A class‐imbalanced deep learning fall detection algorithm using wearable sensors

Abstract

Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. There-fore, timely and accurate fall detection algorithm research is extremely important. Recently, a number of researchers have focused on fall detection and made many achievements, and most of the relevant algorithm studies are based on ideal class‐balanced datasets. However, in real‐life applica-tions, the possibilities of Activities of Daily Life (ADL) and fall events are different, so the data col-lected by wearable sensors suffers from class imbalance. The previously developed algorithms per-form poorly on class‐imbalanced data. In order to solve this problem, this paper proposes an algorithm that can effectively distinguish falls from a large amount of ADL signals. Compared with the state‐of‐the‐art fall detection algorithms, the proposed method can achieve the highest score in multiple evaluation methods, with a sensitivity of 99.33%, a specificity of 91.86%, an F‐Score of 98.44% and an AUC of 98.35%. The results prove that the proposed algorithm is effective on class‐imbal-anced data and more suitable for real‐life application compared to previous works.