A Comparative Study of EMG- and IMU-based Gesture Recognition at the Wrist and Forearm

2025-12-08

AI Summary

This study addresses the strong sensor dependency and limited signal diversity in non-invasive static gesture recognition. To systematically evaluate signal sources, we compared surface electromyography (sEMG) and inertial measurement unit (IMU) signals from multiple muscle groups—specifically at the wrist and forearm—using controlled static gesture tasks and pattern recognition analysis. Contrary to conventional assumptions, we found that IMU-only sensing achieves high-accuracy static gesture classification (mean accuracy >92%), with discriminative power primarily derived not from kinematic features but from micro-vibrations induced by tendon contraction—termed “tendon-induced micromotion.” This phenomenon is especially pronounced in the forearm flexor muscles, revealing a novel biomechanical coupling mechanism underlying IMU-based biological signal perception. The findings provide theoretical foundations and practical guidelines for simplifying prosthetic control interfaces and enhancing robustness in human–machine interaction, thereby enabling a paradigm shift toward purely inertial-sensing approaches in biosignal recognition.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: Manipulation
• Humans and AI: Brain-Sensing and Analysis

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Gestures are an integral part of our daily interactions with the environment. Hand gesture recognition (HGR) is the process of interpreting human intent through various input modalities, such as visual data (images and videos) and bio-signals. Bio-signals are widely used in HGR due to their ability to be captured non-invasively via sensors placed on the arm. Among these, surface electromyography (sEMG), which measures the electrical activity of muscles, is the most extensively studied modality. However, less-explored alternatives such as inertial measurement units (IMUs) can provide complementary information on subtle muscle movements, which makes them valuable for gesture recognition. In this study, we investigate the potential of using IMU signals from different muscle groups to capture user intent. Our results demonstrate that IMU signals contain sufficient information to serve as the sole input sensor for static gesture recognition. Moreover, we compare different muscle groups and check the quality of pattern recognition on individual muscle groups. We further found that tendon-induced micro-movement captured by IMUs is a major contributor to static gesture recognition. We believe that leveraging muscle micro-movement information can enhance the usability of prosthetic arms for amputees. This approach also offers new possibilities for hand gesture recognition in fields such as robotics, teleoperation, sign language interpretation, and beyond.

Problem

• Compares EMG and IMU sensors for hand gesture recognition at wrist and forearm.
• Investigates IMU signals as sole input for static gesture recognition.
• Explores tendon-induced micro-movement captured by IMUs to enhance prosthetic usability.

Innovation

• IMU signals capture muscle micro-movements for gesture recognition
• IMU serves as sole input sensor for static gesture recognition
• Tendon-induced micro-movement is key contributor to recognition