Advances in wireless technologies, the internet of things (IoT), low-power electronics, and in the domain of connected health are driving innovations in wearable medical devices at a tremendous pace. Wearable sensor systems composed of flexible and stretchable materials have the potential to better interface to the human skin, whereas silicon-based electronics are extremely efficient in sensor data processing and transmission. Therefore, flexible and stretchable sensors combined with low-power silicon-based electronics are a viable and efficient approaches for medical monitoring. Medical devices designed for monitoring human vital signs have applications in fitness monitoring, medical diagnostics including disease prediction. In this research, we investigate the feasibility of using wearable sensors for disease prediction. We designed two feasibility case studies of wearable sensor systems for specific disease predictions, hypertension and neonatal sepsis prediction.
The first case study is design wearable system for hypertension prediction. Objective: Heart rate variability (HRV) is often used to assess the risk of cardiovascular disease, and data on this can be obtained via electrocardiography (ECG). However, collecting heart rate data via photoplethysmography (PPG) is now a lot easier. We investigate the feasibility of using the PPG-based heart rate to estimate HRV and predict diseases. Materials and Methods: We obtain three months of PPG-based heart rate data from subjects with and without hypertension, and calculate the HRV based on various forms of time and frequency domain analysis. We then apply a data mining and big data technique to this estimated HRV data, to see if it is possible to correctly identify patients with hypertension. Result: We use six HRV parameters to predict hypertension, and find SDNN has the best predictive power. Conclusion: We show that early disease prediction is possible through collecting one’s PPG-based heart rate information.
The second case study is design the wearable sensor system for neonatal sepsis prediction in the neonatal intensive care unit (NICU). We have applied principles of statistical signal processing and nonlinear dynamics to analyze heart rate time series from premature newborn infants in order to assist in the early diagnosis of sepsis, a common and potentially deadly bacterial infection of the bloodstream. We began with the observation of reduced variability and transient decelerations in heart rate interval time series for hours up to days prior to clinical signs of illness. We find that measurements of standard deviation, sample asymmetry and sample entropy are highly related to imminent clinical illness. We developed multivariable statistical predictive models, and an interface to display the real-time results to clinicians. Using this approach, we have observed numerous cases in which incipient neonatal sepsis was diagnosed and treated without any clinical illness at all. This case study focuses on the mathematical and statistical time series approaches used to detect these abnormal heart rate characteristics and present predictive monitoring information to the clinician.
Both two case studies of wearables sensors system can collect and monitor the vital sign and predict the diseases with the help of improved technology have been developed greatly and are considered reliable tools for long-term health monitoring systems. These are applied in the observation of a large variety of health monitoring indicators in the vital signs. Research is underway to add analysis of other vital signs to algorithms for predicting sepsis, and other pathological conditions for which early detection and intervention would be expected to lead to better patient outcomes.

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