%0 Journal Article
%T Supervised Expert System for Wearable MEMS Accelerometer-Based Fall Detector
%A Gabriele Rescio
%A Alessandro Leone
%A Pietro Siciliano
%J Journal of Sensors
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/254629
%X Falling is one of the main causes of trauma, disability, and death among older people. Inertial sensors-based devices are able to detect falls in controlled environments. Often this kind of solution presents poor performances 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 fall detection, by using a triaxial MEMS wearable wireless accelerometer. The proposed approach allows to generalize the detection of fall events in several practical conditions. It appears invariant to the age, weight, height of people, and to the relative positioning area (even in the upper part of the waist), overcoming the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated according to the specific features of the end user. In order to limit the workload, the specific study on posture analysis has been avoided, and a polynomial kernel function is used while maintaining high performances in terms of specificity and sensitivity. The supervised clustering step is achieved by implementing an one-class support vector machine classifier in a stand-alone PC. 1. Introduction The problem of falls in the elderly has become a health care priority due to the related high social and economic costs [1]. In fact the European population aged 65 years or more, which may be in need of assistance is increasing. This trend asks care-holders institutions to employ more efficient and optimized methods in order to be able to grant the required service at lower costs. The consequences of falls in the elderly may lead to psychological trauma, physical injuries, hospitalization, and even death in the worst scenario [2¨C5]. The main reason that pushed for the development of the presented system is to allow noncompletely self-sufficient people (e.g., older people) to live safely in their own houses as long as possible. This is important not only for aspects of health regarding assisted people, but also for the consequent social advantages. The European community issued and funded various projects and consortia. The mission focuses on several purposes, all addressed to older people, varying from the assistance in case of need, to the prevention of dangerous or unhealthy situations. The purpose of the work described in this paper is to focus on people fall detection. Many solutions have been proposed in the detection and prevention of falls, and some excellent review studies were presented [1, 6]. Basically,
%U http://www.hindawi.com/journals/js/2013/254629/