@conference {523, title = {A Collaborative and Adaptive Feedback System for Physical Exercises}, booktitle = {2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Atlanta, GA, USA}, abstract = {

Maintaining motivation to meet physical exercise goals is a big challenge in virtual/home-based exercise guidance systems. Lack of motivation, long-maintained bad daily routines, and fear of injury are some of the reasons that cause this hesitation. This paper proposes a reinforcement learning-based virtual exercise assistant capable of providing encouragement and customized feedback on body movement form over time. Repeated arm curls were observed and tracked using single and dual-camera systems using the Posenet pose estimation library. To accumulate enough experience across individuals, the reinforcement learning model was collaboratively trained by subjects. The proposed system is tested on 36 subjects. Behavioral changes are apparent in 31 of the 36 subjects, with 31 subjects reducing movement errors over time and 15 subjects completely eliminating the errors. The system was analyzed for which types of feedback provided the highest expected value, and feedback directly related to the previous mistake provided the highest valued feedback ( p\<0.0133 ). The result showed that the Reinforcement Learning system provides meaningful feedback and positively impacts behavior progress.

}, keywords = {Distributed Machine Learning, Human-Computer Interaction, Pose Estimation, Reinforcement Learning}, isbn = {978-1-6654-1625-2}, doi = {10.1109/CIC52973.2021.00012}, url = {https://ieeexplore.ieee.org/abstract/document/9707166}, author = {Ranasinghe, Ishan and Yuan, Chengping and Dantu, Ram and Albert, Mark V.} } @conference {543, title = {Cyber-Physiotherapy: Rehabilitation to Training}, booktitle = {2021 IFIP/IEEE International Symposium on Integrated Network Management (IM)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Bordeaux, France}, abstract = {

Cutting-edge Human-Computer Interaction (HCI) technologies embedded with Machine Learning (ML) will cause a paradigm shift in various domains, including manufacturing and developing facilities and services for professional and personal use. ML implemented HCIs can help people overcome societal challenges brought about by the COVID-19 pandemic. We introduce a system for people to perform physical exercises at home. This system is intended to help a range of demographics, from non-critical physical therapy patients to experienced weightlifters. More specifically, we propose a method to assess the difficulty of an exercise for visual exercise tracking systems. Pose estimation tracks exercises and reinforcement learning provides autonomous feedback to the user (patient/athlete). This information is processed largely on the client side, allowing the application to run smoothly anywhere in the world.

}, keywords = {Fitts{\textquoteright}s law, Human-Computer Interaction, Index of Difficulty, Pose Estimation, Reinforcement Learning}, isbn = {978-3-903176-32-4}, url = {https://ieeexplore.ieee.org/abstract/document/9464036}, author = {Ranasinghe, Ishan and Dantu, Ram and Albert, Mark V. and Watts, Sam and Ocana, Ruben} }