A smartphone-based fall detection system for the elderly

Falls can be severe enough to cause disabilities especially to frail populations. Thus, prompt health care provision is essential to prevent and restore any harm. The purpose of this study is to develop a smartphone-based fall detection system that can distinguish between falls and activities of daily living (ADL). The typical fall detection system consists of a sensing component and a notification module. Android devices, equipped with sensors and communication services, are the best candidates for the development of such systems. This work incorporates a threshold based algorithm, whose accuracy is enhanced by a k Nearest Neighbor (kNN) classifier. In addition, this paper proposes the implementation of a personalization and power regulation system. It achieves high fall detection accuracy, (97.53% sensitivity and 94.89% specificity), which is comparable to related works.