Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection

Autor: Hemmatpour, Masoud, Ferrero, Renato, Gandino, Filippo, Montrucchio, Bartolomeo, Rebaudengo, Maurizio
Rok vydání: 2018
Předmět:
fall prediction
Computer science
02 engineering and technology
computer.software_genre
Accelerometer
law.invention
law
Accelerometry
0202 electrical engineering
electronic engineering
information engineering
Fall avoidance system
health care monitoring
fall prediction
walk classification
abnormal gait detection
Gait
lcsh:R5-920
Signal processing
Movement Disorders
Signal Processing
Computer-Assisted
Gyroscope
Middle Aged
lcsh:R855-855.5
020201 artificial intelligence & image processing
Gait Analysis
lcsh:Medicine (General)
Algorithms
Research Article
Biotechnology
Adult
lcsh:Medical technology
Adolescent
Article Subject
walk classification
0206 medical engineering
Biomedical Engineering
Health Informatics
Machine learning
Young Adult
abnormal gait detection
Humans
Aged
Balance (ability)
business.industry
020601 biomedical engineering
Nonlinear system
Nonlinear Dynamics
Accidental Falls
Surgery
Abnormal walk
Artificial intelligence
Threshold model
business
computer
Zdroj: Journal of Healthcare Engineering, Vol 2018 (2018)
Journal of Healthcare Engineering
ISSN: 2040-2309
2040-2295
Popis: 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.
Databáze: OpenAIRE
Externí odkaz: