Authors:
Luca Palmerini
1
;
Laura Rocchi
1
;
Jeffrey M. Hausdorff
2
and
Lorenzo Chiari
1
Affiliations:
1
University of Bologna, Italy
;
2
Tel-Aviv Sourasky Medical Center, Israel
Keyword(s):
Freezing, Parkinson’s Disease, Symbolic Aggregate Approximation, Acceleration, Wearable Sensors.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Computer Vision, Visualization and Computer Graphics
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning of Action Patterns
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Signal Processing
;
Software Engineering
Abstract:
Freezing of gait (FOG) is a common and disabling gait disturbance among patients with advanced Parkinson’s Disease (PD). FOG episodes are often overcome using attention or cues from the environment. Hence, identification of events prior to FOG may be very effective to improve mobility in PD patients. Previous work has suggested that there are changes in the gait pattern just prior to freezing. Nonetheless, little work has been done to explore the possibility of identifying motor patterns that are characteristic of the pre-FOG phase (few seconds before the FOG). We analysed the acceleration signals from sensors worn on the ankle, thigh, and trunk of eight patients with PD who experienced freezing. We translated windows of the raw signals in symbols by using Symbolic Aggregate approXimation. The aim was to discriminate the patterns of symbols characterizing pre-FOG from the ones characterizing normal activity (standing and walking with no FOG). Sensitivity over 50% and Specificity over
70% were obtained by using a classifier on symbolic data, with different combinations of sensor position/sampling/windows duration. These preliminary findings demonstrate that it is possible to automatically identify (some of) the motor patterns that eventually lead to FOG events before they occur by using wearable sensors.
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