Hand Gesture Recognition and Finger Angle Estimation via Wrist-Worn Modified Barometric Pressure Sensing

This paper presents a new approach to wearable hand gesture recognition and finger angle estimation based on the modified barometric pressure sensing. Barometric pressure sensors were encased and injected with VytaFlex rubber such that the rubber directly contacted the sensing element allowing pressure change detection when the encasing rubber was pressed. A wearable prototype consisting of an array of ten modified barometric pressure sensors around the wrist was developed and validated with experimental testing for three different hand gesture sets and finger flexion/extension trials for each of the five fingers. The overall hand gesture recognition classification accuracy was 94%. Further analysis revealed that the most important sensor location was the underside of the wrist and that when reducing the sensor number to only five optimally placed sensors, classification accuracy was still 90%. For continuous finger angle estimation, aggregate R² values between actual and predicted angles were thumb: 0.81 ± 0.10, index finger: 0.85 ± 0.06, middle finger: 0.77 ± 0.08, ring finger: 0.77 ± 0.12, and pinkie finger: 0.75 ± 0.10, and the overall average was 0.79 ± 0.05. These results demonstrate that a modified barometric pressure wristband can be used to classify hand gestures and to estimate individual finger joint angles. This approach could serve to improve the clinical treatment for upper extremity deficiencies, such as for stroke rehabilitation, by providing objective patient motor control metrics to inform and aid physicians and therapists throughout the rehabilitation process.

I. Introduction

HAND movement measurements are vital for upper extremity rehabilitation, particularly after stroke [1]. Wearable hand gesture recognition is a promising approach as it has been shown to aid in upper extremity stroke rehabilitation [2]. Wearables can further serve to monitor short-and long-term rehabilitation progress for acute injuries and chronic disease [3] by providing objective patient metrics to enable therapists to optimize such rehabilitation programs [4]. Wearable hand gesture recognition is also fundamental to enabling intelligent upper extremity prosthetics to function in real-life situations [5], [6].