Body and Visual Sensor Fusion for Motion Analysis in Ubiquitous Healthcare Systems

Human motion analysis provides a valuable solution for monitoring the well-being of the elderly, quantifying post-operative patient recovery and monitoring the progression of neurodegenerative diseases such as Parkinson's. The development of accurate motion analysis models, however, requires th...

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Bibliographic Details
Published in:	2010 International Conference on Body Sensor Networks pp. 250 - 254
Main Authors:	ElSayed, Mohamed, Alsebai, Abubakr, Salaheldin, Ahmed, El Gayar, Neamat, ElHelw, Mohamed
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-06-2010
Subjects:	Biological system modeling body sensor networks Data analysis Data mining human motion analysis Humans Medical services Motion analysis Parkinson's disease Patient monitoring Senior citizens Sensor fusion ubiquitous sensing visual sensor networks
Online Access:	Get full text
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Summary:	Human motion analysis provides a valuable solution for monitoring the well-being of the elderly, quantifying post-operative patient recovery and monitoring the progression of neurodegenerative diseases such as Parkinson's. The development of accurate motion analysis models, however, requires the integration of multi-sensing modalities and the utilization of appropriate data analysis techniques. This paper describes a robust framework for improved patient motion analysis by integrating information captured by body and visual sensor networks. Real-time target extraction is applied and a skeletonization procedure is subsequently carried out to quantify the internal motion of moving target and compute two metrics, spatiotemporal cyclic motion between leg segments and head trajectory, for each vision node. Extracted motion metrics from multiple vision nodes and accelerometer information from a wearable body sensor are then fused at the feature level by using K-Nearest Neighbor algorithm and used to classify target's walking gait into normal or abnormal. The potential value of the proposed framework for patient monitoring is demonstrated and the results obtained from practical experiments are described.
ISBN:	142445817X 9781424458172
ISSN:	2376-8886
DOI:	10.1109/BSN.2010.38