Storage, retrieval, and communication of body sensor network data
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Multimedia aspects in health care
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Clustering of human motions based on feature-level fusion of multiple body sensor data
Proceedings of the 1st ACM International Health Informatics Symposium
Analyzing and Visualizing Jump Performance Using Wireless Body Sensors
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on CAPA'09, Special Section on WHS'09, and Special Section VCPSS' 09
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Three dimensional motion capture facility is a powerful tool for quantitative and qualitative assessment of multi-joint external movements. Electro-myograph (EMG) signals give the physiologic information of muscles while doing motions. In this paper, our objective is to integrate these two different bio-medical data together and to extract precise and accurate feature information for classifying the human motions. When both forms of data are integrated and analyzed together, the information achieved will be immensely useful to quantify the complex human motions for medical reasons or sport performances. These biological quantifications of biomechanical data, are useful for gait analysis and several orthopedic applications, such as joint mechanics, prosthetic designs, and sports medicines. The different dimensionality reduction approaches such Integral of Absolute value and Weighted Singular Value Decomposition are used to extract the preliminary features from EMG and motion capture data respectively. On combining these feature vectors, fuzzy clustering such as Fuzzy c-means (FCM) is performed on these vectors that are mapped as the points in multi-dimensional feature space. We get the degree of memberships with every cluster for each mapped point. This extracted information is used as the final feature vectors for classifying the human motions.