Autonomous Exercise Rehabilitation for Heart Failure Patients based on Six-Minute Walk Test through Internet-of-Thing Devices

Abstract

Heart failure (HF) is one of the most common causes of hospitalization for people over 65 years old, and over half of patients who diagnosed with severe HF conditions at first time cannot survive over 5 years. It is also noticed that rehospitalization rate of heart failure patients may increase to 50% in three months and the mortality rate from 33% to 50% within five years if HF patients are not treated with proper medication and physical therapies. Here we provide a classification system and an early warning mechanism for detecting HF disease based on integrating 6-minute walking test (6MWT), Internet of medical thing devices, and cloud computing technologies. This study performed 6MWTs for 50 HF patients accompanied by medical staffs for recording walkway distance, walking heart rate, and resting heart rate. All retrieved features and classified functional levels of heart organ of HF patients are trained as a target referencing dataset. According to the selected features and trained results, the clustered information representing various heart conditions is applied for detecting potential HF patients at earlier stages. In addition, the newly obtained self-exercise rehabilitation records from HF patients in a real-time manner will be compared to her/his previous 6MWT patterns. The compared differences are considered as important information for doctors to arrange medical treatment and adjusted physical therapy during the next follow-up visit in hospital.