Application of wearable sensors in constructing a fall risk prediction model for community-dwelling older adults: A scoping review

Abstract

BackgroundFalls are a particularly important public health problem among older people. Early identification of risk factors is crucial for reducing the risk of falls in older adults. Studies have confirmed the effectiveness of sensor-based fall risk prediction models for the older population. This article aims to sort out the current use of wearable sensors in building fall risk models for older adults in the community and explore the suitable use of sensors in model construction and the prospects and possible difficulties of model application. MethodsThis scoping review was conducted from 26 November 2023 to 9 March 2024. It was searched through Web of Science, PubMed, OVID, EBSCO and CNKI using the terms “wearable sensor” or “inertial sensor” or “inertial motion capture” or “wearable electronic devices” or “IMU” or “MEMS” or “accelerometer” or “gyroscope” or “magnetometer” or “smartphone” and “fall” and “predict” or “prediction” and “older adults” or “older men” or “older women” or “elderly” and “community” or “neighborhood” or “dwelling”. ResultsThirty-one articles were included, and the selection of sensor type, location, and other characteristics and indicators, as well as model types, was summarized. Discussion and ConclusionsWearable sensors with a frequency of 100 Hz located in a combination of spine/ pelvis/ hip-shank-feet position is recommended. In addition, walking tests and TUG and its variants are appropriate in the community. However, more empirical research is needed to obtain the best model construction combination and apply it effectively to the community.