Research on Motion Recognition Algorithm Based on Accelerometer

In recent years, human motion recognition acceleration sensor has become one of the active research direction and the emerging field of computer pattern recognition, more and more researchers use acceleration sensor in motion monitoring of human experimental environment and natural living environment. Human motion recognition mainly includes data acquisition, data processing, feature extraction and selection, and classifier design. With the further development of microelectronics and sensors, sensors can be embedded in mobile devices, portable devices and so on. The use of acceleration sensor for human motion monitoring has broad application prospects. On the one hand, the daily exercise behavior is closely related to the physical and mental health. It is of great significance for the development of scientific exercise and fitness program to improve the health status. On the other hand, it is of great value to monitor the abnormal motion of human body. This kind of abnormal exercise can bring some harm to the human body. It can provide timely help to the fall and reduce the risk of falling. Through the comparison between threshold and peak vertical acceleration sensor data waveform threshold trough peak and valley time intervals of three characteristic values, and combined with the principle of decision tree classification to achieve accurate recognition of squatting, jumping and running action. A human fall detection algorithm, the algorithm falls by extracting human motion features in the process, focusing on the use of the three axis acceleration sensor correlation to fall over, fell back, left and right fall four to fall to distinguish fall behavior. The experimental results show that the average recognition rate of human motion behavior is more than 90%. which has a certain practical value.