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Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments

Autor
Sama, A.; Rodriguez-Martin, D.; Perez, C.; Catala, A.; Alcaine, S.; Mestre, B.; Prats, A.; Crespo, M. Cruz; Bayés, À.
Tipus d'activitat
Article en revista
Revista
Pattern recognition letters
Data de publicació
2017-05-17
Volum
105
Pàgina inicial
135
Pàgina final
143
DOI
https://doi.org/10.1016/j.patrec.2017.05.009 Obrir en finestra nova
Projecte finançador
Bloqueig en la malaltia de parkinson:millora de la qualitat de vida amb un sistema de control automàtic
Repositori
http://hdl.handle.net/2117/104840 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S0167865517301496 Obrir en finestra nova
Resum
Freezing of gait (FoG) is one of the most disturbing and incapacitating symptoms in Parkinson's disease. It is defined as a sudden block in effective stepping, provoking anxiety, stress and falls. FoG is usually evaluated by means of different questionnaires; however, this method has shown to be not reliable, since it is subjective due to its dependence on patients’ and caregivers’ judgment. Several authors have analyzed the usage of MEMS inertial systems to detect FoG with the aim of object...
Citació
Sama, A., Rodriguez-Martin, D., Perez, C., Catala, A., Alcaine, S., Mestre, B., Prats, A., Crespo, M. Cruz, Bayés, À. Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments. "Pattern recognition letters", 17 Maig 2017.
Paraules clau
Feature reduction, Freezing of gait, Machine learning, Parkinson's disease, Triaxial accelerometer
Grup de recerca
CETpD -Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma
ISSET - Integrated Smart Sensors and Health Technologies

Participants