Research And Implementation Of Motion Energy Consumption Detection Algorithm In Specific Scenarios Based On Inertial Units

With economic development and scientific and technological progress,people’s living standards are constantly improving,health has become a more important concern,and lack of physical exercise is an important factor leading to human sub-health.In order to scientifically and healthily evaluate the energy consumption of the human body during exercise,and to meet the needs of multi-field energy consumption calculations for human exercise,considering that existing human motion energy consumption detection methods have problems such as inconvenient detection equipment,strong invasiveness to the human body,poor accuracy of detection results,or complex algorithms,there is an urgent need for a portable detection device and high accuracy of detection results in specific scenarios.This thesis mainly studies energy consumption detection technology based on inertial units in specific scenarios,exploring the accurate calculation of human energy consumption in a single scene.It includes three research contents:algorithm for solving the key parameter attitude angle that reflects human motion energy consumption,research on human motion energy consumption,and implementation of a human motion energy consumption detection system.The details are as follows:This thesis mainly studies energy consumption detection technology based on inertial units in specific scenarios,exploring the accurate calculation of human energy consumption in a single scenario.This includes three research topics,including pose angle calculation algorithms for key parameters reflecting human energy consumption,research on human motion energy consumption,and implementation of a human motion energy consumption detection system.The details are as follows:(1)This thesis uses attitude angle parameters for the establishment of human motion energy consumption detection algorithm,and proposes an accurate attitude angle calculation method,which is based on adaptive complementary filtering and Kalman filtering to address the issue of current attitude calculation mainly focusing on the impact of gravity acceleration on attitude calculation and the complexity of algorithms.This study utilizes the attitude angle change rate to adaptively adjust the complementary filtering coefficient,and combines it with Kalman filtering to filter out the temperature deviation inside the gyroscope,increasing the response to rapid attitude changes.It achieves good results in output attitude angle accuracy and attitude angle data curve stationarity.(2)This thesis focuses on the different consumption ratios of various nutrients in the human body under different exercise intensities.By adding exercise intensity features and combining the calculated posture angle data features,a human exercise energy consumption algorithm based on ANN neural network and exercise intensity and posture angle features is proposed.This algorithm uses the motion intensity feature of the motion data,together with the features extracted from the acceleration data,angular velocity data,and attitude angle data,for the training of the ANN neural network energy consumption detection algorithm.While the mean square error is relatively small,the true and detected values are more fitted,the processing time is shorter,and the equipment is more convenient and flexible.(3)This thesis builds its own sports data acquisition equipment,and establishes a self-built data set through this equipment and a gas metabolism meter.At the same time,it designs and implements a human body motion energy consumption detection system,implements the above algorithm,and conducts actual user tests.The test results show that the system can effectively fulfill the expected functional requirements and time-consuming requirements for detection and response,and can complete the actual human motion energy consumption detection.