Search papers, labs, and topics across Lattice.
School of Competitive Sports, Beijing Sports University {hao_yin, lijun_gu}@mail.ustc.edu.cn, paritosh.parmar@alumni.unlv.edu {2023110049, gtx}@bsu.edu.cn, {fuww, zhangyang, zhengty}@sibet.ac.cn Equal contributionCorresponding author Abstract With the increasing awareness of health and the growing desire for aesthetic physique, fitness has become a prevailing trend. However, the potential risks associated with fitness training, especially with weight-loaded fitness actions, cannot be overlooked. Action Quality Assessment (AQA), a technology that quantifies the quality of human action and provides feedback, holds the potential to assist fitness enthusiasts of varying skill levels in achieving better training outcomes. Nevertheless, current AQA methodologies and datasets are limited to single-view competitive sports scenarios and RGB modality and lack professional assessment and guidance of fitness actions. To address this gap, we propose the FLEX dataset, the first multi-modal, multi-action, large-scale dataset that incorporates surface electromyography (sEMG) signals into AQA. FLEX utilizes high-precision MoCap to collect 20 different weight-loaded actions performed by 38 subjects across 3 different skill levels for 10 repetitions each, containing 5 different views of the RGB video
1
0
3
0
Finally, a dataset that could help your AI spot your terrible deadlift form, combining video, pose, muscle activity, and expert feedback to train models for fitness action quality assessment.
