Foot Clearance Prediction using Wrist Acceleration and Gait Speed

Abstract

Elderly individuals experience fall accidents due to tripping because recognizing foot clearance during walking is difficult for them. To prevent fall accidents, foot clearance should be measured and informed in daily life. Foot clearance is commonly measured using vision-based systems, such as optical motion capture systems. However, problem of these vision-based systems is that these systems cannot measure foot clearance in daily life because they have limitations due to obstacles and field of view. Based on this problem, we developed a wearable fall prevention system using smart devices, such as smartphones and smartwatches. This study aimed to evaluate the proposed prediction method for foot clearance using sensor data obtained from wearable smart devices which can be used in daily life. The proposed method will contribute to measure foot clearance in daily life. This method predicts foot clearance from wrist acceleration and gait speed using a machine learning-based regression model. The proposed method was tested in a computational simulation with a public gait dataset obtained using an optical motion capture system. The results showed that the correlations between the predicted and actual foot clearance were at least 0.65. In conclusion, this study indicates the possibility that the proposed method can be used to measure foot clearance and thus can be used in wearable fall prevention systems.