Skip to content

Towards zero re-training for long-term hand gesture recognition via ultrasound sensing

Research output: Contribution to journalArticlepeer-review

While myoelectric pattern recognition is a prevailing way for gesture recognition, the inherent nonstationarity of electromyography signals hinders its long-term application. This study aims to prove a hypothesis that morphological information of muscle contraction detected by ultrasound image is potentially suitable for long-term use. A set of ultrasound-based algorithms are proposed to realize robust hand gesture recognition over multiple days, with user training only at the first day. A markerless calibration algorithm is first presented to position the ultrasound probe during donning and doffing; an algorithm combining speeded-up robust features (SURF) and bag-of-features (BoF) model being immune to ultrasound probe shift and rotation is then introduced; a self-enhancing classification method is next adopted to update classification model automatically by incorporating useful knowledge from testing data; finally the performance of long-term hand gesture recognition with zero re-training is validated by a six-day experiment of six healthy subjects, whose outcomes strongly support the hypothesis with about 94% of gesture recognition accuracy for each testing day. This study confirms the feasibility of adoption of ultrasound sensing for long-term musculature related applications.
Original languageEnglish
Article number0
Pages (from-to)1639 - 1646
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number4
Early online date28 Aug 2018
DOIs
Publication statusEarly online - 28 Aug 2018

Documents

  • Towards Zero Retraining

    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), 3.07 MB, PDF document

Relations Get citation (various referencing formats)

ID: 11371233