Providing user context for mobile and social networking applications
Pervasive and Mobile Computing
IEEE Transactions on Information Technology in Biomedicine
Preprocessing techniques for context recognition from accelerometer data
Personal and Ubiquitous Computing
Grammar-based, posture- and context-cognitive detection for falls with different activity levels
Proceedings of the 2nd Conference on Wireless Health
Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
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The real-time monitoring about the activity of the human provides useful information about the activity quantity and ability. The present study implemented a small-size and low-power acceleration monitoring system for convenient monitoring of activity volume and recognition of emergent situations such as falling during daily life. For the wireless transmission of acceleration sensor signal, we developed a wireless transmission system based on a sensor network. In addition, we developed a program for storing and monitoring wirelessly transmitted signals on PC in real-time. The performance of the implemented system was evaluated by assessing the output characteristic of the system according to the change of posture, and parameters and a context recognition algorithm were developed in order to monitor activity volume during daily life and to recognize emergent situations such as falling. In particular, recognition error in the sudden change of acceleration was minimized by the application of a falling correction algorithm.