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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-06, Vol.23 (12), p.5690
• Main Authors: Okita, Shusuke, Yakunin, Roman, Korrapati, Jathin, Ibrahim, Mina, Schwerz de Lucena, Diogo, Chan, Vicky, Reinkensmeyer, David J
• Format: Article
• Language: English
• Subjects: Acceleration; Activity recognition; Algorithms; Carpal tunnel syndrome; convolutional neural network (CNN); Datasets; Fingers; Hand (anatomy); Health care; human activity recognition (HAR); Hypotheses; Inertial platforms; Laboratories; Magnetic fields; Measurement; Motion capture; motion capture system (MC); neural network; Neural networks; Pervasive developmental disorders; rehabilitation; Sensors; Spectrograms; Stroke; the inertial measurement unit (IMU); Wearable technology; Wrist
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23125690

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 (R = 0.76, < 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.