Improvements of a Simple Piezoresistive Array Armband for Gesture Recognition

This study presents a simple Human Machine Interface (HMI) for gesture recognition purpose, based on a wireless piezoresistive armband. The armband embeds three sensors based on Force Sensitive Resistors (FSRs) applied on specific forearm muscles, which provide signals comparable to the electromyography linear envelope. The system aims to recognize in real-time some hand gestures, opportunely processing the force signals. The HMI control system is based on Arduino platform and implements a Linear Discriminant Analysis (LDA) classifier to perform real-time gesture recognition. The HMI, by means of a Bluetooth module can wireless connect to a computer and provide commands to custom graphical interfaces or other applications as videogames. Four healthy volunteers were involved in testing the device. A double threshold segmentation technique, applied to the recorded FSR signals, allowed to detect and extract features regarding the whole-time evolution of a performed gesture, explicitly considering the transition between gestures. LDA classification performances were assessed by applying the 10-fold cross validation. The mean accuracy across all participants resulted 93.48%. The results suggest that the HMI device allows easy user's interaction with a computer in real-time. In prospective, this device could find practical applications in neuromotor rehabilitation (e.g. exergaming), and in controlling prosthetic hands.