Optimization Design Of Mobile Health Monitoring System

Modern people have an increasing need for health care for themselves and their families.On July 1,2021,General Secretary Xi announced that China has built a moderately prosperous society in an all-round way.The living standard of ordinary people is gradually improving,and the demand for health is also growing.More and more people are beginning to pay attention to their daily health.More and more smart devices are equipped with sensors that can monitor the human heart rate and blood oxygen saturation level.However,many products on the market have limitations.Traditional health monitoring devices have relatively single functions and cannot meet the needs of wearing or long-term monitoring.On the other hand,smart devices carry relatively few health monitoring functions.In view of the above problems,this thesis designs a wearable device and corresponding software.Compared with traditional health monitoring devices,this device can be worn.Compared with smart devices,this device can measure more physiological health parameters.Optimized from the system function,increased respiratory rate,ECG,blood pressure detection.The device is capable of measuring heart rate,blood oxygen,respiratory rate,body surface temperature,pulse waveform,and ECG waveform,which can be combined with a blood pressure model to further measure blood pressure.The detection system involved in this thesis consists of a power supply module,a display module,a wireless transmission module,a PPG signal sensor,an ECG signal sensor,and a central processing module.Through further analysis of the physiological electrical signals,the heart rate detection algorithm selects the PPG signal as the original signal for heart rate extraction and designs the corresponding algorithm to decode the heart rate;the respiratory frequency selects the method based on the variability signal to demodulate the respiratory wave from the PPG signal;the detection of blood pressure selects the method using the neural network model to estimate the continuous blood pressure signal from the PPG signal,which achieves the full signal.The average absolute error of the heart rate algorithm is 1.3 beats/min,the average absolute error of the respiratory rate is 1.28breaths/min,and the average absolute error of the systolic blood pressure detection and diastolic blood pressure detection in the blood pressure detection is 9.72 mm Hg and 6.31 mm Hg,respectively,when tested using open biomedical data sets.In the system software design,the scheduling strategy is implemented to ensure the timed detection and reduce the sensor usage time,and the software lock is designed to avoid the public variable resources from being overwritten during software interruptions,and also to avoid the problems of abnormal task scheduling,physiological parameter task jamming or scheduling timeout during the scheduling process.Using the assembled system for real-life practical testing,experimental tests were conducted on the human body in three states: resting sitting,resting standing,and after light exercise.In the experimental results,the average absolute error of measured heart rate,blood oxygen saturation,and body temperature reached the index of similar products on the market,and the error between measured respiratory rate,systolic blood pressure,diastolic blood pressure and the reference value was small,which was able to achieve the measurement target.With the function fully open,the measured peak power is 0.163W;with the ECG detection function at regular intervals,the average power is 0.12W;without the ECG detection function,the average power is 0.10 W.