Surface electromyography for improving upper limb function after stroke - Development of new rehabilitation tools
Abstract
Background: Upper limb impairment is a major consequence after stroke, significantly affecting independence and quality of life. Various rehabilitation technologies have been developed to improve upper limb function, including surface electromyography (sEMG).
Aim: This thesis explores the potential of sEMG-driven interventions to enhance upper limb function after stroke, employing qualitative and quantitative approaches.
Methods: Study I summarized the current evidence on sEMG-driven interventions for upper limb rehabilitation after stroke through a systematic review and meta-analysis, investigating the effects of different types of sEMG-based therapies. Study II introduced a novel intervention that combined myoelectric pattern recognition (MPR) , virtual reality (VR), and serious gaming. This intervention was evaluated using a single-case experimental design to assess its feasibility and preliminary effectiveness in improving upper limb function in individuals with chronic stroke. Study III employed a qualitative approach to explore the experiences and perceptions of participants involved in Study II. Lastly, Study IV used a co-design approach involving individuals with stroke, clinicians and rehabilitation experts to refine and assess the usability of a training tool that uses a smart textile sleeve sEMG biofeedback system designed for self-administered upper limb rehabilitation.
Results: In Study I, 24 studies (n = 808) were included. Twenty of these studies reported significant improvements in upper limb function following sEMG interventions; however, the meta-analysis revealed no significant differences in overall effect between sEMG and non-sEMG interventions (14 studies), nor among different types of sEMG interventions (7 studies). In Study II (n = 6), all participants showed improvement on motor function, with five exceeding the minimal clinically important difference. Additionally, four participants demonstrated improvements in activity capacity and grip strength, with three out of four kinematic assessments conducted showing clinically meaningful results. Study III revealed that participants valued the intervention, found it unique and meaningful, and reported increased awareness and use of their paretic arm in daily life activities. They noted that “many factors come into play to make it work,” including their own role, the training system, and support from therapists. Study IV (n = 19) found that individuals with stroke and clinicians considered the smart textile sEMG biofeedback tool easy to use and a promising solution for self-administered upper limb rehabilitation at home, while also providing valuable feedback for future improvements.
Conclusion: sEMG-driven interventions show promising results as complementary tools for improving upper limb function after stroke. While the overall effect compared to conventional therapies remains inconclusive, significant improvements were observed, particularly among individuals with severe impairments in the chronic phase. Participants across studies reported positive experiences with novel sEMG technologies that offer real-time visual feedback and recognized the potential of these tools to support focused arm training and self-administered rehabilitation at home. Furthermore, user-centered design and interdisciplinary collaboration added value to the development of these technologies and strengthened the findings. Further research is needed to assess the efficacy of sEMG-driven interventions in improving upper limb function after stroke and to explore their long-term outcomes. Efforts should focus on enhancing usability for home-based applications to support broader access to evidence-based rehabilitation for individuals living with the long-term consequences after stroke.
Parts of work
I. Munoz-Novoa M, Kristoffersen M, Sunnerhagen KS, Naber A, Alt Murphy M, Ortiz-Catalan M. Upper limb stroke rehabilitation using surface electromyography: a systematic review and meta-analysis. Front Hum Neurosci. 2022 May 20;16:897870.https://doi.org/10.3389/fnhum.2022.897870 II. Munoz-Novoa M, Kristoffersen M, Sunnerhagen KS, Naber A, Ortiz-Catalan M, Alt Murphy M. Myoelectric pattern recognition with virtual reality and serious gaming improves upper limb function in chronic stroke: A single case experimental design study. J Neuroeng Rehabil. 2025 Jan 17;22(1):6. https://doi.org/10.1186/s12984-025-01541-y III. Munoz-Novoa M, Andersson C, Sunnerhagen KS, Alt Murphy M. A novel intervention for upper limb rehabilitation in people with stroke combining myoelectric pattern recognition, virtual reality, and serious gaming: a qualitative study. Disabil Rehabil. 2024 Dec 3:1-8. https://doi.org/10.1080/09638288.2024.2434643 IV. Munoz-Novoa M, Guo L, Björkquist A, Kristoffersen M, Khorramshahi P, Sandsjö L, Alt Murphy M. A smart textile biofeedback training tool for upper limb rehabilitation after stroke – a co-design development and evaluation. Manuscript.
Degree
Doctor of Philosophy (Medicine)
University
University of Gothenburg. Sahlgrenska Academy
Institution
Institute of Neuroscience and Physiology. Department of Clinical Neuroscience
Disputation
Fredagen den 13 juni, kl. 9.00, sal 2119, Hälsovetarbacken, Hus 2, Arvid Wallgrens backe, Göteborg
Date of defence
2025-06-13
maria.munoz.novoa@neuro.gu.se
Date
2025-05-15Author
Muñoz Novoa, María José
Keywords
Stroke
upper limb function
electromyography
rehabilitation
feedback
myoelectric pattern recognition
smart textiles
Publication type
Doctoral thesis
ISBN
978-91-8115-190-9 (PRINT)
978-91-8115-191-6 (PDF)
Language
eng