Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/34021
Título: | A personalized rehabilitation system based on wireless motion capture sensors |
Autor(es): | Macedo, Pedro Afonso, José A. Simões, Ricardo |
Palavras-chave: | Motion capture Physiotherapy Wireless sensor networks Wearable sensors |
Data: | 2015 |
Editora: | SCITEPRESS – Science and Technology Publications |
Citação: | Macedo, P., Afonso, J. A., & Simoes, R. (2015). A personalized rehabilitation system based on wireless motion capture sensors. Paper presented at the SENSORNETS 2015 - 4th International Conference on Sensor Networks, Proceedings. |
Resumo(s): | We live in an aging society, an issue that will be exacerbated in the coming decades, due to low birth rates and increasing life expectancy. With the decline in physical and cognitive functions with age, it is of the utmost importance to maintain regular physical activity,in order to preserve an individual’s mobility, motor capabilities and coordination. Within this context, thispaper describes the development of a wireless sensor network and its application in a human motion capturesystem based on wearable inertial and magnetic sensors. The goal is to enable, through continuous real-time monitoring, the creation of a personalized home-based rehabilitation system for the elderly population and/or injured people. Within this system, the user can benefit from an assisted mode, in which their movements can be compared to a reference motion model of the same movements, resulting in visual feedback alerts given by the application. This motion model can be created previously, in a ‘learning phase’, under supervision of a caregiver. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/34021 |
ISBN: | 9789897580864 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | DEI - Artigos em atas de congressos internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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SENSORNETS2015-p.pdf | 676,81 kB | Adobe PDF | Ver/Abrir |