Computational Statistics & Data Analysis - Special issue on multiway data analysis—software and applications
Multiple factor analysis (AFMULT package)
Computational Statistics & Data Analysis - Special issue on multiway data analysis—software and applications
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
3D motion retrieval with motion index tree
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
A Multilevel Distance-Based Index Structure for Multivariate Time Series
TIME '05 Proceedings of the 12th International Symposium on Temporal Representation and Reasoning
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
Indexing large human-motion databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Analysis of human performance using physiological data streams
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Integration of Motion Capture and EMG data for Classifying the Human Motions
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Content-based retrieval for human motion data
Journal of Visual Communication and Image Representation
Hierarchical indexing structure for 3d human motions
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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Advancement in technology has led to the deployment of body sensor networks (BSN) to monitor and sense human activity in pervasive environments. Using multiple wireless on-body systems, such as physiological data monitoring and motion capture systems, body sensor network data consists of heterogeneous physiologic and motoric streams that form a multidimensional framework. In this article, we analyze such high-dimensional body sensor network data by proposing an efficient, multidimensional factor analysis technique for quantifying human performance and, at the same time, providing visualization for performances of participants in a low-dimensional space for easier interpretation.