Skip to content

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

Research output: Contribution to journalArticlepeer-review

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
Volume26
Issue number6
Early online date25 Apr 2018
DOIs
Publication statusEarly online - 25 Apr 2018

Documents

  • FINAL VERSION

    Rights statement: © © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    Accepted author manuscript (Post-print), 1.75 MB, PDF document

Relations Get citation (various referencing formats)

ID: 10307871