Abstract:
In recent years, with growing health consciousness, at-home workout has become increasingly popular for its convenience and safety. Most people choose to follow video guidance during exercising. However, our preliminary study revealed that fitness-minded people face challenges when watching exercise videos on handheld devices or fixed monitors, such as limited movement comprehension due to static camera angles and insufficient feedback. To address these issues, we reviewed popular workout videos, identified user requirements, and came up with an augmented reality (AR) solution. Following a user-centered iterative design process, we proposed a design space of AR visual cues for workouts and implemented an AR-based application. Specifically, we captured users’ exercise performance with pose-tracking technology and provided feedback via AR visual cues. Two user experiments showed that incorporating AR visual cues could improve movement comprehension and enable users to adjust their movements based on real-time feedback. Finally, we presented several suggestions to inspire future design and apply AR visual cues to sports training.
Most people choose to follow video guidance during exercising.
Main reference:
Yihong Wu, Lingyun Yu, Jie Xu, Dazhen Deng, Jiachen Wang, Xiao Xie, Hui Zhang, and Yingcai Wu. 2023. AR-Enhanced Workouts: Exploring Visual Cues for At-Home Workout Videos in AR Environment. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST ’23). Association for Computing Machinery, New York, NY, USA, Article 121, 1–15. https://doi.org/10.1145/3586183.3606796
BibTex:
@inproceedings{10.1145/3586183.3606796,
author = {Wu, Yihong and Yu, Lingyun and Xu, Jie and Deng, Dazhen and Wang, Jiachen and Xie, Xiao and Zhang, Hui and Wu, Yingcai},
title = {AR-Enhanced Workouts: Exploring Visual Cues for At-Home Workout Videos in AR Environment},
year = {2023},
isbn = {9798400701320},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3586183.3606796},
doi = {10.1145/3586183.3606796},
articleno = {121},
numpages = {15},
keywords = {movement learning, argumented reality, SportsXR},
location = {, San Francisco, CA, USA, },
series = {UIST ’23}
}