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In recent years, the prevalence of cardiovascular diseases has been growing geometrically, especially among the elderly. Real-time heart rate monitoring becomes an effective measure for preventing cardiovascular diseases. Traditional contact-based real-time heart rate measurement methods require long-term wearing of contact electrodes, which can cause inconvenience to the elderly population. In this study, a wide spectrum full color night vision elderly face video heart rate automatic measurement and early warning system was designed and developed by combining imaging photoplethysmography (IPPG), video acquisition equipment based on starlight wide spectrum camera and high-speed face recognition signal processing algorithm based on deep learning. By constructing a software and hardware platform, a comprehensive evaluation of the system's application in elderly facial video heart rate measurement experiments was conducted. [ABSTRACT FROM AUTHOR]

Objective: This study is aimed to design wearable care equipment based on the narrow-band Internet of Things (NB-IoT) to assist in applying health-related big data and gain the optimum treatment time for patients with fractures. Methods: A wearable care equipment based on the NB-IoT technique (namely, a brace) was designed by combining sensors with embedded systems, man–machine interaction technique, and so on. Also, the exercise heart rate, the surface temperature of disease location, and the data on oxyhemoglobin saturation of patients were acquired using sensors. Next, these data were sent to an application (APP) on a cell phone after transmission to a single-chip machine for processing. Finally, the data on heart rate, body temperature, blood oxygen, and their variation curves were displayed on the APP. Results: The wearable care equipment could provide a normal average recovery curve for patients with fractures, which was instrumental for patients and doctors to fully grasp the rehabilitation condition. If the key indicators were abnormal and exceeded the threshold value, the wearable equipment generated an early warning signal to alert patients to seek medical attention in time. Conclusions: The test results indicated that the wearable care equipment based on the NB-IoT technique could realize the acquisition, storage, and analysis of the physiological parameters of patients. The wearable equipment had a series of characteristics, including low cost, low power dissipation, stable signal connection, and strong practicability, having a certain practical significance for the consistency of enhancing the health service and assisting the health-related big data development. [ABSTRACT FROM AUTHOR]