Exploring physiological signals on people with Duchenne muscular dystrophy for an active trunk support: a case study
Autor: | Stergios Verros, Arjen Bergsma, Bart F.J.M. Koopman, Laura H. C. Peeters, Edsko E.G. Hekman, Gijsbertus Jacob Verkerke |
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Přispěvatelé: | Personalized Healthcare Technology (PHT), Extremities Pain and Disability (EXPAND) |
Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
musculoskeletal diseases
Duchenne muscular dystrophy Cultural Studies Linguistics and Language History medicine.medical_specialty lcsh:Medical technology Control interface lcsh:Biotechnology Trunk support Interface (computing) 0206 medical engineering 02 engineering and technology Electromyography Language and Linguistics 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation lcsh:TP248.13-248.65 Joystick Medicine medicine.diagnostic_test business.industry medicine.disease 020601 biomedical engineering Trunk lcsh:R855-855.5 Anthropology business 030217 neurology & neurosurgery Research Article |
Zdroj: | Bio-Medical Engineering, 1(1):31. Springer BMC Biomedical Engineering BMC Biomedical Engineering, Vol 1, Iss 1, Pp 1-7 (2019) Nature biomedical engineering, 1(1):31. Nature Publishing Group |
ISSN: | 2157-846X 0006-3398 |
DOI: | 10.1186/s42490-019-0032-x |
Popis: | Background Arm support devices are available to support people with Duchenne muscular dystrophy (DMD), but active trunk support devices are lacking. An active trunk support device can potentially extend the reach of the arm and stabilize the unstable trunk of people with DMD. In a previous study, we showed that healthy people were able to control an active trunk support using four different control interfaces (based on joystick, force on feet, force on sternum and surface electromyography). All four control interfaces had different advantages and disadvantages. The aim of this study was to explore which of the four inputs is detectably used by people with DMD to control an active trunk support. Results The results were subject-dependent in both experiments. In the active experiment, the joystick was the most promising control interface. Regarding the static experiment, surface electromyography and force on feet worked for two out of the three subjects. Conclusions To our knowledge, this is the first time that people with DMD have engaged in a control task using signals other than those related to their arm muscles. According to our findings, the control interfaces have to be customised to every DMD subject. |
Databáze: | OpenAIRE |
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