A Hybrid Algorithm for Fall Detection Serkan TURKELI a,e, 1 , Fikri ELMAS b , Huseyin Tanzer ATAY b,e , Kenan Kaan KURT c,e ,  d,e a Control and Automation Engineering Department, Istanbul Technical University, Turkey b Biomedical Engineering Graduate Program, Istanbul Technical University, Turkey c  Istanbul, Turkey d Electric Engineering Graduate Program, Istanbul Technical University, Turkey e Tesodev, Istanbul, Turkey Abstract. Falling is a major problem among the people globally, frequently due to some health problems including vision loss or balance disorder as a consequence of aging. As a result of the falling in elder people; injuries, complications, neurological problems and mortality are generally occurred. This situation also affects the patients’ families psychologically. A large number of studies show that significant fractures, injuries and in some cases death are commonly encountered in elderly. Nonetheless, in the immediate intervention, the rate of damage is reduced and the life quality can be significantly restored. The purpose of this paper is to reduce fall-related problems by developing a new fall detector that we called Tesodev fall detector. In addition, a proper algorithm and a wearable electronic device attaching the sensor to patient’s clothes are provided. The electronic device is an IOT device which can send and receive data wirelessly, which means that the device can inform the medical centres to improve medical attention time. Also, this device is modular and device allows keeping other medical data such as blood sugar, tension, heart rate, SPO 2 . Sensitivity, error rate and classification accuracy in this study are 89.8%, 23.4%, 76%, respectively. Keywords. IOT, Geriatric Patients, Prototype, Fall Detector Introduction The world is currently facing a population ageing problem [1]. In future this problem will become more serious: According to WHO, “The oldest segment of population, aged 80 and over, particularly prone to falls and its consequences, is the fastest growing within older population expected to represent 20% of the older population by 2050” [1]. There is another report of WHO named Global Report on Falls Prevention in Older Age, which is stating that the second leading cause of accidental injury deaths is falls. 424 000 people died from falling, 80% of them are living in middle or low income countries [2]. 10%-25% of elderly fallen patients have numerous hip and radius fractures or soft tissue damages. There are important factors to fall such as muscle weakness, balance problems, dizziness or vertigo, etc. 31% of falls are ‘accident- /environment-related’; 17% result from ‘gait/balance disorders or weakness’; 13% are caused by ‘dizziness and vertigo’; 9% result from ‘drop attack’ [5, 6, 7, 8, 9]. It is an 1 Corresponding Author. Serkan Turkeli, Control and Automation Department Istanbul Technical University, sturkeli@itu.edu.tr / serkan.turkeli@tesodev.com pHealth 2017 B. Blobel and W. Goossen (Eds.) IOS Press, 2017 © 2017 The authors and IOS Press. All rights reserved. doi:10.3233/978-1-61499-761-0-163 163