Gesture Segmentation and Recognition with an EMG-Based Intimate Approach - An Accuracy and Usability Study

Abstract

In this paper we propose an approach to address the gesture segmentation issue, an important concern strongly related to the gesture recognition field. Gesture segmentation has two main goals: first, detecting when a gesture begins and ends, second, understanding whether a gesture is meant to be meaningful for the machine or is a non-command gesture (such as gesticulation). This work proposes a novel hands-free, always-available approach for the gesture segmentation and recognition in which the user can communicate directly to the system through a wearable and "intimate" interface based on electromyography signals (EMG). The system addresses the well-known "gorilla-arm" problem recognizing subtle gestures and segmenting them through motionless gestures. We report experimental results indicating that the system is able to reliably detect and recognize subtle gestures, with minimal training across users with different muscle volumes, representing a consistent gesture segmentation approach. Finally, the usability tests showed that the system is easy to use and the subjects felt quickly confident with its utilization.