Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study
Abstract Background Myoelectric pattern recognition (MPR) combines multiple surface electromyography channels with a machine learning algorithm to decode motor intention with an aim to enhance upper limb function after stroke. This study aims to determine the feasibility and preliminary effectivenes...
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BMC
2025-01-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
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| Online Access: | https://doi.org/10.1186/s12984-025-01541-y |
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| author | Maria Munoz-Novoa Morten B. Kristoffersen Katharina S. Sunnerhagen Autumn Naber Max Ortiz-Catalan Margit Alt Murphy |
| author_facet | Maria Munoz-Novoa Morten B. Kristoffersen Katharina S. Sunnerhagen Autumn Naber Max Ortiz-Catalan Margit Alt Murphy |
| author_sort | Maria Munoz-Novoa |
| collection | DOAJ |
| description | Abstract Background Myoelectric pattern recognition (MPR) combines multiple surface electromyography channels with a machine learning algorithm to decode motor intention with an aim to enhance upper limb function after stroke. This study aims to determine the feasibility and preliminary effectiveness of a novel intervention combining MPR, virtual reality (VR), and serious gaming to improve upper limb function in people with chronic stroke. Methods In this single case experimental A-B-A design study, six individuals with chronic stroke and moderate to severe upper limb impairment completed 18, 2 h sessions, 3 times a week. Repeated assessments were performed using the Fugl-Meyer Assessment of Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), grip strength, and kinematics of the drinking task at baseline, during, and post intervention. The results were analyzed by using visual analysis and Tau-U statistics. Results All participants improved upper limb function assessed by FMA-UE (Tau-U 0.72–1.0), and five out of six improved beyond the minimal clinical important difference (MCID). Four participants improved ARAT and grip strength scores (Tau-U 0.84–1.0), with one reaching the MCID for ARAT. Three out of four participants in the kinematic analysis achieved improvements beyond the MCID in movement time and smoothness, two with a Tau-U > 0.90, and two participants improved trunk displacement beyond the MCID (Tau-U 0.68). Most participants showed some deterioration in the follow-up phase. Conclusions MPR combined with VR and serious gaming is a feasible and promising intervention for improving upper limb function in people with chronic stroke. Trial Registration: ClinicalTrials.gov, reference number NCT04154371. |
| format | Article |
| id | doaj-art-2417df99a2f344059fd87b91aa5d523f |
| institution | Kabale University |
| issn | 1743-0003 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj-art-2417df99a2f344059fd87b91aa5d523f2025-01-19T12:12:53ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032025-01-0122111210.1186/s12984-025-01541-yMyoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design studyMaria Munoz-Novoa0Morten B. Kristoffersen1Katharina S. Sunnerhagen2Autumn Naber3Max Ortiz-Catalan4Margit Alt Murphy5Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgCenter for Bionics and Pain ResearchDepartment of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgCenter for Bionics and Pain ResearchCenter for Bionics and Pain ResearchDepartment of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgAbstract Background Myoelectric pattern recognition (MPR) combines multiple surface electromyography channels with a machine learning algorithm to decode motor intention with an aim to enhance upper limb function after stroke. This study aims to determine the feasibility and preliminary effectiveness of a novel intervention combining MPR, virtual reality (VR), and serious gaming to improve upper limb function in people with chronic stroke. Methods In this single case experimental A-B-A design study, six individuals with chronic stroke and moderate to severe upper limb impairment completed 18, 2 h sessions, 3 times a week. Repeated assessments were performed using the Fugl-Meyer Assessment of Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), grip strength, and kinematics of the drinking task at baseline, during, and post intervention. The results were analyzed by using visual analysis and Tau-U statistics. Results All participants improved upper limb function assessed by FMA-UE (Tau-U 0.72–1.0), and five out of six improved beyond the minimal clinical important difference (MCID). Four participants improved ARAT and grip strength scores (Tau-U 0.84–1.0), with one reaching the MCID for ARAT. Three out of four participants in the kinematic analysis achieved improvements beyond the MCID in movement time and smoothness, two with a Tau-U > 0.90, and two participants improved trunk displacement beyond the MCID (Tau-U 0.68). Most participants showed some deterioration in the follow-up phase. Conclusions MPR combined with VR and serious gaming is a feasible and promising intervention for improving upper limb function in people with chronic stroke. Trial Registration: ClinicalTrials.gov, reference number NCT04154371.https://doi.org/10.1186/s12984-025-01541-yStrokeUpper limb functionElectromyographyVirtual realityRehabilitationMyoelectric pattern recognition |
| spellingShingle | Maria Munoz-Novoa Morten B. Kristoffersen Katharina S. Sunnerhagen Autumn Naber Max Ortiz-Catalan Margit Alt Murphy Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study Journal of NeuroEngineering and Rehabilitation Stroke Upper limb function Electromyography Virtual reality Rehabilitation Myoelectric pattern recognition |
| title | Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study |
| title_full | Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study |
| title_fullStr | Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study |
| title_full_unstemmed | Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study |
| title_short | Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: a single case experimental design study |
| title_sort | myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke a single case experimental design study |
| topic | Stroke Upper limb function Electromyography Virtual reality Rehabilitation Myoelectric pattern recognition |
| url | https://doi.org/10.1186/s12984-025-01541-y |
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