Wearable assistant for Parkinson's disease patients with the freezing of gait symptom

Authors:
Marc Bächlin;Meir Plotnik;Daniel Roggen;Inbal Maidan;Jeffrey M. Hausdorff;Nir Giladi;Gerhard Tröster
Affiliations:
Wearable Computing Laboratory, Swiss Federal Institute of Technology Zürich, Zürich, Switzerland;Laboratory for Gait and Neurodynamics, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel;Wearable Computing Laboratory, Swiss Federal Institute of Technology Zürich, Zürich, Switzerland;Laboratory for Gait and Neurodynamics, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel;Laboratory for Gait and Neurodynamics, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel and Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University and the Harvard Medic ...;Laboratory for Gait and Neurodynamics, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel;Wearable Computing Laboratory, Swiss Federal Institute of Technology Zürich, Zürich, Switzerland
Venue:
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Year:
2010

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Abstract

In this paper, we present a wearable assistant for Parkinson's disease (PD) patients with the freezing of gait (FOG) symptom. This wearable system uses on-body acceleration sensors to measure the patients' movements. It automatically detects FOG by analyzing frequency components inherent in these movements. When FOG is detected, the assistant provides a rhythmic auditory signal that stimulates the patient to resume walking. Ten PD patients tested the system while performing several walking tasks in the laboratory. More than 8 h of data were recorded. Eight patients experienced FOG during the study, and 237 FOG events were identified by professional physiotherapists in a post hoc video analysis. Our wearable assistant was able to provide online assistive feedback for PD patients when they experienced FOG. The system detected FOG events online with a sensitivity of 73.1% and a specificity of 81.6%. The majority of patients indicated that the context-aware automatic cueing was beneficial to them. Finally, we characterize the system performance with respect to the walking style, the sensor placement, and the dominant algorithm parameters.