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 to-fold cross-validation score of 94%. For the hand activities prediction, DT recorded the best accuracy of 96%, and the average score of to cross-validations is also 96%. Since CFS patients can tire with exertion after a small amount of daily activity, CFSCare can playa vital role in preventing the patient from over-exertion through the monitoring features.

Heart Rate (HR) and Heart Rate Variability (HRV) is an essential measurement to know the heart’s cardiovascular condition. Many works have been done for measuring HR-HRV based on the facial video non-invasively. In this paper, based on our previous work experience of measuring HR-HRV by Remote photoplethysmography signals (rPPG) analysis, we have built a prediction model from the 10-second time series data extracted from a facial video. In this work, we have used the instantaneous public dataset with several data models to predict the HR-HRV, and stress levels exclusively from the dataset. We have used here some of the popular algorithms appropriate for this task. We have also analyzed the stress level classification on the gender of a subject using the same facial videos with 16 different classifiers resulting in almost perfect accuracy for several classifiers.

Accurate and valid health information is crucial for effective medical management. Failure to collect adequate information from physical and mental health examinations can be a barrier to Virtual medical platforms and telemedicine. In this paper, we propose the non-invasive “Dream” project prototype to monitor and record heart rate (HR), heart rate-variation (HRV) (for physical health), and stress (for mental health) using only a smart-phone. This non-invasive mobile application, “Dream” uses the front camera to capture video to calculate HR-HRV and stress. The full “Dream” project encompasses our previous facial video HR-HRV and stress work. We have also compared our proposed “Dream” project with 39 works in this area. We found a significant positive difference between our proposed “Dream” project compared to other projects in respect to user accessibility, application, cost-effectiveness, hospitalization monitoring, and human health status. Furthermore, we can apply “Dream” in remote human health monitoring, driver monitoring, and creating vital sign records non-invasively without a health care assistant. Especially during a pandemic, this virtual health monitoring system can be useful for scaled-up telemedicine to serve the remote population.