Analyzing mobility patterns of complex chronic patients using wearable activity trackers: a machine learning approach

Abstract

This study suggests using wearable activity trackers to identify mobility patterns in Chronic Complex Patients (CCP) and investigate their relation with the Barthel Index (BI) for assessing functional decline. CCP are individuals who suffer from multiple, chronic health conditions that often lead to a progressive decline in their functional capacity. As a result, CCP frequently require the use of healthcare and social resources, which can place a significant challenge on the healthcare system. Evaluating mobility patterns is critical for determining CCP’s functional capacity and prognosis. In order to monitor the overall activity levels of CCP, wearables activity trackers are proposed. Utilizing the data gathered by the wearables, time series clustering with Dynamic Time Warping (DTW) is employed to generate synchronized mobility patterns of mean activity and coefficient of variation profiles. The research has revealed distinct patterns in individuals’ walking habits, including the time of day they walk, whether they walk continuously or intermittently, and their relation to BI. These findings could significantly enhance CCP’s quality of care by providing a valuable tool for personalizing treatment and care plans.