Kun Fang
ABSTRACT
In order to improve the accuracy of recognition of human motion states by smart phones, a deep recognition method based on Bayesian optimized Random Forest (B-RF) is proposed. Firstly, the sensor data input format is standardized by using time coordinate labels; Secondly, k-nearest neighbor and moving time window are used to expanding and de-noising the data of acceleration sensor, gyroscope, barometer, sensitometer and distance sensor. Finally, data mining is performed on sensor data in Bayesian-optimized random forests, and human motion states are classified. Experiments show that the recognition accuracy of the method can reach 97.26% in the training of multi person model, which can improve the recognition rate of motion state.
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