Multi-Grip Classification-Based Prosthesis Control With Two EMG-IMU Sensors

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

In the field of upper-limb myoelectric prosthesis control, the use of statistical and machine learning methods has been long proposed as a means of enabling intuitive grip selection and actuation. Recently, this paradigm has found its way toward commercial adoption. Machine learning-based prosthesis control typically relies on the use of a large number of electrodes. Here, we propose an end-to-end strategy for multi-grip, classification-based prosthesis control using only two sensors, comprising electromyography (EMG) electrodes and inertial measurement units (IMUs). We emphasize the importance of accurately estimating posterior class probabilities and rejecting predictions made with low confidence, so as to minimize the rate of unintended prosthesis activations. To that end, we propose a confidence-based error rejection strategy using grip-specific thresholds. We evaluate the efficacy of the proposed system with real-time pick and place experiments using a commercial multi-articulated prosthetic hand and involving 12 able-bodied and two transradial (i.e., below-elbow) amputee participants. Results promise the potential for deploying intuitive, classification-based multi-grip control in existing upper-limb prosthetic systems subject to small modifications.
Original languageEnglish
Pages (from-to)508-518
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number2
Early online date13 Dec 2019
Publication statusPublished - 1 Feb 2020

Keywords / Materials (for Non-textual outputs)

  • Classification
  • electromyography
  • Inertial measurement unit
  • myoelectric control
  • ensor minimization
  • upper-limb prosthesis


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