Poster: Indoor Inertial-Based Fall Prediction and Pedestrian Tracking For The Elderly
Jingjing Yan, Craig Matthew Hancock · 2024
Recently, with the growing elderly population, fall prediction has gained more attention. However, only a few studies have focused on both fall prediction and pedestrian tracking, especially in indoor environments. This study proposes a novel prototype of simultaneous fall prediction and indoor pedestrian tracking by using smartphone-based inertial sensors. It also first introduces visual calibration of inertial data for fall prediction. This prototype has been tested in a single room with normal walking and falling activities. The accuracy of localization is 0.06m and a fall action could be identified about 350ms~400ms before collision.