Discovery of Gait Anomalies from Motion Sensor Data

A tool for discovery of gait anomalies of elderly from motion sensor data is proposed. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting...

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Bibliographic Details
Main Authors: Pogorelc, B, Gams, M
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Artificial neural networks
Classification algorithms
data mining
gait analysis
Legged locomotion
motion recognition
Noise
Senior citizens
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Description
Summary:A tool for discovery of gait anomalies of elderly from motion sensor data is proposed. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with dynamic time warping and machine learning algorithms in order to identify the specific gait anomaly. We designed medically oriented features for training a machine learning classifier that classifies the user's gait into: i) normal, ii) with hemiplegia, iii) with Parkinson's disease, iv) with pain in the back and v) with pain in the leg. Experimental results show that the proposed tool is usable for discovery of gait anomalies.
ISSN:1082-3409
2375-0197
DOI:10.1109/ICTAI.2010.119