Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection

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.