Ultrasound feature evaluation for robustness to sensor shift in ultrasound sensor based hand motion recognition

Peter Boyd, Yinfeng Fang, Honghai Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Pattern Recognition based approaches have offered great promise in the field of bio-signal controlled prosthesis. Traditionally Surface Electromyography based Approaches (SEMG) have been used to satisfy the purpose of providing Bio-Signal control in upper extremity Prosthesis. Although these methods have been shown to be robust, there still exists issues in performance within clinical environments. In recent years, Ultrasound signal based methods have seen growing interest within the field of motion Recognition, largely due to the increased resolution, deeper muscle observation, and reduced cross-talk that can be achieved in comparison to SEMG methods. However, the methods to be applied for hand Motion recognition are still only just beginning to be explored. In this paper, we shall investigate the applicability of SEMG feature extraction techniques to Ultrasound based hand motion recognition and the subsequent impact of Sensor shift on these features. The results of this study indicate that SEMG feature extraction techniques have excellent single location accuracy in Ultrasound based Hand motion recognition. However this paper more visibly presents the strong impact of Sensor Shift on A-Mode ultrasound based hand motion Recognition, and finally presents which feature extraction methods are most robust to this shift.

Original languageEnglish
Title of host publicationTowards Autonomous Robotic Systems
Subtitle of host publication20th Annual Conference, TAROS 2019, London, UK, July 3–5, 2019, Proceedings, Part I
EditorsKaspar Althoefer, Jelizaveta Konstantinova, Ketao Zhang
PublisherSpringer
Pages115-125
Number of pages11
ISBN (Electronic)978-3-030-23807-0
ISBN (Print)978-3-030-23806-3
DOIs
Publication statusPublished - 28 Jun 2019
Event20th Annual Conference on Towards Autonomous Robotic Systems - London, United Kingdom
Duration: 3 Jul 20195 Jul 2019

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11649
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume11649
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th Annual Conference on Towards Autonomous Robotic Systems
Abbreviated titleTAROS 2019
CountryUnited Kingdom
CityLondon
Period3/07/195/07/19

Keywords

  • Feature extraction
  • Hand gesture recognition
  • Pattern recognition
  • Prosthesis
  • Sensor shift
  • Ultrasound

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