Heart Failure Indicators Estimation in a Nonmedical Setting: a Pilot Study Using 6MWT Data from Smart Shoes

Heart failure (HF) is a chronic disease that requires long-term monitoring of a patient's health. However, current medical testing methods usually require specialized equipment and the involvement of professionals. These make it difficult to obtain data on patients' medical indicators in a non-medical setting. In this study, we explored the feasibility of assessing HF patients' medical indicators using Six-Minute Walk Test (6MWT) data collected by smart shoes. We use machine learning algorithms to establish mapping relationships between gait characteristics and 42 medical indicators. The relative errors of the top 7 medical indicators are less than 5%. This finding provides a new perspective on how to obtain patients' medical indicators data in a non-medical setting.