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

Xingchen Yang, Dalin Zhou, Yu Zhou, Youjia Huang, Honghai Liu

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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
Issue number4
Early online date28 Aug 2018
Publication statusEarly online - 28 Aug 2018


  • ultrasound image
  • hand gesture recognition
  • probe position calibration
  • speeded-up robust features
  • bag-offeatures
  • classifier self-enhancing


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