Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface
Abstract Objective Seventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the s...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-01-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
| Online Access: | https://doi.org/10.1186/s12984-023-01301-w |
| _version_ | 1797355940917280768 |
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| author | Eric C. Meyers David Gabrieli Nick Tacca Lauren Wengerd Michael Darrow Bryan R. Schlink Ian Baumgart David A. Friedenberg |
| author_facet | Eric C. Meyers David Gabrieli Nick Tacca Lauren Wengerd Michael Darrow Bryan R. Schlink Ian Baumgart David A. Friedenberg |
| author_sort | Eric C. Meyers |
| collection | DOAJ |
| description | Abstract Objective Seventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs. Approach To address these limitations, we have developed the NeuroLife® EMG System, an investigational device which consists of a wearable forearm sleeve with 150 embedded electrodes and associated hardware and software to record and decode surface EMG. Here, we demonstrate accurate decoding of 12 functional hand, wrist, and forearm movements in chronic stroke survivors, including multiple types of grasps from participants with varying levels of impairment. We also collected usability data to assess how the system meets user needs to inform future design considerations. Main results Our decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve’s design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort. Significance The NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use. |
| first_indexed | 2024-03-08T14:19:25Z |
| format | Article |
| id | doaj.art-a8f8def22b114e7c8678fe04845f01c6 |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-03-08T14:19:25Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-a8f8def22b114e7c8678fe04845f01c62024-01-14T12:14:13ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032024-01-0121111810.1186/s12984-023-01301-wDecoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interfaceEric C. Meyers0David Gabrieli1Nick Tacca2Lauren Wengerd3Michael Darrow4Bryan R. Schlink5Ian Baumgart6David A. Friedenberg7Medical Device Solutions, Battelle Memorial InstituteHealth Analytics, Battelle Memorial InstituteMedical Device Solutions, Battelle Memorial InstituteMedical Device Solutions, Battelle Memorial InstituteMedical Device Solutions, Battelle Memorial InstituteMedical Device Solutions, Battelle Memorial InstituteMedical Device Solutions, Battelle Memorial InstituteHealth Analytics, Battelle Memorial InstituteAbstract Objective Seventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs. Approach To address these limitations, we have developed the NeuroLife® EMG System, an investigational device which consists of a wearable forearm sleeve with 150 embedded electrodes and associated hardware and software to record and decode surface EMG. Here, we demonstrate accurate decoding of 12 functional hand, wrist, and forearm movements in chronic stroke survivors, including multiple types of grasps from participants with varying levels of impairment. We also collected usability data to assess how the system meets user needs to inform future design considerations. Main results Our decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve’s design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort. Significance The NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use.https://doi.org/10.1186/s12984-023-01301-w |
| spellingShingle | Eric C. Meyers David Gabrieli Nick Tacca Lauren Wengerd Michael Darrow Bryan R. Schlink Ian Baumgart David A. Friedenberg Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface Journal of NeuroEngineering and Rehabilitation |
| title | Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface |
| title_full | Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface |
| title_fullStr | Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface |
| title_full_unstemmed | Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface |
| title_short | Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface |
| title_sort | decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user friendly wearable emg based neural interface |
| url | https://doi.org/10.1186/s12984-023-01301-w |
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