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

TL;DR

This paper compares EMG and IMU sensors for wrist and forearm gesture recognition, demonstrating IMUs can effectively recognize static gestures and highlighting the importance of tendon micro-movements.

Contribution

It provides a comprehensive comparison of IMU and EMG modalities for gesture recognition and emphasizes the role of tendon micro-movements captured by IMUs as a key recognition feature.

Findings

IMU signals are sufficient for static gesture recognition.

Different muscle groups provide varying recognition quality.

Tendon micro-movements significantly contribute to gesture recognition.

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.