U.S. patent number D953,754 [Application Number D/732,573] was granted by the patent office on 2022-06-07 for personal training mirror. This patent grant is currently assigned to Elo Labs, Inc.. The grantee listed for this patent is Elo Labs, Inc.. Invention is credited to Sami Asikainen, Riikka Tarkkanen.
United States Patent | D953,754 |
Asikainen , et al. | June 7, 2022 |
Inventors: | Asikainen; Sami (North Vancouver, CA), Tarkkanen; Riikka (North Vancouver, CA) | ||||||||||
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Applicant: |
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Assignee: | Elo Labs, Inc. (New York,
NY) |
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Appl. No.: | D/732,573 | ||||||||||
Filed: | April 24, 2020 |
Current U.S. Class: | D6/300; D14/126 |
Current International Class: | 0607 |
Field of Search: | ;D6/300,301,302,303,304,307,308,309,310,311,312,313,314 ;D28/64.1-64.7 ;D14/126,336,341,371,372,374,381,382,448,450 ;D19/113,114 ;D20/10,27,42 |
D472223 | March 2003 | Wilmotte |
D547071 | July 2007 | Mischel, Jr. |
7637847 | December 2009 | Hickman |
D661123 | June 2012 | Curbbun |
D691208 | October 2013 | Gorelick |
D759617 | June 2016 | Soares |
D789313 | June 2017 | Jacobi |
D801703 | November 2017 | Robertson |
D807648 | January 2018 | Gilad |
D869412 | December 2019 | Spencer |
D880593 | April 2020 | Lee |
D890710 | July 2020 | Bakshi |
D925484 | July 2021 | Easton |
D927595 | August 2021 | Ogden |
2002/0039952 | April 2002 | Clem |
2007/0219059 | September 2007 | Schwartz et al. |
2007/0225118 | September 2007 | Giomo |
2010/0022351 | January 2010 | Lanfermann et al. |
2015/0100141 | April 2015 | Hughes |
008624647-0001 | Jul 2021 | EM | |||
00126348 | Jul 2021 | RU | |||
Carbon Trainer [online], [site visited Jan. 26, 2022]. Available from internet, URL: <https://www.carbontrainer.com/order> (Year: 2022). cited by examiner . Carbon Trainer is the New Fitness Mirror Designed for Home Strength Training [online], [site visited Jan. 26, 2022]. Available from internet, URL: <https://manofmany.com/lifestyle/fitness/carbon-trainer-is-the-ne- w-fitness-mirror-designed-for-home-strength-training> (Year: 2020). cited by examiner . Mirror Pro [online], [site visited Jan. 26, 2022]. Available from internet, URL: <https://www.mirror.co/shop/mirror-pro-cw?gclid=Cj0KCQiA_8OPBhDtARIsAK- Qu0gbPF_10mpXrPaHO2jCsfyA3fBWUHIJqwJe0AauAMn4sJiNHZkJImi8aAgltEALw_wcB> (Year: 2022). cited by examiner . International Search Report and Written Opinion for PCT/US2020/041860, filed Jul. 13, 2020, dated Sep. 28, 2020, 21 pgs. cited by applicant . Van Hooff, Nino, "Performance Assessment and Feedback of Fitness Exercises Using Smartphone Sensors", Master Thesis, Jul. 2013, 55 pgs. cited by applicant . Runia, Tom et al., "Real-World Repetition Estimation by Div, Grad and Curl", http://tomrunia.github.io/projects/repetition/, 2018, 5 pgs. cited by applicant . Guler, Riza et al., "DensePose: Dense Human Pose Estimation in the Wild", http://densepose.org/, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. cited by applicant . Dong, Junting, et al., "Fast and Robust Multi-Person 3D Pose Estimation From Multiple Views", https://arxiv.org/pdf/1901.04111.pdf, Jan. 14, 2019, 10 pgs. cited by applicant . "Weight Lifting", https://vcl.iti.gr/weight-lifting/, Research, Motion Capturing and Analysis, Oct. 24, 2017, 3 pgs. cited by applicant . Omran, Mohamed, et al., "Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation", https://paperswithcode.com/paper/neural-body-fitting-unifying-deep-learn2- , Aug. 17, 2018, 13 pgs. cited by applicant . Cho, Youngjun, et al., "Instant Automated Inference of Perceived Mental Stress Through Smartphone PPG and Thermal Imaging", https://arxiv.org/ftp/arxiv/papers/1901/1901.00449.pdf, Journal of Medical Internet Research/JMIR Mental Health--Special Issue on Computing and Mental Health, 2018, 24 pgs. cited by applicant . Zdziarski, Zbigniew, "Heart Rate Estimation Using Computer Vision--Zbigatron", https://zbigatron.com/heart-rate-estimation-using-computer-vision/, Dec. 12, 2018, 5 pgs. cited by applicant . "Video Magnification", http://people.csail.mit.edu/mrub/vidmag/, Jun. 2015, 4 pgs. cited by applicant . Pilz, Christian S., et al., "Local Group Invariance for Heart Rate Estimation From Face Videos in the Wild", http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w27/Pilz_- Local_Group_Invariance_CVPR_2018_paper.pdf, 2018, pp. 1367-1375. cited by applicant . Wang, Chen, et al., "A Comparative Survey of Methods for Remote Heart Rate Detection From Frontal Face Videos", Frontiers in Bioengineering and Biotechnology, May 1, 2018, vol. 6, Article 33, 16 pgs. cited by applicant . Trivedi, Chintan, "Using Tensorflow Object Detection to Control First-Person Shooter Games", Towards Data Science, Nov. 25, 2018, 6 pgs. cited by applicant . Jovanov, Goran, "Realtime Face Recognition in the Browser", https://medium.com/@gjovanov/realtime-face-recognition-de1ee3076878, Jan. 10, 2019, 10 pgs. cited by applicant . Bhoi, Amlaan, "Spatio-Temporal Action Recognition: A Survey", https://arxiv.org/pdf/1901.09403.pdf, Jan. 27, 2019, 15 pgs. cited by applicant . Pham, Huy-Hieu, et al., "Learning to Recognize 3D Human Action From A New Skeleton-Based Representation Using Deep Convolutional Neural Networks", https://arxiv.org/pdf/1812.10550.pdf, IET Research Journals, Dec. 26, 2018, 11 pgs. cited by applicant . Fang, Hao-Shu, et al., "AlphaPose: Real-Time and Accurate Full-Body Multi-Person Pose Estimation & Tracking System", https://github.com/MVIG-SJTU/AlphaPose, Accessed Oct. 15, 2020, 8 pgs. cited by applicant . "Open Source IMU and AHRS Algorithms", https://x-io.co.uk/open-source-imu-and-ahrs-algorithms/, x-io Technologies, Posted Jul. 31, 2012, 2 pgs. cited by applicant . Brownlee, "Deep Learning Models for Human Activity Recognition," Machine Learning Mastery, Sep. 26, 2018, retrieved from https://machinelearningmastery.com/deep-learning-models-for-human-act . . . on Jun. 24, 2020, 28 pgs. cited by applicant . Runia et al., "Repitition Estimation," International Journal of Computer Vision, 2019, vol. 127, pp. 1361-1383. cited by applicant. |
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