Ultrasonography and electromyography based hand motion intention recognition for a trans-radial amputee: a case study

Zheng Wang, Yinfeng Fang, Dalin Zhou, Kairu Li, Christophe Cointet, Honghai Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Surface electromyography (sEMG) has dominated upper-limb prosthesis control for decades due to its simplicity and effectiveness [1–3]. However, the inherent variability of EMG signal hinders the flexible and accurate control of advanced multi-functional prosthesis. This study is an attempt to use ultrasonography (US) as an alternative for prosthetic hand control. A type of multi-sensory module, comprising a single-element ultrasound channel and one sEMG bipolar channel, is customised to ensure a fair comparison between these two modalities. Three machine-learning-oriented approaches were adopted to evaluate the performance in motion classification based on datasets captured from a trans-radial amputee. The experimental results demonstrated that the ultrasound outperformed the sEMG in random (98.9% vs 70.4%) and enhanced-trial-wise (74.10% vs 61.83%) cross-validation, but fell behind the sEMG in trial-wise (39.47% vs 58.04%) validation that is the closest comparison to a real life prosthetic control. This study preliminarily implies that 1) A-mode ultrasound signal can be more stable than the sEMG with minimum electrode shift, but more sensitive to external interference than the sEMG; and 2) to maintain high classification accuracy, US approach may require harsher electrode fixing mechanism or advanced on-line calibration approach.

Original languageEnglish
Pages (from-to)45-48
Number of pages4
JournalMedical Engineering and Physics
Early online date19 Dec 2019
Publication statusPublished - 1 Jan 2020


  • Electromyography
  • Prosthesis
  • Ultrasonography
  • Upper-limb amputee


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