01909nas a2200205   4500008004100000020002200041245007200063210006900135260002700204520122500231653003301456653003101489653002001520653002701540100002201567700002001589700001501609700002101624856005801645       2021                        eng d  a978-1-6654-1625-200aA Collaborative and Adaptive Feedback System for Physical Exercises0 aCollaborative and Adaptive Feedback System for Physical Exercise  aAtlanta, GA, USAbIEEE3 a<p>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 ( <em>p</em>&lt;0.0133 ). The result showed that the Reinforcement Learning system provides meaningful feedback and positively impacts behavior progress.</p>
10aDistributed Machine Learning10aHuman-Computer Interaction10aPose Estimation10aReinforcement Learning1 aRanasinghe, Ishan1 aYuan, Chengping1 aDantu, Ram1 aAlbert, Mark, V.  uhttps://ieeexplore.ieee.org/abstract/document/9707166