Put Your Muscle Into It: Introducing XEM2, a Novel Approach for Monitoring Exertion in Stationary Physical Exercises Leveraging Muscle Work

2026-01-21

AI Summary

This work addresses the limitations of existing motion monitoring approaches in accurately assessing muscle loading during non-uniform movements and providing fine-grained feedback on the activation levels of individual muscle groups. To this end, we propose XEM², a vision-based system that, for the first time, enables quantitative estimation and visualization of mechanical work performed by specific muscle groups. By integrating Hill-type muscle models with computer vision and biomechanical analysis, XEM² leverages an ordinary camera to non-invasively measure muscle work during static exercises. Experimental results demonstrate that XEM² reliably reflects actual muscle loading, aligns closely with users’ subjective perception of exertion, and significantly enhances the precision of exercise monitoring and training balance.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We present a novel system for camera-based measurement and visualization of muscle work based on the Hill-Type-Muscle-Model: the exercise exertion muscle-work monitor (\textit{XEM}$^{2}$). Our aim is to complement and, thus, address issues of established measurement techniques that offer imprecise data for non-uniform movements (burned calories) or provide limited information on strain across different body parts (self-perception scales). We validate the reliability of XEM's measurements through a technical evaluation of ten participants and five exercises. Further, we assess the acceptance, usefulness, benefits, and opportunities of \textit{XEM}$^{2}$ in an empirical user study. Our results show that \textit{XEM}$^{2}$ provides reliable values of muscle work and supports participants in understanding their workout while also providing reliable information about perceived exertion per muscle group. With this paper, we introduce a novel system capable of measuring and visualizing exertion for single muscle groups, which has the potential to improve exercise monitoring to prevent unbalanced workouts.

Problem

• exercise monitoring
• muscle exertion
• muscle work
• stationary physical exercises
• workout imbalance

Innovation

• muscle work
• exertion monitoring
• Hill-Type Muscle Model
• camera-based sensing
• exercise visualization