Experimental Evaluation of Regression Model-Based Walking Speed Estimation using Lower Body-Mounted IMU

Shaghayegh Zihajehzadeh, Edward J. Park

Details

Category

Contributed Papers (Oral)

Theme

Biomedical Sensors and Wearable Systems

Sessions

08:00 - 09:30 | Wed 17 Aug | Fantasia Q | WeAT12

Physical Sensors and Sensor Systems

Abstract

This study provides a concurrent comparison of regression model-based walking speed estimation accuracy using lower body mounted inertial sensors. The comparison is based on different sets of variables, features, mounting locations and regression methods. An experimental evaluation was performed on 15 healthy subjects during free walking trials. Our results show better accuracy of Gaussian process regression compared to least square regression using Lasso. Among the variables, external acceleration tends to provide improved accuracy. By using both time-domain and frequency-domain features, waist and ankle-mounted sensors result in similar accuracies: 4.5% for the waist and 4.9% for the ankle. When using only frequency-domain features, estimation accuracy based on a waist-mounted sensor suffers more compared to the one from ankle.

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