Publicação
Decision support system for NMES treatments : a solution design
| Resumo: | The preservation of functional capacity in old age is associated with a more active and dignified life. Maintaining this capacity is not a trivial task; several diseases can make it difficult to practice regular physical exercises or make it unfeasible, such as knee osteoarthritis. Neuromuscular electrical stimulation (NMES) is a treatment for muscle rehabilitation positioned as an alternative. However, it is still unclear which electrostimulation configurations can produce the most effective treatment. The literature indicates that the difference in treatment depends on the intrinsic characteristics of the patients. In this scenario, a decision support system is designed to assist physiotherapists in data analyses to create a personalized treatment. The proposed treatment relies on a wearable system with an NMES actuator and biofeedback sensors. Thus, it is expected to adapt the NMES in real-time based on unique patient characteristics, such as muscle fatigue. In addition, the system architecture is designed for the treatment session to be carried out at the patient's home, reducing costs and providing more comfort. |
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| Autores principais: | Franco, Tiago |
| Assunto: | Decision support system NMES Muscle rehabilitation Artificial intelligence Wearable sensors |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | The preservation of functional capacity in old age is associated with a more active and dignified life. Maintaining this capacity is not a trivial task; several diseases can make it difficult to practice regular physical exercises or make it unfeasible, such as knee osteoarthritis. Neuromuscular electrical stimulation (NMES) is a treatment for muscle rehabilitation positioned as an alternative. However, it is still unclear which electrostimulation configurations can produce the most effective treatment. The literature indicates that the difference in treatment depends on the intrinsic characteristics of the patients. In this scenario, a decision support system is designed to assist physiotherapists in data analyses to create a personalized treatment. The proposed treatment relies on a wearable system with an NMES actuator and biofeedback sensors. Thus, it is expected to adapt the NMES in real-time based on unique patient characteristics, such as muscle fatigue. In addition, the system architecture is designed for the treatment session to be carried out at the patient's home, reducing costs and providing more comfort. |
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