Accelerometer sensor module and fall detection monitoring system based on wireless sensor network for e-health applications.

ABSTRACT

This research implements a wireless accelerometer sensor module and an algorithm to determine an individual's posture, activity, and fall. An analog wireless, two-axis accelerometer sensor module was used. Using a wireless radiofrequency module, the accelerometer signal is displayed on a PC-based "Acceloger" (e-Digitalmed, Seoul, Korea) viewer program. The activities of daily living algorithm uses the AC components of the accelerometer signal to determine posture, activity, and fall, and posture while standing, sitting, lying, walking, running, and so on is determined by the DC component. The performance of 30 subjects was evaluated by assessing the algorithm and calculating the detection rate for postures, motions, and subjects. Using a wireless sensor network in an experimental space, subject's postures, motions, and fall monitoring system was implemented. In conclusion, this system can be applied to patients such as the elderly for activity monitoring and fall detection and also for sports athlete's exercise measurement and pattern analysis.