Comfort Prediction Method for Wearable Devices: Current Progress and Future Direction

Abstract

Falls constitute a significant health risk, particularly among the elderly, thus prompting the introduction of various wearable devices capable of fall detection. However, the majority of these devices prioritize accuracy over wearer comfort, which significantly influences user adherence and, by extension, the broader development of wearable technologies. Addressing this oversight, this review first summarizes the current methods for predicting the comfort of wearable devices, evaluating them in terms of feasibility and accuracy, reliability and effectiveness, as well as safety and privacy. Subsequently, building upon the evaluation of existing methods, this review proposes a predictive solution based on the XGBoost algorithm.