Body and visual sensor fusion for motion analysis in Ubiquitous healthcare systems

M. ElSayed*, A. Alsebai, A. Salaheldin, N. El Gayar, M. ElHelw

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


Human motion analysis provides a valuable solution for monitoring the wellbeing 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.

Original languageEnglish
Title of host publication2010 International Conference on Body Sensor Networks
Number of pages5
ISBN (Print)9781424458172
Publication statusPublished - 8 Jul 2010
Event2010 International Conference on Body Sensor Networks - Singapore, Singapore
Duration: 7 Jun 20109 Jun 2010


Conference2010 International Conference on Body Sensor Networks
Abbreviated titleBSN 2010


  • Body sensor networks
  • Human motion analysis
  • Sensor fusion
  • Ubiquitous sensing
  • Visual sensor networks

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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