能量学
可穿戴计算机
原位
计算机科学
嵌入式系统
物理
生物
生态学
气象学
作者
Jiajie Guo,Yiran Tong,Chuxuan Guo,Zijie Liu,Hao Yin,Yuchao Liu,Zhuo Li,Hao Wu,Caihua Xiong
出处
期刊:Soft science
[OAE Publishing Inc.]
日期:2025-04-22
卷期号:5 (2)
摘要
Skeletal muscles, as the primary actuators for voluntary limb motions, achieve motion dexterity and endurance at the cost of majority of metabolic energy. Muscle energetics provide a powerful framework for examining motion skills, serving as fundamental mechanisms for converting metabolic energy into effective work to optimize motion performance through muscle synergy. However, existing energy sensing methods are sensitive to physiological, psychological, and environmental disturbances, making it challenging to monitor the energetic dynamics of muscle synergy. Inspired by the characteristics of muscle excitation and contraction, this study proposes a neural-mechanical sensing method to perceive muscle work by integrating the myoelectric and capacitive measurements that are indicative of muscle forces and contraction displacements. The proposed sensing method is validated through the weight lifting tests, comparing results against the dynamic analysis and muscle oxygen consumption. To the best of our knowledge, this research is the first to achieve in-situ real-time wearable sensing of muscle work. It is expected to pave a practical way to study muscle energetics that is beneficial to sports science, rehabilitation medicine and robotics engineering.
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