Abstract
The mobile crowd sensing technology in the environment integrating human, machines and things is an emerging direction in social computing. In kinematics research, continuous blood pressure monitoring and calibration are the basis for revealing the correlation between athlete motor function and blood pressure. At the same time, in the field of medical research, hypertension can be more easily controlled, thus improving the effectiveness of hypertension treatment. This paper presents the design principle of a human-machine fusion system based on CrowdOS, a mobile crowd sensing platform. The system innovatively establishes the correlation between blood pressure and exercise, improves the accuracy of cuffless blood pressure measurement, and verifies the feasibility of calibrating continuous cuffless blood pressure measurement based on exercise information. Using our system and electronic cuff sphygmomanometer, we measured 65 groups of data in walking, running, sitting and climbing stairs, each group lasting about 10 minutes. Based on these data, we established a regression analysis model for blood pressure measurement calibration. The accuracy of blood pressure calibration was improved from the original systolic root mean square error of 13.43mmHg and diastolic root mean square error of 8.35mmHg to 9.76mmHg and 5.56mmHg. The design method proposed in this paper provides a feasible solution for continuous cuffless blood pressure measurement and calibration, and shows broad application prospects in the fields of athlete scientific training and medical care.
Highlights
The blood pressure monitoring system we proposed is just one application on the CrowdOS
The OS kernel architecture is presented in figure 1 and we introduce the relationships between various modules
When the data collection process of the sensing-end runs, the blood pressure heart rate data collected by the MKB0803 module and the inertial measurement unit (IMU) data collected by the LPMS-ME1 DK module are stored in the list of the Redis process
Summary
The publishing frequency is 1Hz. At the same time, the device uses the Pyserial driver to read the heart rate, systolic and diastolic blood pressure data collected by the MKB0803 sensor from the UART serial port. The sensing-end system parses the messages to obtain the current working status of the device, including six states: reading, wearing error, clearing success, calibrating, calibration success and calibration failure, and report the device status, calibrated blood pressure and heart rate data to the server. After the two services are called by the web application, the server platform will send the transmitted value to the ‘‘property/set’’ topic of the sensing-end system for calibration of the blood pressure heart rate chip. The data calibration process approximately publishes forecast data every 10 seconds
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