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Falling down events can cause trauma, disability and death among older people. Accelerometer-based devices are able to detect falls in controlled environments. This kind of solution often presents poor performance in real conditions. The aim of this work is the development of a computationally low-cost algorithm for feature extraction and the implementation of a Machine Learning scheme for people...
The aim of this work is the development of a computationally low-cost scheme for feature extraction and the implementation of an One-class Support Vector Machine classifier for people fall detection, by using a tri-axial MEMS wearable wireless accelerometer, managed by a stand-alone PC through ZigBee connection. The proposed approach allows the generalization of the detection of fall events in several...
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