3rd International ICST Conference on Body Area Networks

Research Article

Analysis of human performance using physiological data streams

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  • @INPROCEEDINGS{10.4108/ICST.BODYNETS2008.2937,
        author={Gaurav N. Pradhan and Balakrishnan Prabhakaran},
        title={Analysis of human performance using physiological data streams},
        proceedings={3rd International ICST Conference on Body Area Networks},
        publisher={ICST},
        proceedings_a={BODYNETS},
        year={2010},
        month={5},
        keywords={Motion capture electromyogram multi-dimensional factor analysis principal component analysis.},
        doi={10.4108/ICST.BODYNETS2008.2937}
    }
    
  • Gaurav N. Pradhan
    Balakrishnan Prabhakaran
    Year: 2010
    Analysis of human performance using physiological data streams
    BODYNETS
    ICST
    DOI: 10.4108/ICST.BODYNETS2008.2937
Gaurav N. Pradhan1,*, Balakrishnan Prabhakaran1,*
  • 1: University of Texas at Dallas, P.O. Box 75083 Richardson, Texas 75083
*Contact email: gaurav@utdallas.edu, praba@utdallas.edu

Abstract

Advancement in technology has led to measure the human performance using sophisticated multiple systems such as motion capture and physiological data monitoring systems. These systems together, represent the human activity in various physiologic and motoric streams that forms a multi-dimensional framework. The immediate requirement that rises is, analyzing these data streams to quantify the human performance. In this paper, we have proposed an efficient, multi-dimensional factor analysis technique that quantifies the multiple observations of data streams across different participants. In our approach, we extract characteristic parameters from the streams and conduct a separate global analysis on the data sets of each stream. The individual data sets are then projected onto the respective global analysis to analyze the differences in the responses of the participants. Next, we integrate these global analysis spaces of all streams, to get a compromise structure that represents the aggregate effect of all streams on the performance of each participant.