id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_isdelkci4ravthy6kted5tfk7a	Gabriele Rescio	Supervised Expert System for Wearable MEMS Accelerometer-Based Fall Detector	2013	12	.pdf	application/pdf	6877	940	70	this work is the development of a computationally low-cost algorithm for feature extraction and the implementation of a machinelearning scheme for people fall detection, by using a triaxial MEMS wearable wireless accelerometer. approaches in which several parameters need to be manually estimated according to the specific features of the end user. to limit the workload, the specific study on posture analysis has been avoided, and a polynomial kernel function is used while The waveform in Figure 4 represents a typical acceleration signal of a forward fall on three axes and all phases are the fall events are detected by a one-class support vector Figure 11: Features extracted of falls and ADLs simulated wearing the device on the waist. Figure 12: Features extracted of falls and ADLs simulated wearing the device on the chest. threshold-based tri-axial accelerometer fall detection algorithm," Gait & Posture, vol.	./cache/work_isdelkci4ravthy6kted5tfk7a.pdf	./txt/work_isdelkci4ravthy6kted5tfk7a.txt