A comprehensive comparison of accuracy and practicality of different types of algorithms for pre-impact fall detection using both young and old adults

Abstract

This study aims to comprehensively compare the accuracy and practicality of three different types of algorithms for pre-impact fall detection using both young and old subjects. Threshold-based, conventional machine learning (SVM) and deep learning (ConvLSTM) algorithms were compared. Results showed that ConvLSTM had an ac-curacy of 99.16 % (sensitivity: 99.32 %, specificity: 99.01 %) and an averaged lead time of 403 ms on young subjects, which outperformed SVM (97.16 %, 385 ms) and much superior to the threshold-based algorithm (89.06 %, 333 ms). In addition, latency tests on an embedded device showed that the Lite model of ConvLSTM had a low latency of 2.1 ms, which was comparable to the threshold-based algorithm (<1 ms) but much lower than SVM (86.9 ms). The feasibility and effectiveness of applying algorithms trained on young subjects to old subjects were also validated. These findings suggested that ConvLSTM has great potential for detecting pre -impact falls and preventing fall-related injuries.