Indexing of variable length multi-attribute motion data
Proceedings of the 2nd ACM international workshop on Multimedia databases
End-to-end QoS framework for heterogeneous wired-cum-wireless networks
Wireless Networks
Middleware for streaming 3D progressive meshes over lossy networks
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Segmentation and recognition of motion streams by similarity search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Partial query resolution for animation authoring
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments
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
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Recently, Body Sensor Networks (BSNs) are being deployed for monitoring and managing medical conditions as well as human performance in sports. These BSNs include various sensors such as accelerometers, gyroscopes, EMG (Electromyogram), EKG (Electro-cardiograms), and other sensors depending on the needs of the medical conditions. Data from these sensors are typically Time Series data and the data from multiple sensors form multiple, multidimensional time series data. Analyzing data from such multiple medical sensors pose several challenges: different sensors have different characteristics, different people generate different patterns through these sensors, and even for the same person the data can vary widely depending on time and environment. This tutorial describes the technologies that go behind BSNs - both in terms of the hardware infrastructure as well as the basic software. First, we outline the BSN hardware features and the related requirements. We then discuss the energy and communication choices for BSNs. Next, we discuss approaches for classification, data mining, visualization, and securing these data. We also show several demonstrations of body sensor networks as well as the software that aid in analyzing the data.