With the widespread use of smartphones,their built-in sensors provide rich data resources for monitoring and analyzing users'movement status.These data can not only help users understand their activity patterns,but also be used to estimate the amount of calories consumed during daily exercise.However,makes sports health applications accurately classify these data and predicting users'motion characteristics remains a technical challenge.It explores the problems in motion state classification and feature prediction based on smartphone sensor data,and proposes suggestions for optimizing smartphone sensor data processing to enhance the accuracy of motion state classification,laying the foundation for developing more intelligent and accurate sports health monitoring applications.
NETL
NSTL
万方数据
