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

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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
Volume75
Early online date19 Dec 2019
DOIs
Publication statusPublished - 1 Jan 2020

Documents

  • SC_USHMIR

    Accepted author manuscript (Post-print), 1.66 MB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 19/12/20

    Licence: CC BY-NC-ND

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