Exploring the Feasibility of Full-Body Muscle Activation Sensing with Insole Pressure Sensors

2026-02-11

AI Summary

This study addresses the limitations of conventional electromyography (EMG)-based sensing, which relies on dedicated electrodes and is ill-suited for long-term, unobtrusive mobile health monitoring. The authors propose a novel approach that leverages insole pressure sensors to capture subtle plantar pressure variations induced by whole-body muscle activation. By integrating user-specific biometric features, they develop a data-driven model capable of inferring muscle activity without direct skin contact. The method introduces a dynamic region-weighting scheme and a cross-user generalization mechanism, effectively eliminating dependence on EMG electrodes. Evaluated on a cohort of 30 participants, the system achieves an RMSE of 0.025—representing a 19% improvement over video-based methods—and demonstrates robust performance across diverse populations, footwear types, and surface conditions, highlighting its practical viability in real-world scenarios.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• muscle activation sensing
• insole pressure sensors
• mobile health
• long-term monitoring
• unobtrusive sensing

Innovation

• insole pressure sensing
• muscle activation inference
• data-driven personalization
• mobile health
• wearable sensing