Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection
Joint Authors
Rebaudengo, Maurizio
Hemmatpour, Masoud
Ferrero, Renato
Montrucchio, Bartolomeo
Gandino, Filippo
Source
Journal of Healthcare Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-26
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Hemmatpour, Masoud& Ferrero, Renato& Gandino, Filippo& Montrucchio, Bartolomeo& Rebaudengo, Maurizio. 2018. Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1187310
Modern Language Association (MLA)
Hemmatpour, Masoud…[et al.]. Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection. Journal of Healthcare Engineering No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1187310
American Medical Association (AMA)
Hemmatpour, Masoud& Ferrero, Renato& Gandino, Filippo& Montrucchio, Bartolomeo& Rebaudengo, Maurizio. Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1187310
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187310