Towards wearable A-mode ultrasound sensing for real-time finger motion recognition

Xingchen Yang, Xueli Sun, Dalin Zhou, Yuefeng Li, Honghai Liu

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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 as well as its prosperous applications in prosthetic devices, virtual reality and remote manipulation.
Original languageEnglish
Pages (from-to)1199-1208
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number6
Early online date25 Apr 2018
Publication statusEarly online - 25 Apr 2018


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