Muscle activation initiates contractions that drive human movement, and understanding it provides valuable insights for injury prevention and rehabilitation. Yet, sensing muscle activation is barely explored in the rapidly growing mobile health market. Traditional methods for muscle activation sensing rely on specialized electrodes, such as surface electromyography, making them impractical, especially for long-term usage. In this paper, we introduce Press2Muscle, the first system to unobtrusively infer muscle activation using insole pressure sensors. The key idea is to analyze foot pressure changes resulting from full-body muscle activation that drives movements. To handle variations in pressure signals due to differences in users' gait, weight, and movement styles, we propose a data-driven approach to dynamically adjust reliance on different foot regions and incorporate easily accessible biographical data to enhance Press2Muscle's generalization to unseen users. We conducted an extensive study with 30 users. Under a leave-one-user-out setting, Press2Muscle achieves a root mean square error of 0.025, marking a 19% improvement over a video-based counterpart. A robustness study validates Press2Muscle's ability to generalize across user demographics, footwear types, and walking surfaces. Additionally, we showcase muscle imbalance detection and muscle activation estimation under free-living settings with Press2Muscle, confirming the feasibility of muscle activation sensing using insole pressure sensors in real-world settings.

翻译：肌肉激活引发驱动人体运动的收缩，理解这一过程为损伤预防与康复提供了宝贵见解。然而，在快速发展的移动健康市场中，肌肉激活感知技术尚未得到充分探索。传统的肌肉激活感知方法依赖于表面肌电图等专用电极，使其难以实际应用，尤其不适用于长期监测。本文提出Press2Muscle系统，首次实现利用鞋垫压力传感器无干扰地推断肌肉激活状态。其核心思想在于分析由驱动运动的全身肌肉激活所引起的足底压力变化。为处理因用户步态、体重和运动方式差异导致的压力信号变异，我们提出一种数据驱动方法：动态调整对不同足部区域的依赖度，并结合易获取的个人生理数据以增强Press2Muscle对未见用户的泛化能力。我们开展了包含30名用户的广泛研究。在留一用户交叉验证设置下，Press2Muscle的均方根误差达到0.025，较基于视频的对照方法提升19%。鲁棒性研究验证了Press2Muscle在不同用户群体、鞋具类型和行走表面间的泛化能力。此外，我们通过Press2Muscle展示了自由生活场景下的肌肉失衡检测与肌肉激活估计功能，证实了在现实环境中使用鞋垫压力传感器进行肌肉激活感知的可行性。