Researchers are looking into Human Activity Recognition to find out different activities using affordable sensors. They are also using machine learning for real-time monitoring with sensor data. In healthcare, it's important to know how much energy is used during activities, especially for people who cannot move much, like older folks with Chronic Fatigue Syndrome. Figuring out their daily energy use is crucial to help them manage fatigue better. So, there's a need for a detailed study on finding different activities and understanding how much energy is used in every activity, especially in healthcare. In this survey paper, we explored articles about recognizing physical and social activities. We looked into the detailed methods used to calculate energy expenditure, carefully comparing the sensors used in various research projects. We investigated pre-processing techniques for sensor data. We uncovered how to extract features and studied the machine-learning methods used with raw sensor data. Bringing all this together not only broadened our understanding but also gave us a nuanced view of recognizing activities and calculating energy expenditure. This paper provides a detailed survey of recent sensor-based research focused on detecting and measuring physical activities. There is a scarcity of systems utilizing sensors for this purpose. As the number of individuals facing challenges in performing day-to-day activities continues to rise, there is an imperative need for enhanced methods to develop such systems.

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.

Two billion people are affected by hemoglobin (Hgb) related diseases. Usual clinical assessments of Hgb are conducted by analyzing venipuncture-obtained blood samples in laboratories. A non-invasive, cheap, point-of-care and accurate Hgb test is needed everywhere. Our group has developed a non-invasive Hgb measurement system using 10-second Smartphone videos of the index fingertips. Custom hardware sets were used to illuminate the fingers. We tested four lighting conditions with wavelengths in the near-infrared spectrum suggested by the absorption properties of two primary components of blood-oxygenated Hgb and plasma. We found a strong linear correlation between our measured and laboratory-measured Hgb levels in 167 patients with a mean absolute percentage error (MAPE) of 5%. In our initial analysis, critical tasks were performed manually. Now, using the same data, we have automated or modified all the steps. For all, male, and female subjects we found a MAPE of 6.43%, 5.34%, and 4.85 and mean squared error (MSE) of 0.84, 0.5, and 0.49 respectively. The new analyses however, have suggested inexplicable inconsistencies in our results, which we attribute to laboratory measurement errors reflected in a non-normative distribution of Hgb levels in our studied patients, as well as excess noise in the specific signals we measured in the videos. Based on these encouraging results, and the promise of greater accuracy with our revised hardware and software tools, we now propose a rigorous validation study to demonstrate that this approach to hemoglobin measurement is appropriate for general clinical application.