Authors:
Marta Bieńkiewicz
1
;
Philipp Gulde
1
;
Georg Goldenberg
2
and
Joachim Hermsdörfer
1
Affiliations:
1
Technische Universität München, Germany
;
2
Städtisches Klinikum München, Germany
Keyword(s):
Apraxia, Smoothness of Movement, Harmonicity, Kinematic Patterns, Stroke Rehabiliation.
Related
Ontology
Subjects/Areas/Topics:
Animation and Simulation
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Detection and Identification
;
Devices
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion Control
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Wearable Sensors and Systems
Abstract:
Due to the brain damage caused by stroke, apraxic patients suffer from tool use impairment, and sequencing actions during daily tasks (ADL). Patients fail to use tools in a purposeful manner, often adopting an inappropriate speed of the movement and a disrupted movement path (Laimgruber et al., 2005). The core of this symptom lies in the compromised ability to access the appropriate motor program relevant to the task goal (Hermsdörfer et al., 2006). Although many studies have explored kinematic and spatial features of apraxia both in object and non-object related motor tasks, there is a niche in the research to provide a spatiotemporal biomarker for this behaviour. We propose a novel approach based on dynamical systems framework (Bootsma et al., 2004), looking into the temporal and spatial components of movements. Preliminary data shows that this measure has a potential to encapsulate the disrupted motor behaviour in those patients. We put forward a circular-fit based model to quanti
fy deviations from the regular movement pattern. The application of this study is to create a measure of motor behaviour to be implemented in the autonomous assistance system (CogWatch) that could facilitate performance of ADL both in the clinical and home-based setting.
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