Limb-O: Real-Time Comparison and Visualization of Lower Limb Motions

Abstract

Since the rise in popularity of video-sharing platforms such as Youtube, learning new skills from the comfort of one’s own home has become more accessible than ever. Though such independent learning methods are useful, they lack the real-time feedback component of being in the same room with an expert, which is why expensive private coaching sessions remain desirable. Accordingly,we propose Limb-O (orbs for limb movement visualization), a real-time quantitative virtual coach application for learning lower-limb motions through motion comparison. The proposed application turns the practice of things like sports motions into a game that highlights imperfections and allows for tracking of progress over time. A user validation study was run which confirmed that Limb-O outperforms traditional video learning methods both quantitatively and qualitatively,by providing objective feedback that keeps users engaged.

Publisher Link

https://doi.org/10.1145/3396339.3396360

Citation

Michael Gammon, Ritesh Udhani, Parth Patekar, Yumiko Sakamoto, Carlo Menon, and Pourang Irani. 2020. Limb-O: Real-Time Comparison and Visualization of Lower Limb Motions. In 11th Augmented Human International Conference (AH ’20), May 27–29, 2020, Winnipeg, MB, Canada. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3396339.3396360