EchoForce: Continuous Grip Force Estimation from Skin Deformation Using Active Acoustic Sensing on a Wristband

2025-07-27

AI Summary

Existing wearable grip-force measurement methods are bulky, require user-specific calibration, and cannot support continuous, universal, contactless monitoring. This paper introduces EchoForce, a wristband system that enables robust, cross-user, cross-temporal, and cross-hand-pose grip-force estimation by detecting micro-deformations of the skin induced by forearm flexor muscle contractions, using active acoustic sensing. EchoForce integrates physics-informed acoustic signal modeling with a lightweight machine learning framework, eliminating the need for personalized calibration or precise佩戴 positioning. Experiments with 11 participants demonstrate mean absolute errors of 9.08% for user-specific models and 12.3% for user-agnostic models, with high robustness across diverse scenarios. To our knowledge, this is the first work to employ active acoustic sensing for continuous grip-force reconstruction, establishing a new paradigm for low-cost, unobtrusive health monitoring and rehabilitation assessment.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Machine Learning: Active Learning
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

Abstract

Grip force is commonly used as an overall health indicator in older adults and is valuable for tracking progress in physical training and rehabilitation. Existing methods for wearable grip force measurement are cumbersome and user-dependent, making them insufficient for practical, continuous grip force measurement. We introduce EchoForce, a novel wristband using acoustic sensing for low-cost, non-contact measurement of grip force. EchoForce captures acoustic signals reflected from subtle skin deformations by flexor muscles on the forearm. In a user study with 11 participants, EchoForce achieved a fine-tuned user-dependent mean error rate of 9.08% and a user-independent mean error rate of 12.3% using a foundation model. Our system remained accurate between sessions, hand orientations, and users, overcoming a significant limitation of past force sensing systems. EchoForce makes continuous grip force measurement practical, providing an effective tool for health monitoring and novel interaction techniques.

Problem

• Continuous grip force estimation from skin deformation
• Overcoming limitations of existing wearable force measurement methods
• Enabling practical health monitoring and interaction techniques

Innovation

• Active acoustic sensing for grip force
• Wristband captures skin deformation signals
• Low-cost non-contact continuous measurement