The cyber-physical system (CPS) has been discussed and studied extensively since 2010. It provides various solutions for monitoring the user's physical and psychological health states, enhancing the user's experience, and improving the lifestyle. A variety of mobile internet devices with built-in sensors, such as accelerators, cameras, PPG sensors, pressure sensors, and the microphone, can be leveraged to build mobile cyber-physical applications that collected sensing data from the real world, had data processed, communicated to the internet services and transformed into behavioral and physiological models. The detected results can be used as feedback to help the user understand his/her behavior, improve the lifestyle, or avoid danger. They can also be delivered to therapists to facilitate their diagnose. Designing CPS for health monitoring is challenging due to multiple factors. First of all, high estimation accuracy is necessary for health monitoring. However, some systems suffer irregular noise. For example, PPG sensors for cardiac health state monitoring are extremely vulnerable to motion noise. Second, to include human in the loop, health monitoring systems are required to be user-friendly. However, some systems involve cumbersome equipment for a long time of data collection, which is not feasible for daily monitoring. Most importantly, large-scale high-level health-related monitoring systems, such as the systems for human activity recognition, require high accuracy and communication efficiency. However, with users' raw data uploading to the server, centralized learning fails to protect users' private information and is communication-inefficient. The research introduced in this dissertation addressed the above three significant challenges in developing health-related monitoring systems. We build a lightweight system for accurate heart rate measurement during exercise, design a smart in-home breathing training system with bio-Feedback via virtual reality (VR) game, and propose federated learning via dynamic layer sharing for human activity recognition.
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