Authors:
Julien Stamatakis
;
Adriana Gonzalez
;
Benoit Caby
;
Stephanie Lefebvre
;
Yves Vandermeeren
and
Benoit Macq
Affiliation:
Université catholique de Louvain, Belgium
Keyword(s):
Accelerometers, Codamotion, Kalman Filter, Kinematic Analysis, Stroke.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Devices
;
Health Information Systems
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Physiological Computing Systems
;
Wearable Sensors and Systems
Abstract:
Rehabilitation is an essential process to recover impaired motor functions after stroke. Typically, visual
marker-based systems such as the Codamotion are used, as kinematic analyses seem to be an excellent tool
to quantify objectively the effects of rehabilitation processes. However, this solution remains expensive. A low-cost accelerometer-based system has been developed and its performances were compared to those of the Codamotion system, used as a gold standard. Thanks to a model for prediction and an error model Kalman filter, the recorded signals were broken up into gravity and dynamic accelerations components that were placed in a global frame and compared to the Codamotion signals. The vertical z-axis was well reconstructed and used as a basis for kinematic analyses. Different features expressing movement speed, control strategy or movement smoothness have been computed from both systems and compared. Despite the fact that some of them showed differences between both systems,
the accelerometer-based system computed features with a discriminant power comparable to the ones derived from the Codamotion. In conclusion, this accelerometer-based system is a low-cost alternative to expensive visual marker-based systems that could be extensively used for rehabilitation processes in routine clinical practice or even at home.
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