Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals
Abstract Background Assistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12984-023-01222-8 |
| _version_ | 1797453656742690816 |
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| author | Jessica Gantenbein Chakaveh Ahmadizadeh Oliver Heeb Olivier Lambercy Carlo Menon |
| author_facet | Jessica Gantenbein Chakaveh Ahmadizadeh Oliver Heeb Olivier Lambercy Carlo Menon |
| author_sort | Jessica Gantenbein |
| collection | DOAJ |
| description | Abstract Background Assistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to detect their movement intention for device control is crucial. Gesture recognition based on force myography measuring volumetric changes in the muscles during contraction has been previously shown to be a viable and easy to implement strategy to control hand prostheses. Whether this approach could be efficiently applied to intuitively control an assistive robotic hand orthosis remains to be investigated. Methods In this work, we assessed the feasibility of using force myography measured from the forearm to control a robotic hand orthosis worn on the hand ipsilateral to the measurement site. In ten neurologically-intact participants wearing a robotic hand orthosis, we collected data for four gestures trained in nine arm configurations, i.e., seven static positions and two dynamic movements, corresponding to typical activities of daily living conditions. In an offline analysis, we determined classification accuracies for two binary classifiers (one for opening and one for closing) and further assessed the impact of individual training arm configurations on the overall performance. Results We achieved an overall classification accuracy of 92.9% (averaged over two binary classifiers, individual accuracies 95.5% and 90.3%, respectively) but found a large variation in performance between participants, ranging from 75.4 up to 100%. Averaged inference times per sample were measured below 0.15 ms. Further, we found that the number of training arm configurations could be reduced from nine to six without notably decreasing classification performance. Conclusion The results of this work support the general feasibility of using force myography as an intuitive intention detection strategy for a robotic hand orthosis. Further, the findings also generated valuable insights into challenges and potential ways to overcome them in view of applying such technologies for assisting people with sensorimotor hand impairment during activities of daily living. |
| first_indexed | 2024-03-09T15:25:06Z |
| format | Article |
| id | doaj.art-28724b88f079407aa9217372a48fc195 |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-03-09T15:25:06Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-28724b88f079407aa9217372a48fc1952023-11-26T12:32:33ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032023-08-0120111310.1186/s12984-023-01222-8Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individualsJessica Gantenbein0Chakaveh Ahmadizadeh1Oliver Heeb2Olivier Lambercy3Carlo Menon4Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH ZurichBiomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH ZurichRehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH ZurichRehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH ZurichBiomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH ZurichAbstract Background Assistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to detect their movement intention for device control is crucial. Gesture recognition based on force myography measuring volumetric changes in the muscles during contraction has been previously shown to be a viable and easy to implement strategy to control hand prostheses. Whether this approach could be efficiently applied to intuitively control an assistive robotic hand orthosis remains to be investigated. Methods In this work, we assessed the feasibility of using force myography measured from the forearm to control a robotic hand orthosis worn on the hand ipsilateral to the measurement site. In ten neurologically-intact participants wearing a robotic hand orthosis, we collected data for four gestures trained in nine arm configurations, i.e., seven static positions and two dynamic movements, corresponding to typical activities of daily living conditions. In an offline analysis, we determined classification accuracies for two binary classifiers (one for opening and one for closing) and further assessed the impact of individual training arm configurations on the overall performance. Results We achieved an overall classification accuracy of 92.9% (averaged over two binary classifiers, individual accuracies 95.5% and 90.3%, respectively) but found a large variation in performance between participants, ranging from 75.4 up to 100%. Averaged inference times per sample were measured below 0.15 ms. Further, we found that the number of training arm configurations could be reduced from nine to six without notably decreasing classification performance. Conclusion The results of this work support the general feasibility of using force myography as an intuitive intention detection strategy for a robotic hand orthosis. Further, the findings also generated valuable insights into challenges and potential ways to overcome them in view of applying such technologies for assisting people with sensorimotor hand impairment during activities of daily living.https://doi.org/10.1186/s12984-023-01222-8Force myographyRobotic hand orthosisGesture recognitionHuman–machine interfacesIntention detection |
| spellingShingle | Jessica Gantenbein Chakaveh Ahmadizadeh Oliver Heeb Olivier Lambercy Carlo Menon Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals Journal of NeuroEngineering and Rehabilitation Force myography Robotic hand orthosis Gesture recognition Human–machine interfaces Intention detection |
| title | Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals |
| title_full | Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals |
| title_fullStr | Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals |
| title_full_unstemmed | Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals |
| title_short | Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals |
| title_sort | feasibility of force myography for the direct control of an assistive robotic hand orthosis in non impaired individuals |
| topic | Force myography Robotic hand orthosis Gesture recognition Human–machine interfaces Intention detection |
| url | https://doi.org/10.1186/s12984-023-01222-8 |
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