Journal article
Neurophysiological Evaluation of Haptic Feedback for Myoelectric Prostheses
IEEE transactions on human-machine systems, v 51(3), pp 253-264
Jun 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Evaluations of haptic feedback in myoelectric prostheses are generally limited to task performance outcomes, which while necessary, fail to capture the mental effort of the user operating the prosthesis. Cognitive load is usually investigated with reaction time metrics and secondary task accuracy, which are indirect, and may not capture the time-varying nature of mental effort. Here, we propose wearable, wireless functional near infrared spectroscopy (fNIRS) neuroimaging to provide a continuous direct assessment of operator mental effort during use of a prosthesis. Utilizing fNIRS in a two-alternative forced-choice stiffness discrimination task, we asked participants to differentiate objects using their natural hand, a (traditional) myoelectric prosthesis without sensory feedback, and a myoelectric prosthesis with haptic (vibrotactile) feedback of grip force. Results showed that discrimination accuracy and mental effort are optimal with the natural hand, followed by the prosthesis featuring haptic feedback, and then the traditional prosthesis, particularly for objects whose stiffness were difficult to differentiate. This experiment highlights the utility of haptic feedback in improving task performance and lowering cognitive load for prosthesis use, and demonstrates the potential for fNIRS to provide a robust measure of cognitive effort for other human-in-the-loop systems.
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Details
- Title
- Neurophysiological Evaluation of Haptic Feedback for Myoelectric Prostheses
- Creators
- Neha Thomas - Dept. of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.Garrett Ung - Johns Hopkins UniversityHasan Ayaz - Drexel UniversityJeremy D Brown - Johns Hopkins University
- Publication Details
- IEEE transactions on human-machine systems, v 51(3), pp 253-264
- Publisher
- IEEE
- Grant note
- NSF Graduate Research Fellowship
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000652789700008
- Scopus ID
- 2-s2.0-85104189282
- Other Identifier
- 991019168442604721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Cybernetics