation of f.sub.rx Third Differentiation
FRtrd_BIN.sub.x Detection of positive third of f.sub.rx
differentiation of f.sub.rx Static f.sub.rx STA_BIN.sub.x Detection
of a static evolution of all f.sub.rx
[0220] The conditions placed on the values of each of the depicted
events of Table 2 define when the state machines pass from one
state to another for each of the localized plantar pressures. Table
3 lists the thresholds used to assess if the aforementioned
conditions are met, in which sum.sub.y depicts the five
complementary signals, for y=[a, e] as described in Table 4, while
Table 5 shows the mathematical form of the events used to evaluate
the state boundary condition of the localized plantar
pressures.
TABLE-US-00003 TABLE 3 List of thresholds used to evaluate the
state boundary condition of a localized plantar pressure Threshold
Acronym Description Positive value ZV_FR.sub.x Threshold to
consider f.sub.rx to be positive of f.sub.rx Positive value
ZV_FRfst.sub.x Threshold to consider the first of
.differential.f.sub.rx/.differential.t differentiation of f.sub.rx
to be positive. Positive value ZV_FRsec.sub.x Threshold to consider
the second of .differential..sup.2f.sub.rx/.differential.t.sup.2
differentiation of f.sub.rx to be positive. Positive value
ZV_FRtrd.sub.x Threshold to consider the third of
.differential..sup.3f.sub.rx/.differential.t.sup.3 differentiation
of f.sub.rx to be positive. Position value ZV_SUMfst Threshold to
consider the absolute value of
.differential.sum.sub.y/.differential.t of the first
differentiation of sum.sub.y to be positive. Positive value
ZV_SUMsec Threshold to consider the absolute value of
.differential..sup.2sum.sub.y/.differential.t.sup.2 of the second
differentiation of sum.sub.y to be positive
TABLE-US-00004 TABLE 4 List of complementary signals built from the
four localized plantar pressure f.sub.r1, f.sub.r2, f.sub.r3,
f.sub.r4, Mathematical Signal Acronym Description value Left foot
sum.sub.a Localized plantar pressure (f.sub.r1 + f.sub.r2)/2 signal
of left foot Right foot sum.sub.b Localized plantar pressure
(f.sub.r3 + f.sub.r4)/2 signal of right foot Both sum.sub.c
Localized plantar pressure (f.sub.r1 + f.sub.r3)/2 calcaneus signal
of both calcaneus Both MP sum.sub.d Localized plantar pressure
(f.sub.r2 + f.sub.r4)/2 signal of both MP Both feet sum.sub.e
Localized plantar pressure (f.sub.r1 + f.sub.r2 + signal of both
feet f.sub.r3 + f.sub.r4)/4
TABLE-US-00005 TABLE 5 Mathematical formulation of events Acronym
Mathematical form FR_BIN.sub.x { 0 if f rx ( k ) < ZV_FR x 1
otherwise } ##EQU00003## FRfst_BIN.sub.x { 0 if df rx ( k ) d ( k )
< ZV_FRfst x 1 otherwise } ##EQU00004## FRsec_BIN.sub.x { 0 if d
2 f rx ( k ) d 2 ( k ) < ZV_FRsec x 1 otherwise } ##EQU00005##
FRtrd_BIN.sub.x { 0 if d 3 f rx ( k ) d 3 ( k ) < ZV_FRtrd x 1
otherwise } ##EQU00006## STA_BIN { 0 if ( ( dsum y ( k ) d ( k )
> ZV_SUMfst ) ( d 2 sum y ( k ) d 2 ( k ) > ZV_SUMsec ) )
.A-inverted. y 1 otherwise } ##EQU00007##
[0221] FIGS. 13 and 14 show, respectively, the diagrams of the
state machines used for the detection of the state of the localized
plantar pressure at the calcaneous and the MP regions, while Tables
6 and 7 summarize the state boundary conditions between the states
of each localized plantar pressure.
TABLE-US-00006 TABLE 6 List of state boundary conditions defining
the states of the main artificial proprioceptors at the calcaneus
region CURRENT NEXT STATE STATE BOUNDARY CONDITIONS STATE Any state
!FR_BIN.sub.x XHZVS Any state FR_BIN.sub.x && STA_BIN.sub.x
XHSTA Any state FR_BIN.sub.x && !STA_BIN.sub.x &&
XHLSB FRfst_BIN.sub.x && FRsec_BIN.sub.x &&
FRtrd_BIN.sub.x Any state FR_BIN.sub.x && !STA_BIN.sub.x
&& XHLSM FRfst_BIN.sub.x && FRsec_BIN.sub.x
&& !FRtrd_BIN.sub.x Any state FR_BIN.sub.x &&
!STA_BIN.sub.x && XHLST FRfst_BIN.sub.x && !FRsec_
BIN.sub.x Any state FR_BIN.sub.x && !STA_BIN.sub.x
&& XHUST !FRfst_BIN.sub.x && !FRsec_ BIN.sub.x Any
state FR_BIN.sub.x && !STA_BIN.sub.x && XHUSB
!FRfst_BIN.sub.x && FRsec_ BIN.sub.x
TABLE-US-00007 TABLE 7 List of state boundary conditions defining
the states of the main artificial proprioceptors at
metatarsophalangeal region CURRENT NEXT STATE STATE BOUNDARY
CONDITIONS STATE Any state !FR_BIN.sub.x XMZVS Any state
FR_BIN.sub.x && STA_BIN.sub.x XMSTA Any state FR_BIN.sub.x
&& !STA_BIN.sub.x && XMLSB FRfst_BIN.sub.x
&& FRsec_ BIN.sub.x Any state FR_BIN.sub.x &&
!STA_BIN.sub.x && XMLST FRfst_BIN.sub.x && !FRsec_
BIN.sub.x Any state FR_BIN.sub.x && !STA_BIN.sub.x
&& XMUST !FRfst_BIN.sub.x && !FRsec_ BIN.sub.x Any
state FR_BIN.sub.x && !STA_BIN.sub.x && XMUSB
!FRfst_BIN.sub.x && FRsec_ BIN.sub.x
[0222] FIG. 15 shows a flow chart that depicts the PRM algorithm,
which comprises two main parts, namely the pre-processing of the
main artificial proprioceptors signals and the locomotion
breakdown, illustrated by blocks 100 and 102 respectively. The
sequence of steps performed the pre-processing of the main
artificial proprioceptors signals, represented by block 100, is
indicated by the sequence of blocks 104 to 118. At block 104, the
four localized plantar pressures signals are received from the
interface and normalized at block 106 using subject specific
calibration values. The four normalized local plantar pressures
then go through the pre-processing steps represented by blocks 104
to 118. At block 112, the four normalized local plantar pressures
are filtered to reduce their spectral composition. A counter is
then initialized at block 108, which in turn starts a loop
comprising blocks 110 to 116. The first step of the loop, at block
110, consist in the differentiation of the signals. The signals
resulting from the differentiation step are filtered at block 112,
in order to limit the noise induced during the differential
computation, and go through binary formatting at block 114. At
block 116, the algorithm checks if the counter has reached 3
iterations. If so, the algorithm, having computed all first three
derivatives of the four normalized local plantar pressures signals,
exits the loop to block 102. If not, the algorithm proceeds to
block 110 where the counter is increased at block 118 and the loop
is repeated, in order to computed the next derivative, by
proceeding to block 110. When the loop exists to block 102, the
algorithm enters into the locomotion breakdown part of the
algorithm. The sequence of steps performed by the locomotion
breakdown, represented by block 102, is indicated by the sequence
of blocks 120 to 124. From the four normalized local plantar
pressures and their first three derivatives, block 120 determines
the states of each sensor while blocks 122 and 124 determine the
phase and the portion of locomotion, respectively.
[0223] The normalization step, represented by block 106, consists
in levelling the magnitude of the raw data signals according to the
anthropomorphic characteristics of the subject such as, in the
preferred embodiment, the subject's weight. The raw data signals of
the four localized plantar pressures are divided by the total
magnitude provided by the four sensors during calibration and then
provided as the normalized local plantar pressures to block
110.
[0224] At block 112 the normalized raw signals of the four
localized plantar pressures and their first three differentials are
numerically filtered to reduce their spectral composition, as well
as to limit the noise induced during the derivative computation.
The preferred embodiment of the PRM (42) uses a 2.sup.nd order
numerical filter in which the cut-off frequency, the damping factor
and the forward shifting have been set, experimentally, to optimize
the calculation according to the locomotion portion and the type of
signal. The PRM (42) may use other types of numerical filters as
well, for example a "Butterworth" filter, as long as the filter's
dynamic is similar to the one provided by the 2.sup.nd order filter
shown thereafter for each locomotion portion. Equation 4 shows the
mathematical relationships of the 2.sup.nd order numerical filter
which is implemented within the PRM (42). Table 8 provides examples
of filtering parameters for three different portions of
locomotion.
Laplace Form
[0225] H ( s ) = .omega. n 2 s 2 + 2 .zeta. .omega. n s + .omega. n
2 Equation 3 ##EQU00008## [0226] where .omega..sub.n in the nth
damping natural frequency,
[0226] .omega. n = .omega. r 1 - 2 .zeta. 2 , .zeta. < 1
##EQU00009## [0227] .omega..sub.r is called the resonance frequency
for .zeta.<1 [0228] .zeta. is the damping factor
Recursive Form
[0229] H ( z ) = b 2 z - 1 + b 3 z - 2 a 1 + a 2 z - 1 + a 3 z - 2
a 1 y ( k ) = b 2 x ( k - 1 ) + b 3 x ( k - 2 ) - a 2 y ( k - 1 ) -
a 3 y ( k - 2 ) where a 1 = 1 a 2 = - 2 .alpha. .beta. a 3 =
.alpha. 2 b 1 = 0 b 2 = 1 - .alpha. [ .beta. + .zeta. .omega. n
.differential. .omega. r ] b 3 = .alpha. 2 + .alpha. [ .zeta.
.omega. n .differential. .omega. r - .beta. ] .alpha. = - .zeta.
.omega. n T e .beta. = cos ( .omega. r T e ) .differential. = sin (
.omega. r T e ) T e = sampling rate Equation 4 ##EQU00010##
TABLE-US-00008 TABLE 8 Examples of parameters of 2.sup.nd order
filters used by the PRM Filtering Parameters Type of Cut-Off
Damping Forward Portion of locomotion signal Frequency (F.sub.c)
Factor (z) Shifting Linear Walking - Raw 2 0.680 7 Beginning path
(BTW) Derivative 3 0.700 3 Linear Walking - Raw 2 0.680 7 Cyclical
path (CTW) Derivative 3 0.700 3 Linear Walking - Raw 2 0.680 7
Ending path (ETW) Derivative 3 0.700 3
[0230] At block 110, the derivatives are obtained by the standard
method consisting of numerically differentiating the current and
the previous samples of localized plantar pressures.
[0231] The derivatives obtained at block 110 then go through binary
formatting at block 114. The result of the binary formatting
operation will be a "1" if the sign of the derivative is positive,
"0" if it is negative. This step facilitates the identification of
the sign changes of the differentiated signals as binary
events.
[0232] At block 120, the PRM (42) determines the current state of
each sensor using state machines such as the ones shown in FIGS. 13
and 14.
[0233] In the PRM (42), the states of the localized plantar
pressures are preferably expressed as a 10-bit words in which each
bit corresponds to a specific possible state. Tables 9 to 12 list
the binary equivalents of each state of the localized plantar
pressures at the calcaneous and the MP regions of the left and the
right foot. Of course, words of different bit length may be used as
well to represent the state of each localized plantar pressure.
TABLE-US-00009 TABLE 9 Numerical labels of the states for the
localized plantar pressure at calcaneous area of the left foot
DECIMAL STATE BINARY LABEL LABEL LHSBS 0 0 0 0 0 0 0 0 0 0 1 0
LHLSB 0 0 0 0 0 0 0 0 0 1 0 1 LHLSM 0 0 0 0 0 0 0 0 1 0 0 2 LHLST 0
0 0 0 0 0 0 1 0 0 0 3 LHUST 0 0 0 0 0 0 1 0 0 0 0 4 LHUSM 0 0 0 0 0
1 0 0 0 0 0 5 LHUSB 0 0 0 0 1 0 0 0 0 0 0 6 LHZVS 0 0 0 1 0 0 0 0 0
0 0 7 LHSTA 0 0 1 0 0 0 0 0 0 0 0 8
TABLE-US-00010 TABLE 10 Numerical labels of the states for the
localized plantar pressure at metatarsophalangeal area of the left
foot DECIMAL STATE BINARY LABEL LABEL LMSBS 0 0 0 0 0 0 0 0 0 0 1 0
LMLSB 0 0 0 0 0 0 0 0 0 1 0 1 LMLSM 0 0 0 0 0 0 0 0 1 0 0 2 LMLST 0
0 0 0 0 0 0 1 0 0 0 3 LMUST 0 0 0 0 0 0 1 0 0 0 0 4 LMUSM 0 0 0 0 0
1 0 0 0 0 0 5 LMUSB 0 0 0 0 1 0 0 0 0 0 0 6 LMZVS 0 0 0 1 0 0 0 0 0
0 0 7 LHSTA 0 0 1 0 0 0 0 0 0 0 0 8
TABLE-US-00011 TABLE 11 Numerical labels of the states for the
localized plantar pressure at calcaneous area of the right foot
DECIMAL STATE BINARY LABEL LABEL RHSBS 0 0 0 0 0 0 0 0 0 0 1 0
RHLSB 0 0 0 0 0 0 0 0 0 1 0 1 RHLSM 0 0 0 0 0 0 0 0 1 0 0 2 RHLST 0
0 0 0 0 0 0 1 0 0 0 3 RHUST 0 0 0 0 0 0 1 0 0 0 0 4 RHUSM 0 0 0 0 0
1 0 0 0 0 0 5 RHUSB 0 0 0 0 1 0 0 0 0 0 0 6 RHZVS 0 0 0 1 0 0 0 0 0
0 0 7 RHSTA 0 0 1 0 0 0 0 0 0 0 0 8
TABLE-US-00012 TABLE 12 Numerical labels of the states for the
localized plantar pressure at metatarsophalangeal area of the right
foot DECIMAL STATE BINARY LABEL LABEL RMSBS 0 0 0 0 0 0 0 0 0 0 1 0
RMLSB 0 0 0 0 0 0 0 0 0 1 0 1 RMLSM 0 0 0 0 0 0 0 0 1 0 0 2 RMLST 0
0 0 0 0 0 0 1 0 0 0 3 RMUST 0 0 0 0 0 0 1 0 0 0 0 4 RMUSM 0 0 0 0 0
1 0 0 0 0 0 5 RMUSB 0 0 0 0 1 0 0 0 0 0 0 6 RMZVS 0 0 0 1 0 0 0 0 0
0 0 7 RHSTA 0 0 1 0 0 0 0 0 0 0 0 8
[0234] At block 122, the PRM (42) generates the phase, which is
preferably expressed as the direct binary combination of the states
of the four localized plantar pressures. Accordingly, the phase can
be represented by a 40-bit word wherein the lower part of the lower
half word, the higher part of the lower half word, the lower part
of the higher half word and the higher part of the higher half word
correspond, respectively, to the calcaneous area of the left foot,
the MP area of the left foot, the calcaneous area of the right foot
and the MP area of the right foot, as represented in Tables 9 to
12. Table 13 presents an example of the identification of a phase
from the states of the four localized plantar pressures.
TABLE-US-00013 TABLE 13 Identification of a phase from the states
of the main artificial proprioceptors State of Localized Plantar
Pressure Right Foot Left Foot MP area Calcaneous MP area Calcaneous
Corresponding Phase 0000000100 0000010000 0000000001 0000010000
00000001000000010000 00000000010000010000
[0235] At block 124, the PRM (42) selects the portion of locomotion
the subject is currently using the state machine shown in FIG. 6.
Each portion of locomotion is composed of a sequence of phases.
[0236] Accordingly, Table 14 presents the phases sequence mapping
for the Beginning Path of Linear Walking (BTW) locomotion portion
corresponding to FIG. 7. This table shows the label, the decimal
value and as well the phase boundary conditions of each phase.
TABLE-US-00014 TABLE 14 Example of phases sequence mapping for the
locomotion portion labeled "Beginning Path of Linear Walking" (BTW)
Phase Phase Boundary Conditions Label Value F.sub.r1 F.sub.r2
F.sub.r3 F.sub.r4 BTW_1 27516604800 8 8 8 8 BTW_2 3449396416 5 7 3
7 BTW_3 2281717888 1 7 4 7 BTW_4 4429217920 2 7 5 7 BTW_5
17213489280 4 5 6 7 BTW_6 1731119808 4 7 5 7 BTW_7 34493988992 5 7
5 7 BTW_8 34494087296 5 7 7 7 BTW_9 3436186816 5 1 5 7 BTW_10
34361966720 5 1 7 7 BTW_11 68723802240 6 2 7 7 BTW_12 68727996544 6
3 7 7 BTW_13 68727867520 6 3 1 7 BTW_14 137455732864 7 4 1 7 BTW_15
137455734912 7 4 2 7 BTW_16 137455739008 7 4 3 7 BTW_17 13772512128
7 5 2 7 BTW_18 13772516224 7 5 3 7 BTW_19 1377252416 7 5 4 7 BTW_20
137573187712 7 7 4 7 BTW_21 137573204096 7 7 5 7 BTW_22
137573187586 7 7 4 1 BTW_23 137573203970 7 7 5 1 BTW_24
137573236740 7 7 6 2 BTW_25 137573236744 7 7 6 3
[0237] Table 15 enumerates a sample of boundary conditions
associated with the locomotion portion of the sitting and typical
walking on flat ground movements, while Table 3 lists the
thresholds used to assess if the aforementioned conditions are
met.
TABLE-US-00015 TABLE 15 Example of a list of portion boundary
conditions defining specific locomotion portions such as sitting
movements (STA- SUP-SIT-SDW-STA locomotion portion) and typical
walking on flat ground (STA-BTW-CTW-ETW-STA locomotion portion)
Current Next Portion Set of Events Portion STA SWING.sub.leg BTW
!STATIC_GR.sub.leg .parallel. !STATIC_GR.sub.prost
FR_LOW.sub.prost_heel FR_BIN.sub.leg_heel BTW_SWING
FR_HIGH.sub.leg_heel SDW FR_HIGH.sub.prost_heel PKA_SDW BTW
STATIC_GR.sub.leg ETW STATIC_GR.sub.prost SUM_BIN.sub.prost CTW
SWING.sub.prost CTW STATIC_GR.sub.leg STA STATIC_GR.sub.prost
FR_BIN.sub.prost_heel ETW FR_BIN.sub.leg_heel PKA_ETW
STATIC_GR.sub.leg .parallel. STATIC_GR.sub.prost ETW PKA_STA STA
SDW PKA_SIT SIT PKA_STA STA SIT GR_POS.sub.leg SUP MIN_SIT
FR_HIGH.sub.leg_mp FR_HIGH.sub.prost_mp PKA_STA STA SUP
!SUM_BIN.sub.prost SIT !SUM_BIN.sub.leg PKA_STA STA !PKA_SUP_RAMP
SIT
TABLE-US-00016 TABLE 16 Example of a list of events used to
evaluate the portion boundary conditions defining specific
locomotion portions such as sitting movements (STA-SUP-SIT-SDW-STA
locomotion portion) and typical waking on flat ground
(STA-BTW-CTW-ETW-STA locomotion portion) Event Acromyn Description
Swing occurence SWING.sub.y Detection of a swing prior to a foot
strike Non-Zero of f.sub.rx FR_BIN.sub.x Detection of a positive
f.sub.rx Low f.sub.rx FR_LOW.sub.x Detection of f.sub.rx level
between the zero envelope and the STA envelope High f.sub.rx
FR_HIGH.sub.x Detection of f.sub.rx level above the STA envelope
Static g.sub.ry STATIC_GR.sub.y Detection of g.sub.ry level below
the zero angular speed envelope and the zero acceleration envelope
Non-Zero of sum.sub.y SUM_BIN.sub.y Detection of a positive
sum.sub.y BTW swing BTW_SWING Detection of typical walking
g.sub.r_leg during leg occurrence swing Positive g.sub.ry
GR_POS.sub.y Detection of a positive g.sub.ry Minimum sitting
MIN_SIT Detection of a minimum time in portion SIT Sit down knee
angle PKA_SDW Detection of knee angle higher than the STA envelope
End walking knee PKA_ETW Detection of knee angle lower than the STA
angle envelope Stance knee angle PKA_STA Detection of knee angle
lower than the STA envelope Sit down knee angle PKA_SIT Detection
of knee angle higher than the SIT envelope Standing up knee
PKA_SUP_RAMP Detection of standing up knee angle evolution
angle
[0238] where X stands for leg_heel, leg_mp, prosthetic_heel or
prosthetic_mp [0239] Y stands for leg or prosthesis
[0240] The normalization step of block 106 uses specific
calibration values. These values are computed the first time a
subject uses the actuated prosthesis (12) or at any other time as
may be required. Two calibration values are preferably used: the
zero calibration value and the subject's weight calibration value.
The zero calibration value consists in the measurement of the four
localized plantar pressures when no pressure is applied to the
sensors, while the subject's weight calibration value is the
subject's weight relative to the magnitude of the total response of
the sensors.
[0241] The algorithm to obtain the zero calibration value of the
sensors is depicted by the flow chart shown in FIG. 16. The
sequence of steps composing the algorithm is indicated by the
sequence of blocks 200 to 222. In block 200, the algorithm starts
with the four localized plantar pressures. At block 202, the
subject sits on a surface high enough such that his feet hang
freely in the air. Then, at block 204, the subject lightly swings
his feet back and forth, which initialises a timer at block 206,
which in turn starts a loop comprising blocks 208, 210 and 212. At
block 208, the algorithm checks if the timer has reached 10
seconds, if so, then the algorithm exits the loop to block 220, if
not, the algorithm proceeds to block 210 and records the zero value
of the four sensors. Then, at block 212, the timer is increased and
the loop is repeated by proceeding to block 208. At block 220, the
average of each localized plantar pressures is computed and finally
provided as the zero calibration value at block 222.
[0242] In a similar fashion, the algorithm to obtain the subject's
weight calibration value is depicted by the flow chart shown in
FIG. 17. The sequence of steps composing the algorithm is indicated
by the sequence of blocks 300 to 322. In block 300, the algorithm
starts with the four localized plantar pressure. At block 302, the
subject stands up in a comfortable position, feet at shoulder width
distance, while maintaining the body in the stance position. Then,
at block 304, the subject slowly swings back and forth and then
left to right, which initialises a timer at block 306, which in
turn starts a loop comprising blocks 308, 310 and 312. At block
308, the algorithm checks if the timer has reached 10 seconds, if
so, then the algorithm exists the loop to block 320, if not, the
algorithm proceeds to block 310 and records the subject's weight
relative to the magnitude of the total response of the sensors.
Then, at block 312, the timer is increased and the loop is repeated
by proceeding to block 308. At block 320, the average of each
localized plantar pressures is computed and finally provided as the
weight calibration value at block 322.
[0243] FIG. 18 shows a flow chart that depicts the TG algorithm
used to establish a relationship, in real-time, between the output
of the PRM (42) and localized plantar pressures and the knee joint
trajectory. The sequence of steps composing the algorithm is
indicated by the sequence of blocks 400 to 408. At block 400, the
algorithm receives the normalized localized plantar pressures, the
phase of locomotion portion and the portion of the locomotion from
the PRM (42). Then, at block 402, the walking speed of the subject,
in steps per minute, is obtained from computing the number of
frames between two heel strikes, while taking into account the
sampling frequency, and is binary formatted. More specifically, the
subject's speed estimate {circumflex over (x)}.sub.v [k]
(steps/minute) is obtained from computing the number of frames
between two heel strikes s.sub.heel [k] (frames/step):
x ^ v = 60 f s s heel [ k ] - s heel [ k - 1 ] Equation 5
##EQU00011##
where f.sub.s is the frame sampling frequency (frames/second). A
heel strike event occurs when:
THRESHOLDHEELLOADING<f.sub.ri.sub.f[k]-f.sub.ri.sub.f[k-1],
i.sub.f=1, 3 Equation 6
[0244] At block 404, the algorithm uses the normalized localized
plantar pressures, the phase of locomotion portion, the portion of
the locomotion and the subject's speed in binary format to identify
a set of linear normalized static characteristics linking the knee
joint kinetic/kinematic parameters with the subject's locomotion in
a lookup table. At block 406 the TG (44) comprises two
transformation functions which compute the kinetic/kinematic
parameters at time k, which are the angular displacement
.theta..sub.kn(k) and the moment of force (torque) m.sub.kn(k),
using the localized plantar pressures and their corresponding
mathematical relationships (time-dependant equations and static
characteristics) identified at block 404. The values of the
kinetic/kinematic variables are then provided to the REG (48) at
block 408.
[0245] The transformation functions used by the TG (44) at block
406 may generally be represented by a system of equations such
as:
.theta..sub.g,h(k)=.OMEGA..sub.1(.THETA..sub.1(k),.chi.(k),
v(k))+.OMEGA..sub.2(.THETA..sub.2(k),.chi.(k),v(k))+ . . .
+.OMEGA..sub.q-1(.THETA..sub.q-1(k),.chi.(k),v(k))+.OMEGA..sub.q(.THETA..-
sub.q(k),.chi.(k),v(k)) Equation 7
j.sub.g,h(k)=M.sub.1(.THETA..sub.1(k),.chi.(k),
v(k))+M.sub.2(.THETA..sub.2(k),.chi.(k),v(k))+ . . .
+M.sub.q-1(.THETA..sub.q-1(k),.chi.(k),v(k))+M.sub.q(.THETA..sub.q(k),.ch-
i.(k),v(k)) Equation 8
where g=[sagittal (sg), frontal (fr), transversal (tr)] is the
plane of the motion [0246] h=[hip (hp), knee (kn), ankle (an),
metatarsophalangeal (mp)] is the joint [0247] q is the number of
the main artificial proprioceptors' sensors [0248] .THETA..sub.q is
the phenomenological entity related to the locomotion and provided
by the main artificial proprioceptors' sensors [0249] .OMEGA..sub.q
is the transformation function between the phenomenological entity
related to the locomotion, the kinematic variables of the lower
extremities and the time [0250] M.sub.q is the transformation
function between the phenomenological entity related to the
locomotion, the kinetic variables of the lower extremities and the
time [0251] .THETA..sub.q is the phenomenological entity related to
the locomotion and provided by the main artificial proprioceptors'
sensors [0252] .chi.(k)=.OMEGA.(p.sub.h (k),p.sub.r (k),v(k)) is
the state of the whole system (amputee and the AAP) in which k is
the current increment [0253] p.sub.h (k) is the phase of the
respective locomotion portion [0254] p.sub.r (k) is the locomotion
portion [0255] v(k) is the walking speed [0256] k is the current
increment
[0257] In the case where the TG (44) uses polynomial relationships
of order n, Equation 7 and Equation 8 become:
.theta..sub.g,h(k)=a.sub.1,1(.chi.(k),v(k)).THETA..sub.1(k)+ . . .
+a.sub.1,n(.chi.(k)v(k)).THETA..sub.1(k).sup.n+a.sub.2,1(.chi.(k),v(k)).T-
HETA..sub.2(k)+ . . .
+a.sub.q-1,1(.chi.(k),v(k)).THETA..sub.q-1(k)+ . . .
+a.sub.q-1,n(.chi.(k),v(k)).THETA..sub.q-1(k).sup.n+ . . .
+a.sub.q,1(.chi.(k),v(k)).THETA..sub.q(k)+ . . .
+a.sub.q,n(.chi.(k),v(k)).THETA..sub.q(k).sup.n Equation 9
m,h(k)=b.sub.1,1(.chi.(k),v(k)).THETA..sub.1(k)+ . . .
+b.sub.1,n(.chi.(k),v(k)).THETA..sub.1(k).sup.n+b.sub.2,1(.chi.(k),v(k)).-
THETA..sub.2(k)+ . . .
+b.sub.2,n(.chi.(k),v(k)).THETA..sub.2(k).sup.n+ . . .
+b.sub.q-1,1(.chi.(k),v(k)).THETA..sub.q-1(k)+ . . .
+b.sub.q-1,n(.chi.(k),v(k)).THETA..sub.q-1(k).sup.n+ . . .
+b.sub.q,1(.chi.(k),v(k)).THETA..sub.q(k)+ . . .
+b.sub.q,n(.chi.(k),v(k)).THETA..sub.q(k).sup.n Equation 10 [0258]
where a.sub.i,j(.chi.(k)) and b.sub.i,j(.chi.(k)) i=1.fwdarw.q are
the coefficients for the state .chi.(k) of the whole system and the
walking speed v(k) and n is the order of the polynomial The
preferred embodiment uses four localized plantar pressures, thus
Equation 9 and Equation 10 become:
[0258] .THETA..sub.g,h(k)=a.sub.1,1(.chi.(k),v(k))f.sub.r1(k)+ . .
.
+a.sub.q,n(.chi.(k),v(k))f.sub.r1(k).sup.n+a.sub.2,1(.chi.(k),v(k))f.sub.-
r2(k)+ . . .
+a.sub.2,n(.chi.(k),v(k))f.sub.r2(k).sup.n+a.sub.3,1(.chi.(k),v(k))f.sub.-
r3(k)+ . . .
+a.sub.3,n(.chi.(k),v(k))f.sub.r3(k).sup.n+a.sub.4,1(.chi.(k),v(k))f.sub.-
r3(k)+ . . . +a.sub.4,n(.chi.(k),v(k))f.sub.r3(k).sup.n Equation
11
h.sub.g,h(k)=b.sub.1,1(.chi.(k),v(k))f.sub.r1(k)+ . . .
+b.sub.1,n(.chi.(k),v(k))f.sub.r1(k).sup.n+b.sub.2,1(.chi.(k),v(k))f.sub.-
r2(k)+ . . .
+b.sub.2,n(.chi.(k),v(k))f.sub.r2(k).sup.n+b.sub.3,1(.chi.(k),v(k))f.sub.-
r3(k)+ . . .
+b.sub.3,n(.chi.(k),v(k))f.sub.r3(k).sup.n+b.sub.4,1(.chi.(k),v(k))f.sub.-
r3(k)+ . . . +b.sub.4,n(.chi.(k),v(k))f.sub.r3(k).sup.n Equation 12
[0259] where a.sub.i,j(.chi.(k)) and b.sub.i,j(.chi.(k))
i=1.fwdarw.q are the coefficients for the state .chi.(k) of the
whole system and the walking speed v(k) and n is the order of the
polynomial
[0260] Since all the kinetic/kinematic parameters .theta..sub.kn(k)
and m.sub.kn(k) are computed from non complex mathematical
relationships, the computation of the trajectory is simple and fast
and can be calculated by a non-sophisticated electronic circuit
board.
[0261] The mathematical relationships (time-dependant equations and
static characteristics) used in these non complex mathematical
relationships are contained in a lookup table referenced at block
404. FIG. 19 shows a flow chart that depicts the algorithm used to
create the TG lookup table. The sequence of steps composing the
algorithm is indicated by the sequence of blocks 100 to 512. At
block 100, the algorithm measures the selected phenomelogical
parameters, which in the preferred embodiment are the localized
plantar pressures, and the kinetc/kinematic parameters
.theta..sub.kn (k) and m.sub.kn (k) of a subject. The measured
phenomelogical parameters are then normalized in function of the
subject's weight. At block 104, the static characteristics linking
the phenomelogical parameters to the kinetc/kinematic parameters
and the time-dependant equations linking to the time are identified
and are then normalized at block 106. Then at block 108, the
mathematical relationships (time-dependant equations and static
characteristics) are broken down according to the phenomelogical
parameters, the phases of locomotion portion, portions of
locomotion, the speed of the subject and in the case were Equation
11 and Equation 12 are linear functions, the binary formatted data
signals. For each set of mathematical relationships (time-dependant
equations and static characteristics) created by the breakdown, a
polynomial regression is applied, at block 510, to the mathematical
relationships (time-dependant equations and static characteristics)
contained in the set. Finally, at block 512, the results of the
polynomial regressions are stored in the lookup table and are
indexed according to the breakdown of block 108.
[0262] The method for building this TG lookup table depicted by the
flow chart of FIG. 19 may be applied to any equations belonging to
the following analytical/logical family of functions:
y g , h = a 0 + a 1 x 1 + a 2 x 1 2 + + b 0 + b 1 x 2 + b 2 x 2 2 +
+ b m x 2 m + .beta. 0 + .beta. 1 x .chi. + .beta. 2 x .chi. 2 + +
.beta. .eta. x .chi. .eta. Equation 13 y g , h = i = 0 n a i x 1 i
+ i = 0 m b i x 2 i + i = 0 .eta. .beta. i x .chi. i y g , h = i =
0 n 1 a 1 , i x 1 i + i = 0 n 2 a 2 , i x 2 i + i = 0 n .chi. a
.chi. , i x .chi. i y g , h = j = 1 .chi. i = 0 n j a j , i x j i
##EQU00012## [0263] where y.sub.g,h is the estimated kinematic
({circumflex over (.theta.)}.sub.g,h) or kinetic ({circumflex over
(m)}.sub.g,h) variables for the g lower extremities joint through
the h plane of motion [0264] g is the lower extremities joint among
the following set: hip, knee, ankle and metatarsophalangeal [0265]
h is the plan of motion among the following set: sagittal, frontal
and transversal [0266] x.sub.j is the j.sup.th locomotion related
phenomenon, for example the j.sup.th localized plantar pressure
[0267] a.sub.j,i is the i.sup.th coefficient associated the
j.sup.th locomotion related phenomenon denoted x.sub.j [0268]
n.sub.j is the order of the polynomial depicting the j.sup.th
locomotion related phenomenon denoted x.sub.j [0269] .chi. is the
number of locomotion related phenomena
[0270] If it is considered that the family of functions in Equation
13 are dependant on the state of the system they depict, thus
following system of equations is obtained:
y g , h = j = 1 .chi. i = 0 n j a j , i ( x ) x j i Equation 14
##EQU00013##
where x is the time dependant state vector of the system
[0271] In the preferred embodiment, x.sub.j may be substituted by
the localized plantar pressures denoted f.sub.ri.sub.r, where
i.sub.f=[1, .chi.]. In the case of time-dependant equations,
x.sub.j may be substituted by the time. Thus, in the case of
plantar pressures, Equation 14 becomes:
y g , h = i f = 1 .chi. i = 0 n i f a i f , i ( x ) f ri f i
Equation 15 ##EQU00014##
where x is the time dependant state vector of the system
[0272] Previously, y.sub.g,h has been defined as the estimated
kinematic ({circumflex over (.theta.)}.sub.g,h) or kinetic)
({circumflex over (m)}.sub.g,h) variable for the g lower
extremities joints through the h plan of motion. Thus, Equation 15
may be written as:
.theta. ^ g , h = i f = 1 .chi. i = 0 n i f a i f , i ( x ) f ri f
i Equation 16 or m ^ g , h = i f = 1 .chi. i = 0 n i f a i f , i (
x ) f ri f i Equation 17 ##EQU00015##
[0273] The goal is the identification of the Equation 16 and
Equation 17 functions from a set of n.sub.s samples, obtained from
experimentation. A sample contains data related to the locomotion
related phenomenon along with the corresponding kinematic
(.theta..sub.g,h) or kinetic (m.sub.g,h) variables.
[0274] The following array of data is obtained from
experimentation:
TABLE-US-00017 TABLE 16 Data obtained from experimentation t x
x.sub.1 x.sub.2 . . . x.sub.j . . . x.sub.x .theta..sub.g, h
m.sub.g, h 1 2 . . . . . . i.sub.s . . . x.sub.j, i.sub.s . . . . .
. . . . n.sub.s
[0275] where j, .chi. is the index and the number of locomotion
related phenomena [0276] i.sub.s, n.sub.s is the index and the
number of frames [0277] t is the time [s] [0278] x is the time
dependant state vector of the system [0279] x.sub.j is the selected
locomotion related phenomenon [0280] .theta..sub.g,h is the
kinematic variables for the g lower extremities joint through the h
plan of motion [0281] m.sub.g,h is the kinetic variable for the g
lower extremities joint through the h plan of motion
[0282] The logical functions a.sub.j,i(x) are then presented in the
form of a look-up table, as shown in the following example:
TABLE-US-00018 TABLE 17 Look-up table example a.sub.j, i (x) t x
a.sub.1, 0 a.sub.1, 1 . . . a.sub.2, 0 a.sub.2, 1 . . . a.sub.x, 0
a.sub.x, 1 . . . a.sub.x, n.sub.x 1 x.sub.1 34.5 23.1 . . . 12.3
92.5 . . . 83.6 52.4 . . . 72.5 2 x.sub.2 23.6 87.5 . . . 64.4 84.9
. . . 93.4 38.6 . . . 28.5 . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . i.sub.c x.sub.ic 76.9 82.5 . . . 93.3
a.sub.j, i, i.sub.c . . . 37.5 82.3 . . . 84.4 . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . n.sub.c x.sub.nc 61.4
90.6 . . . 72.3 26.4 . . . 83.5 26.4 . . . 28.6
[0283] where i.sub.c, n.sub.c index and dimension of the look-up
table (n.sub.c is the number of considered quantized states) [0284]
x is the time dependant state vector of the system
[0285] Table 17 establishes the relationship between the time
dependent state vector of the system, the locomotion related
phenomenon and the kinematic and the kinetic variables of the lower
extremities joints, which are the following static
characteristics:
{circumflex over (.theta.)}.sub.g,h=f.sup..theta.(x, x) Equation
18
{circumflex over (m)}.sub.g,h=f.sup.m(x, x) Equation 19
[0286] The methodology used to identify the parameters a.sub.j,i(x)
is based on the application of a curve-fitting algorithm to a set
of data provided from experimentation on human subjects. This
experimentation is performed in a laboratory environment under
controlled conditions, yielding a set of data in the form of an
array, as shown in Table 16.
[0287] The curve-fitting algorithm is used to obtain the parameters
a.sub.j,i(x) for every given time dependant state vector x. This
data is used to construct the look-up table, as shown in Table
17.
[0288] An example of configuration for the method previously
described is presented below:
[0289] the particularities of this configuration are: [0290] a. the
locomotion related phenomenon is composed of a set of four
localized plantar pressures supplied by the main artificial
proprioceptors; [0291] b. the time dependant state vector is
composed of: [0292] i. the walking speed of the subject; [0293] ii.
the phase of locomotion portion and the portion of locomotion;
[0294] iii. and if Equation 16 and Equation 17 are linear
functions: [0295] iv. the binary formatted magnitude of the four
localized plantar pressures;
[0296] the family of functions depicting the static characteristics
{circumflex over (.theta.)}.sub.g,h=f.sup..theta.(x, x) and
{circumflex over (m)}.sub.g,h=f.sup.m(x, x), as described in
Equation 16 and Equation 17;
[0297] or [0298] a. the family of functions depicting the
time-dependant equations {circumflex over
(.theta.)}.sub.g,h=f.sup..theta.(x, t) and {circumflex over
(m)}.sub.g,h=f.sup.m(x,t), as described in Equation 16 and Equation
17 when f.sub.ri.sub.f is substituted by time t.
[0299] the selected lower extremities joints is the knee joint,
which is the joint between the thigh (th) and the shank (sh);
[0300] the selected plan of motion is the sagittal plan;
[0301] In the case where Equation 16 and Equation 17 are linear
functions, the time dependant state vector further comprises the
binary formatted magnitude of the four localized plantar pressures
as added parameters to further segment the curve representing the
kinematic (.theta..sub.g,h) or kinetic (m.sub.g,h) variables. This
is due to the fact that, as shown by FIG. 20, that for a given
portion of locomotion, phase of locomotion portion and subject's
speed, the curve representing the kinematic (.theta..sub.g,h) or
kinetic (m.sub.g,h) variables cannot efficiently be approximated by
a linear function. To that end, the binary formatted plantar
pressures are used to further subdivide the phase of locomotion
portion in a number of intervals on which the curve representing
the kinematic (.theta..sub.g,h) or kinetic (m.sub.g,h) variables
may be approximated by linear functions. FIG. 21 is a close-up view
of FIG. 20 where it is shown that the curve representing the
kinematic (.theta..sub.g,h) or kinetic (m.sub.g,h) variables appear
relatively linear on each of the added subdivisions. Thus, the use
of Equation 16 and Equation 17 which are linear functions entails
that the time dependant stated vector will further comprise the
binary formatted plantar pressures.
[0302] It should be noted that in the preferred embodiment, the
lookup table contains mathematical relationships that have been
normalized in amplitude. The TG (44) uses the relative value of the
localized plantar pressures instead of the magnitude of the signal.
This means that the localized plantar pressures are set into a [0,
1] scale for a specific state of the whole system .chi.(k). This
ensures that the mathematical relationships (time-dependant
equations and static characteristics) are independent of the weight
of the subject. It is worth to note that, because the TG's
architecture use the walking speed as a component of the state of
the whole system, the static characteristics lookup table is valid
for any walking speed comprised within the operational conditions,
which are, in the preferred embodiment, between 84 and 126
steps/min, though the lookup table may be computed for other
intervals.
[0303] The Regulator (48) uses a control law with a similar
structure to control algorithms currently employed in numerous
commercial or experimental applications. Various control laws may
be implemented in the Regulator (48), examples of which are
provided below.
[0304] First, the Regulator (48) may use a simple PID control law,
which is written as:
.mu.(t)=k.sub.d {dot over (x)}(t)+k.sub.i.intg. xdt Equation 20
[0305] where k.sub.d is the gain associated to the differential
component of the regulator [0306] k.sub.p is the gain associated to
the proportional component of the regulator [0307] k.sub.i is the
gain associated to the integral component of the regulator [0308]
x.sub.i is the requested trajectory [0309] x.sub.o is the
trajectory performed by the system [0310] x is the error between
the requested (x.sub.i) and performed trajectory (x.sub.o) [0311]
.mu. is the set point intended to the system applied to the
proposed system, that is x=.theta. or x=m, we have:
[0311] .mu..sub.g,h.sup.x(t)=k.sub.d {dot over
(x)}.sub.g,h(t)+k.sub.p x.sub.g,h+k.sub.i.intg. x.sub.g,hdt
Equation 21 [0312] where g=[sagittal (sg), frontal (fr),
transversal (tr)] is the plan of the motion [0313] h=[hip (hp),
knee (kn), ankle (an), metatarsophalangeal (mp)] is the joint
[0314] x=.theta. or m where the transfer function between the error
x and the set-point is expressed as:
[0314] .mu. g , h .theta. ( t ) x _ g , h ( t ) = b 2 z 2 + b 1 z +
b 0 z ( z - 1 ) Equation 22 ##EQU00016## [0315] where
b.sub.2=k.sub.i+k.sub.p+k.sub.d [0316] b.sub.1=-(k.sub.p+k.sub.d)
[0317] b.sub.o=k.sub.d [0318] x=.theta. or m in which the
corresponding recurrent equation is:
[0318] .mu..sub.g,h.sup.x(k)=.mu..sub.g,h.sup.x(k-1)+b.sub.0
x.sub.g,h(k-2)+b.sub.1 x.sub.g,h(k-1)+b.sub.2 x.sub.g,h(k) Equation
23 [0319] where k is the current increment [0320] x=.theta.or m
[0321] Secondly, the Regulator (48) may use an adaptive PID control
law. The transfer function of an adaptive PID is the same as that
of a conventional PID but the parameters b.sub.2, b.sub.1 and
b.sub.0 are function of the state of the whole system .chi.(k).
From Equation 23, the recurrence equation of the adaptive PID
is:
.mu..sub.g,h.sup.x(k)=.mu..sub.g,h.sup.x(k-1)+b.sub.0(.chi.(k))
x.sub.g,h(k-2)+b.sub.1(.chi.(k)) x.sub.g,h(k-1)+b.sub.2(.chi.(k))
x.sub.g,h(k) Equation 24 [0322] where k is the current increment
[0323] x=.theta. or m
[0324] Thirdly, the Regulator (48) may use a conventional PID with
measured moment, which may be written as:
f.sub.g,h.sup..mu.(k)=f.sub.g,h.sup.m(k)+ f.sub.g,h(k) Equation 25
[0325] where f.sub.g,h.sup.m(k) is the force measured at the joint
[0326] f.sub.g,h(k) is the force generated by the regulator [0327]
f.sub.g,h.sup..mu.(k) is the set point of the force intended to the
joint
[0328] Form Equation 22, the transfer function between the position
error x.sub.g,h and the force set-point f.sub.g,h (k) is expressed
as:
f _ g , h ( t ) x _ g , h ( t ) = K ( b 2 z 2 + b 1 z + b 0 z ( z -
1 ) ) Equation 26 ##EQU00017## [0329] where K is the gain yielded
by the device between the position and the force set point [0330]
x=.theta. or m
[0331] Thus, the recurrent equation of the final force set point
f.sub.g,h.sup..mu.(k) is given by the following relationship:
f.sub.g,h.sup..mu.(k)=f.sup.m(k)+ f.sub.g,h(k-1)+b.sub.0
x.sub.g,h(k-2)+b.sub.1 x.sub.g,h(k-1)+b.sub.2 x.sub.g,h(k) Equation
27 [0332] where k is the current increment [0333] x=.theta. or
m
* * * * *