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Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit

Author
Camps, J.; Sama, A.; Martin, M.; Rodriguez-Martin, D.; Perez, C.; Moreno, J.; Cabestany, J.; Catala, A.; Alcaine, S.; Mestre, B.; Prats, A.; Crespo, M. Cruz; Counihan, T.; Browne, P.; Quinlan, L.; ÓLaighin, G.; Sweeney, D.; Lewy, H.; Vainstein, G.; Costa, A.; Annicchiarico, R.; Bayés, À.; Rodríguez, A.
Type of activity
Journal article
Journal
Knowledge-based systems
Date of publication
2017-10-16
Volume
139
First page
119
Last page
131
DOI
https://doi.org/10.1016/j.knosys.2017.10.017 Open in new window
Project funding
Bloqueig en la malaltia de parkinson:millora de la qualitat de vida amb un sistema de control automàtic
Personal Health Device for the Remote and Autonomous Management of Parkinson's Disease
Repository
http://hdl.handle.net/2117/110570 Open in new window
https://www.researchgate.net/publication/320438480_Deep_learning_for_freezing_of_gait_detection_in_Parkinson's_disease_patients_in_their_homes_using_a_waist-worn_inertial_measurement_unit Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S0950705117304859 Open in new window
Abstract
Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients’ quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patient’s condition and the symptom’s characteristics, while it could enable non-pharmacologic support based on rhythmic cues. This paper is, to the best of our knowledge, the first study to propose a deep learning method for dete...
Citation
Camps, J., Sama, A., Martin, M., Rodriguez-Martin, D., Perez, C., Moreno, J., Cabestany, J., Catala, A., Alcaine, S., Mestre, B., Prats, A., Crespo, M. Cruz, Counihan, T., Browne, P., Quinlan, L., ÓLaighin, G., Sweeney, D., Lewy, H., Vainstein, G., Costa, A., Annicchiarico, R., Bayés, À., Rodríguez, A. Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit. "Knowledge-based systems", 16 Octubre 2017, vol. 139, p. 119-131.
Keywords
Deep learning, Freezing of gait, Parkinson’s disease, Signal processing, Wearable device
Group of research
CETpD - Technical Research Centre for Dependency Care and Autonomous Living
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
ISSET - Integrated Smart Sensors and Health Technologies
KEMLG - Knowledge Engineering and Machine Learning Group

Participants