A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results. (1st July 2019)

• Record Type: Journal Article
• Authors: Hemmatpour, Masoud; Ferrero, Renato; Montrucchio, Bartolomeo; Rebaudengo, Maurizio
• Other Names: Piccinno Antonio Academic Editor.
• Is Part Of: Advances in human-computer interaction. Volume 2019(2019)
• Publication Date: 2019-07-01
• Subjects: Human-computer interaction -- Periodicals; Human-computer interaction; Periodicals; Electronic journals; 004.019
• DOI: 10.1155/2019/9610567
• Languages: English
• ISSNs: 1687-5893

Abstract

Abstract : Injuries due to unintentional falls cause high social cost in which several systems have been developed to reduce them. Recently, two trends can be recognized. Firstly, the market is dominated by fall detection systems, which activate an alarm after a fall occurrence, but the focus is moving towards predicting and preventing a fall, as it is the most promising approach to avoid a fall injury. Secondly, personal devices, such as smartphones, are being exploited for implementing fall systems, because they are commonly carried by the user most of the day. This paper reviews various fall prediction and prevention systems, with a particular interest to the ones that can rely on the sensors embedded in a smartphone, i.e., accelerometer and gyroscope. Kinematic features obtained from the data collected from accelerometer and gyroscope have been evaluated in combination with different machine learning algorithms. An experimental analysis compares the evaluated approaches by evaluating their accuracy and ability to predict and prevent a fall. Results show that tilt features in combination with a decision tree algorithm present the best performance.