CFSCare: ML-based Activity Monitoring System for Chronic Fatigue Syndrome Patients Using Smartphone and Wrist Sensor

Abstract

Chronic Fatigue Syndrome (CFS) is a disorder with complex symptoms among patients. In most cases, CFS sufferers describe severe body weakness, poor sleep and inability to perform their usual work as their primary complaints. Symptoms worsen when the patient attempts to do similar work as tolerated. To prevent the worsening of symptoms, the patients need to be aware of what intensity of work they can manage. In this paper, we propose CFSCare, a hardware and software-based system that uses ML models to measure CFS patients' daily activity and energy expenditure objectively. Through our developed App, CFSCare submits to the user a summary of the comprehensive reports of the user's activity and sends a recommendation to the user on how they can prevent acquiring symptoms of CFS brought about by over-exertion. We use an Android smartphone and wrist sensor (MetamotionC) to monitor their leg and hand activity. We develop ML models based on SVM and DT algorithms to predict particular leg and hand activities. Among the applied ML models, SVM exhibited a brilliant performance with 98% accuracy in predicting leg activities and an average 10-fold cross-validation score of 94%. For the hand activities prediction, DT recorded the best accuracy of 96%, and the average score of 10 cross-validations is also 96%. Since CFS patients can tire with exertion after a small amount of daily activity, CFSCare can play a vital role in preventing the patient from over-exertion through the monitoring features.