Journal Article

Counting Finger and Wrist Movements Using Only a Wrist-Worn, Inertial Measurement Unit: Toward Practical Wearable Sensing for Hand-Related Healthcare Applications

Ibrahim, Mina, Chan, Vicky, Reinkensmeyer, David J., Yakunin, Roman, Schwerz de Lucena, Diogo, Okita, Shusuke, Korrapati, Jathin

2023

Overview

The ability to count finger and wrist movements throughout the day with a nonobtrusive, wearable sensor could be useful for hand-related healthcare applications, including rehabilitation after a stroke, carpal tunnel syndrome, or hand surgery. Previous approaches have required the user to wear a ring with an embedded magnet or inertial measurement unit (IMU). Here, we demonstrate that it is possible to identify the occurrence of finger and wrist flexion/extension movements based on vibrations detected by a wrist-worn IMU. We developed an approach we call “Hand Activity Recognition through using a Convolutional neural network with Spectrograms” (HARCS) that trains a CNN based on the velocity/acceleration spectrograms that finger/wrist movements create. We validated HARCS with the wrist-worn IMU recordings obtained from twenty stroke survivors during their daily life, where the occurrence of finger/wrist movements was labeled using a previously validated algorithm called HAND using magnetic sensing. The daily number of finger/wrist movements identified by HARCS had a strong positive correlation to the daily number identified by HAND (R2 = 0.76, p < 0.001). HARCS was also 75% accurate when we labeled the finger/wrist movements performed by unimpaired participants using optical motion capture. Overall, the ringless sensing of finger/wrist movement occurrence is feasible, although real-world applications may require further accuracy improvements.

• Publisher: MDPI AG,MDPI
• Subject: Algorithms / convolutional neural network (CNN) / Datasets / Delivery of Health Care / human activity recognition (HAR) / Humans / Hypotheses / Laboratories / Magnetic fields / Measurement / Motion capture / motion capture system (MC) / Movement / neural network / Neural networks / Pervasive developmental disorders / rehabilitation / Sensors / Stroke / Stroke - diagnosis / the inertial measurement unit (IMU) / Upper Extremity / Wearable Electronic Devices / Wrist