Hindawi Publishing Corporation Journal of Sensors Volume 2013, Article ID 254629, 11 pages http://dx.doi.org/10.1155/2013/254629 Research Article Supervised Expert System for Wearable MEMS Accelerometer-Based Fall Detector Gabriele Rescio, Alessandro Leone, and Pietro Siciliano Institute for Microelectronics and Microsystems, Italian National Research Council (CNR), Via Monteroni, c/o Campus Universit` a del Salento, Palazzina A3, 73100 Lecce, Italy Correspondence should be addressed to Gabriele Rescio; gabriele.rescio@le.imm.cnr.it Received 8 February 2013; Revised 10 June 2013; Accepted 11 June 2013 Academic Editor: Andrea Cusano Copyright © 2013 Gabriele Rescio et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. Oten this kind of solution presents poor performances in real conditions. he 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. he 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 speciic features of the end user. In order to limit the workload, the speciic study on posture analysis has been avoided, and a polynomial kernel function is used while maintaining high performances in terms of speciicity and sensitivity. he supervised clustering step is achieved by implementing an one-class support vector machine classiier in a stand-alone PC. 1. Introduction he 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. his trend asks care-holders institutions to employ more eicient and optimized methods in order to be able to grant the required service at lower costs. he consequences of falls in the elderly may lead to psychological trauma, physical injuries, hospi- talization, and even death in the worst scenario [2–5]. he main reason that pushed for the development of the presented system is to allow noncompletely self-suicient people (e.g., older people) to live safely in their own houses as long as possible. his is important not only for aspects of health regarding assisted people, but also for the consequent social advantages. he European community issued and funded various projects and consortia. he mission focuses on sev- eral purposes, all addressed to older people, varying from the assistance in case of need, to the prevention of dangerous or unhealthy situations. he 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, fall-detection solutions can be classiied in three main classes: wearable devices, ambi- ent devices, and camera-based devices. he irst approach requires that the elderly holds some kind of devices (e.g., an assistive cane) or wears sensors like accelerometers and/or gyroscopes to detect the motion of the body. In partic- ular, recent miniaturization and cost reduction of MEMS accelerometers and the availability of reliable wireless com- munication technologies enabled the realization of afordable wearable monitoring systems that can be worn by people performing their normal daily activities [7–10]. For these reasons, in the last few years, the use of portable devices in the health monitoring of chronic patients has increased con- siderably. However, these devices have some drawbacks: they are prone to be forgotten, worn in a wrong body position, or accidentally damaged. Regarding fall detection, with respect to vision or acoustic sensors, the accelerometer module has the advantage of not having to be set up and installed in all rooms of the “smart home,” as it is required for instance for 3D video trackers or acoustic scene analyzers. On the other hand,