Big Data Energy Consumption Monitoring Technology of Obese Individuals Based on MEMS Sensor

Abstract

The application of micro electro mechanical system (MEMS) is more and more extensive, involving military, medical, communication and other major fields. The progress of science and technology has brought cross era changes to human beings, but also brought troubles to human beings. Because machines can replace most people, which leads to a significant reduction in human exercise, many people have the symptoms of obesity. Therefore, how to effectively detect human exercise energy consumption is of great significance to improve obesity symptoms. The energy consumption detector takes stm32f103zet6 as the core processor and uses the inertial sensor mpu6050 to build a MEMS sensor system to monitor the daily motion state and gait of human body in real time. In the design of the big data algorithm, the adaptive peak detection and step, decision tree two-level classification of motion recognition big data algorithm are organically integrated, and then combined with the acceleration vector value of the motion energy detection big data algorithm, to process the collected motion data, including the acceleration signal, gyroscope and other data processing, and finally complete the feature extraction, get the final recognition and detection results. Through the data reference, we can know that the system can recognize different human motion states. Among them, it has 95% accuracy in the motion recognition of sitting, standing, walking, running, going up and down stairs and lying back, which is basically the same as the top detectors on the market. In the energy consumption detection, it also has 95% accuracy, which proves the correctness of the experimental big data algorithm design, and also improves the accuracy It is proved that the system has good performance and high practicability, and can provide a new idea for obese individual motion detection.