Health Monitoring of Human Multiple Physiological Parameters Based on Wireless Remote Medical System

• Main Authors: Kai Zhang, Wenjie Ling
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Telemedicine; health monitoring; physiological signals; signal processing
• Online Access: https://ieeexplore.ieee.org/document/9063440/

Telemedicine, as a new technical means and medical model, can truly realize the sharing and monitoring of telemedicine information, and ultimately ensure that everyone has equal access to medical and health resources. Based on the research on the status of telemedicine application and wireless communication technology, this paper proposes a multi-physical parameter wireless telemedicine health monitoring system solution, and analyzes the overall structure and functional requirements of the system. Human physiological parameters of the wireless remote medical system for health monitoring include body temperature, respiration, blood oxygen saturation, pulse, blood pressure, and electrocardiogram. In this paper, fabric electrodes are used to extract human bioimpedance signals, discrete Fourier transform algorithm is used to detect human respiratory signals, and respiratory rate is detected based on dynamic differential threshold peak detection technology. The reflection type photoelectric sensor is used to realize the reflection of the human pulse signal, and the continuous measurement of the cuff-free blood pressure based on the pulse wave conduction time is combined with the ECG (Electrocardiogram) data. A self-learning threshold algorithm based on near-infrared photo plethysmo graphy signal trough detection is designed on the reflective blood oxygen saturation calculation algorithm. The difference threshold method is used to extract the QRS band feature points. We tested the overall operation of the system. The results show that the collected human physiological signal data is accurate. After a series of tests, the validity and reliability of the collected physiological signals have been proven.