Inertial measurement unit signal-based machine learning methods for frailty assessment in geriatric health

Abstract

Frailty is a geriatric syndrome that may result in poor health outcomes such as hospitalization, disability, psychological distress, and reduced life satisfaction, and it is also associated with higher healthcare costs. The aim of this study is to classify frailty in elders at an early stage (pre-frail) to lower the risk of frailty and, hence, improve the quality of life. The other two classes in the classification task are frail and robust (non-frail). To achieve this, a dataset based on gait was utilized, which was recorded by an Inertial Measurement Unit (IMU) sensor, including gyroscope and accelerometer data. In this study, two approaches were assessed: the first used advanced Deep Learning (DL) algorithms to analyze raw IMU signals, and the second used conventional Machine Learning (ML) methods with hand-crafted features. The DL model, i.e., InceptionTime, beat the other algorithms in the DL approach with a remarkable test accuracy of 98%. On the ML side, Random Forest reported the most successful ML method, which achieved a test accuracy of 63.3%. For a careful assessment of the models, other evaluation metrics like Precision, Recall, and F1-score were also evaluated. The evaluation of both approaches produces research benefits for the classification of frailty in older people and allows for the investigation of new areas, promoting deeper comprehension and well-informed decision-making, particularly in healthcare systems.