An Unsupervised Approach to Identify Patient-Specific EMG Detector to Trigger Robot-Assisted Therapy Article Swipe
Monisha Yuvaraj
,
Akash Prabakar
,
Varadhan SKM
,
Etienne Burdet
,
Ander Ramos-Murgialday
,
Sivakumar Balasubramanian
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.12.06.24318597
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.12.06.24318597
A bstract In severely impaired stroke patients, implementing EMG-driven robot-assisted therapy requires the presence of sufficient residual EMG and a patient-specific detector for accurate and low-latency EMG detection. However, identifying such a detector is challenging, especially when the level of residual EMG in a given patient is unknown . This paper proposes an unsupervised approach to distinguish between EMG data when the patient is relaxed versus attempting a movement – the maximally separating detector . We investigated six different detector types and separation measures using EMG data from a previous randomized controlled trial. The results indicate that the approximate generalized likelihood ratio detector, along with the modified Hodges and modified Lidierth detectors, achieved the best separation. Using a subset of clinician annotated data to evaluate the detection performance, the modified Hodges detector employing the probability difference-sum ratio measure had the best detection performance in terms of detection accuracy and latency. Using the data from 30 participants, we propose a probability difference-sum ratio threshold of 0.7 for the modified Hodges detector to identify patients with sufficient residual EMG to trigger robotic assistance. From the results, we propose the use of modified Hodges detector along with a probability difference-sum ratio measure to learn the maximally separating detector for a given patient, which will screen the patient for sufficient residual EMG and provide a detector to trigger robotic assistance if sufficient EMG is present. The validation of this approach using a large dataset and investigating the quality of the human-machine interaction implemented with such a detector is warranted.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.12.06.24318597
- https://www.medrxiv.org/content/medrxiv/early/2024/12/10/2024.12.06.24318597.full.pdf
- OA Status
- green
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405205610
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405205610Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.12.06.24318597Digital Object Identifier
- Title
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An Unsupervised Approach to Identify Patient-Specific EMG Detector to Trigger Robot-Assisted TherapyWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-12-10Full publication date if available
- Authors
-
Monisha Yuvaraj, Akash Prabakar, Varadhan SKM, Etienne Burdet, Ander Ramos-Murgialday, Sivakumar BalasubramanianList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.12.06.24318597Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2024/12/10/2024.12.06.24318597.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2024/12/10/2024.12.06.24318597.full.pdfDirect OA link when available
- Concepts
-
Stroke (engine), Physical medicine and rehabilitation, Artificial intelligence, Detector, Computer science, Medicine, Engineering, Telecommunications, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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18Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ratio | 101, 136, 161, 197 |
| abstract_inverted_index.terms | 144 |
| abstract_inverted_index.types | 80 |
| abstract_inverted_index.using | 84, 236 |
| abstract_inverted_index.which | 209 |
| abstract_inverted_index.Hodges | 107, 130, 168, 190 |
| abstract_inverted_index.screen | 211 |
| abstract_inverted_index.stroke | 5 |
| abstract_inverted_index.subset | 118 |
| abstract_inverted_index.trial. | 92 |
| abstract_inverted_index.versus | 65 |
| abstract_inverted_index.between | 57 |
| abstract_inverted_index.bstract | 1 |
| abstract_inverted_index.dataset | 239 |
| abstract_inverted_index.measure | 137, 198 |
| abstract_inverted_index.patient | 45, 62, 213 |
| abstract_inverted_index.propose | 157, 185 |
| abstract_inverted_index.provide | 219 |
| abstract_inverted_index.quality | 243 |
| abstract_inverted_index.relaxed | 64 |
| abstract_inverted_index.results | 94 |
| abstract_inverted_index.robotic | 179, 224 |
| abstract_inverted_index.therapy | 10 |
| abstract_inverted_index.trigger | 178, 223 |
| abstract_inverted_index.unknown | 47 |
| abstract_inverted_index.However, | 28 |
| abstract_inverted_index.Lidierth | 110 |
| abstract_inverted_index.accuracy | 147 |
| abstract_inverted_index.accurate | 23 |
| abstract_inverted_index.achieved | 112 |
| abstract_inverted_index.approach | 54, 235 |
| abstract_inverted_index.detector | 21, 32, 73, 79, 131, 169, 191, 204, 221, 252 |
| abstract_inverted_index.evaluate | 124 |
| abstract_inverted_index.identify | 171 |
| abstract_inverted_index.impaired | 4 |
| abstract_inverted_index.indicate | 95 |
| abstract_inverted_index.latency. | 149 |
| abstract_inverted_index.measures | 83 |
| abstract_inverted_index.modified | 106, 109, 129, 167, 189 |
| abstract_inverted_index.movement | 68 |
| abstract_inverted_index.patient, | 208 |
| abstract_inverted_index.patients | 172 |
| abstract_inverted_index.presence | 13 |
| abstract_inverted_index.present. | 230 |
| abstract_inverted_index.previous | 89 |
| abstract_inverted_index.proposes | 51 |
| abstract_inverted_index.requires | 11 |
| abstract_inverted_index.residual | 16, 40, 175, 216 |
| abstract_inverted_index.results, | 183 |
| abstract_inverted_index.severely | 3 |
| abstract_inverted_index.annotated | 121 |
| abstract_inverted_index.clinician | 120 |
| abstract_inverted_index.detection | 126, 141, 146 |
| abstract_inverted_index.detector, | 102 |
| abstract_inverted_index.different | 78 |
| abstract_inverted_index.employing | 132 |
| abstract_inverted_index.maximally | 71, 202 |
| abstract_inverted_index.patients, | 6 |
| abstract_inverted_index.threshold | 162 |
| abstract_inverted_index.EMG-driven | 8 |
| abstract_inverted_index.assistance | 225 |
| abstract_inverted_index.attempting | 66 |
| abstract_inverted_index.controlled | 91 |
| abstract_inverted_index.detection. | 27 |
| abstract_inverted_index.detectors, | 111 |
| abstract_inverted_index.especially | 35 |
| abstract_inverted_index.likelihood | 100 |
| abstract_inverted_index.randomized | 90 |
| abstract_inverted_index.separating | 72, 203 |
| abstract_inverted_index.separation | 82 |
| abstract_inverted_index.sufficient | 15, 174, 215, 227 |
| abstract_inverted_index.validation | 232 |
| abstract_inverted_index.warranted. | 254 |
| abstract_inverted_index.approximate | 98 |
| abstract_inverted_index.assistance. | 180 |
| abstract_inverted_index.distinguish | 56 |
| abstract_inverted_index.generalized | 99 |
| abstract_inverted_index.identifying | 29 |
| abstract_inverted_index.implemented | 248 |
| abstract_inverted_index.interaction | 247 |
| abstract_inverted_index.low-latency | 25 |
| abstract_inverted_index.performance | 142 |
| abstract_inverted_index.probability | 134, 159, 195 |
| abstract_inverted_index.separation. | 115 |
| abstract_inverted_index.challenging, | 34 |
| abstract_inverted_index.implementing | 7 |
| abstract_inverted_index.investigated | 76 |
| abstract_inverted_index.performance, | 127 |
| abstract_inverted_index.unsupervised | 53 |
| abstract_inverted_index.human-machine | 246 |
| abstract_inverted_index.investigating | 241 |
| abstract_inverted_index.participants, | 155 |
| abstract_inverted_index.difference-sum | 135, 160, 196 |
| abstract_inverted_index.robot-assisted | 9 |
| abstract_inverted_index.patient-specific | 20 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5071387980 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I172917736 |
| citation_normalized_percentile.value | 0.32862769 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |