Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention
Targeted muscle reinnervation (TMR) was initially developed as a technique for bionic prosthetic control but has since become a widely adopted strategy for managing pain and preventing neuroma formation after amputation. This shift in TMR’s motivation has influenced surgical approaches, i...
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/11071642/ |
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| author | Anna Rita E. Moukarzel Justin Fitzgerald Marcus Battraw Clifford Pereira Andrew Li Paul Marasco Wilsaan M. Joiner Jonathon Schofield |
| author_facet | Anna Rita E. Moukarzel Justin Fitzgerald Marcus Battraw Clifford Pereira Andrew Li Paul Marasco Wilsaan M. Joiner Jonathon Schofield |
| author_sort | Anna Rita E. Moukarzel |
| collection | DOAJ |
| description | Targeted muscle reinnervation (TMR) was initially developed as a technique for bionic prosthetic control but has since become a widely adopted strategy for managing pain and preventing neuroma formation after amputation. This shift in TMR’s motivation has influenced surgical approaches, in ways that may challenge conventional electromyography (EMG)-based prosthetic control. The primary goal is often to simply reinnervate nerves to accessible muscles. This contrasts the earlier, more complex TMR surgeries that optimize EMG signal detection by carefully selecting target muscles near the skin’s surface and manipulate residual anatomy to electrically isolate muscle activity. Consequently, modern TMR surgeries can involve less consideration for factors such as the depth of the reinnervated muscles or electrical crosstalk between closely located reinnervated muscles, all of which can impair the effectiveness of conventional prosthetic control systems. We recruited 4 participants with TMR, varying levels of upper limb loss, and diverse sets of reinnervated muscles. Participants attempted performing movements with their missing hands and we used a muscle activity measurement technique that employs ultrasound imaging and machine learning (sonomyography) to classify the resulting muscle movements. We found that attempted missing hand movements resulted in unique patterns of deformation in the reinnervated muscles and applying a K-nearest neighbors machine learning algorithm, we could predict 4-10 hand movements for each participant with 83.3-99.4% accuracy. Our findings suggest that despite the shifting motivations for performing TMR surgery this new generation of the surgical procedure not only offers prophylactic benefits but also retains promising opportunities for bionic prosthetic control. |
| format | Article |
| id | doaj-art-c92ed42ebd244ecea3e5ac705ddccd9b |
| institution | Kabale University |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-c92ed42ebd244ecea3e5ac705ddccd9b2025-08-20T03:30:23ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01332631263710.1109/TNSRE.2025.358617411071642Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain PreventionAnna Rita E. Moukarzel0https://orcid.org/0009-0006-0065-2828Justin Fitzgerald1Marcus Battraw2https://orcid.org/0000-0002-8577-4974Clifford Pereira3Andrew Li4https://orcid.org/0000-0001-9933-7057Paul Marasco5https://orcid.org/0000-0002-1689-7161Wilsaan M. Joiner6https://orcid.org/0000-0001-6816-8473Jonathon Schofield7https://orcid.org/0000-0002-6422-7429Department of Biomedical Engineering, University of Alberta, Edmonton, CanadaDepartment of Biomedical Engineering, University of California at Davis, Davis, CA, USADepartment of Mechanical and Mechatronic Engineering and Advanced Manufacturing, California State University at Chico, Chico, CA, USADepartment of Surgery, Hand and Microsurgery, Division of Plastic Surgery, University of California at Davis, Sacramento, CA, USADepartment of Surgery, Hand and Microsurgery, Division of Plastic Surgery, University of California at Davis, Sacramento, CA, USADepartment of Biomedical Engineering, Laboratory for Bionic Integration, Lerner Research Institute, Cleveland, OH, USADepartment of Neurobiology, Physiology and Behavior, University of California at Davis, Davis, CA, USADepartment of Mechanical and Aerospace Engineering, University of California at Davis, Davis, CA, USATargeted muscle reinnervation (TMR) was initially developed as a technique for bionic prosthetic control but has since become a widely adopted strategy for managing pain and preventing neuroma formation after amputation. This shift in TMR’s motivation has influenced surgical approaches, in ways that may challenge conventional electromyography (EMG)-based prosthetic control. The primary goal is often to simply reinnervate nerves to accessible muscles. This contrasts the earlier, more complex TMR surgeries that optimize EMG signal detection by carefully selecting target muscles near the skin’s surface and manipulate residual anatomy to electrically isolate muscle activity. Consequently, modern TMR surgeries can involve less consideration for factors such as the depth of the reinnervated muscles or electrical crosstalk between closely located reinnervated muscles, all of which can impair the effectiveness of conventional prosthetic control systems. We recruited 4 participants with TMR, varying levels of upper limb loss, and diverse sets of reinnervated muscles. Participants attempted performing movements with their missing hands and we used a muscle activity measurement technique that employs ultrasound imaging and machine learning (sonomyography) to classify the resulting muscle movements. We found that attempted missing hand movements resulted in unique patterns of deformation in the reinnervated muscles and applying a K-nearest neighbors machine learning algorithm, we could predict 4-10 hand movements for each participant with 83.3-99.4% accuracy. Our findings suggest that despite the shifting motivations for performing TMR surgery this new generation of the surgical procedure not only offers prophylactic benefits but also retains promising opportunities for bionic prosthetic control.https://ieeexplore.ieee.org/document/11071642/Sonomyographyprosthetic controltargeted muscle reinnervation |
| spellingShingle | Anna Rita E. Moukarzel Justin Fitzgerald Marcus Battraw Clifford Pereira Andrew Li Paul Marasco Wilsaan M. Joiner Jonathon Schofield Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention IEEE Transactions on Neural Systems and Rehabilitation Engineering Sonomyography prosthetic control targeted muscle reinnervation |
| title | Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention |
| title_full | Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention |
| title_fullStr | Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention |
| title_full_unstemmed | Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention |
| title_short | Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention |
| title_sort | ultrasound imaging and machine learning to detect missing hand motions for individuals receiving targeted muscle reinnervation for nerve pain prevention |
| topic | Sonomyography prosthetic control targeted muscle reinnervation |
| url | https://ieeexplore.ieee.org/document/11071642/ |
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