PiaMuscle: Improving Piano Skill Acquisition by Cost-effectively Estimating and Visualizing Activities of Miniature Hand Muscles

要旨

Understanding neuromusculoskeletal mechanisms significantly impacts skill specialization and proficiency. While existing methods can infer large muscle activities during gross motor movements, the estimation of dexterous motor control involving miniature muscles remains underexplored. Targeting the coordinated hand muscles in advanced piano performance, we learn spatiotemporal discrete representations of electromyography (EMG) data and hand postures utilizing a multimodal dataset. Subsequently, we train a precise and cost-effective neural network model. Based on this model, PiaMuscle is introduced to investigate if visualizing muscle activities during piano training enhances piano performance. Quantitative and qualitative results of a user study with highly skilled professional pianists demonstrate that PiaMuscle provides reliable muscle activation data to support and optimize force control. Our research underscores the potential of a naturalistic workflow to estimate small muscles' activities from readily accessible human-centric information and more accurately when combined with tool-centric data, thereby enhancing skill acquisition.

著者
Ruofan Liu
Tokyo Institute of Technology, Tokyo, Japan
Yichen Peng
Tokyo Intitute of Technology, Tokyo, Japan
Takanori Oku
Shibaura Institute of Technology, Tokyo, Japan
Chen-Chieh Liao
Tokyo Institute of Technology, Tokyo, Japan
Erwin Wu
Tokyo Institute of Technology, Tokyo, Japan
Shinichi Furuya
Sony Computer Science Laboratories Inc., Shinagawa, Tokyo, Japan
Hideki Koike
Tokyo Institute of Technology, Tokyo, Japan
DOI

10.1145/3706598.3713465

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713465

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Embodied Stimulation

Annex Hall F206
7 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
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