Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection. (26th June 2018)

• Record Type: Journal Article
• Authors: Hemmatpour, Masoud; Ferrero, Renato; Gandino, Filippo; Montrucchio, Bartolomeo; Rebaudengo, Maurizio
• Other Names: Hartwig Valentina Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2018(2018)
• Publication Date: 2018-06-26
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2018/4750104
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : Falls are critical events for human health due to the associated risk of physical and psychological injuries. Several fall-related systems have been developed in order to reduce injuries. Among them, fall-risk prediction systems are one of the most promising approaches, as they strive to predict a fall before its occurrence. A category of fall-risk prediction systems evaluates balance and muscle strength through some clinical functional assessment tests, while other prediction systems investigate the recognition of abnormal gait patterns to predict a fall in real time. The main contribution of this paper is a nonlinear model of user gait in combination with a threshold-based classification in order to recognize abnormal gait patterns with low complexity and high accuracy. In addition, a dataset with realistic parameters is prepared to simulate abnormal walks and to evaluate fall prediction methods. The accelerometer and gyroscope sensors available in a smartphone have been exploited to create the dataset. The proposed approach has been implemented and compared with the state-of-the-art approaches showing that it is able to predict an abnormal walk with a higher accuracy (93.5%) and a higher efficiency (up to 3.5 faster) than other feasible approaches.