Towards Wearable A-Mode Ultrasound Sensing for Real-Time Finger Motion Recognition

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 26; no. 6; pp. 1199 - 1208
• Main Authors: Yang, Xingchen, Sun, Xueli, Zhou, Dalin, Li, Yuefeng, Liu, Honghai
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: A-mode ultrasound; Change detection; Computer applications; Electromyography; Feasibility studies; Feature extraction; Finger; finger motion recognition; Graphical user interfaces; Human motion; Human-machine interface; Imaging; Interfaces; Internet; Motion perception; Muscles; online gesture recognition; Physical characteristics; Prostheses; Real time; Real-time systems; Recognition; Tendons; Transducers; Ultrasonic imaging; Ultrasound; Virtual reality; Wearable technology
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2018.2829913

It is evident that surface electromyography (sEMG) based human-machine interfaces (HMI) have inherent difficulty in predicting dexterous musculoskeletal movements such as finger motions. This paper is an attempt to investigate a plausible alternative to sEMG, ultrasound-driven HMI, for dexterous motion recognition due to its characteristic of detecting morphological changes of deep muscles and tendons. A multi-channel A-mode ultrasound lightweight device is adopted to evaluate the performance of finger motion recognition; an experiment is designed for both widely acceptable offline and online algorithms with eight able-bodied subjects employed. The experiment result presents that the offline recognition accuracy is up to 98.83% ± 0.79%. The real-time motion completion rate is 95.4% ± 8.7% and online motion selection time is 0.243 ± 0.127 s. The outcomes confirm the feasibility of A-mode ultrasound based wearable HMI and its prosperous applications in prosthetic devices, virtual reality, and remote manipulation.