Evoked Tactile Feedback and Control Scheme on Functional Utility of Prosthetic Hand

Luis Vargas, He Huang, Yong Zhu, Xiaogang Hu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Fine manual control relies on intricate action-perception coupling to effectively interact with objects. Here, we evaluated how electrically evoked artificial tactile sensation can be integrated into the functional utility of a prosthetic hand. Using different myoelectric-control strategies, participants performed a modified box-and-block task using a prosthetic hand. Transcutaneous nerve stimulation was employed to elicit somatotopic fingertip tactile feedback reflecting prosthetic fingertip forces. This feedback was evoked using an electrode grid placed along the participants' upper arm targeting the median and ulnar nerve bundles. Myoelectric signals from the finger flexor and extensor controlled the prosthetic joint velocity or position. Participants lifted, held, and transported cubes of varying weights using their minimum grip forces. The results showed that participants exerted lower forces and presented lower number of failed trials (prematurely dropped objects) when feedback was provided with respect to without feedback. We also found that position control required more flexor muscle activation compared with velocity control when tactile feedback was provided. Our findings reveal that non-invasively evoked tactile feedback could be used to effectively enable human-in-the-loop control of a prosthetic hand. The outcomes can provide a platform to characterize the action-perception couplings during prosthetic control, in order to improve user experience and system functionality.

Original languageEnglish (US)
Pages (from-to)1308-1315
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number2
DOIs
StatePublished - Apr 1 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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