Gait recognition via random forests based on wearable inertial measurement unit

In recent years, gait detection has been widely used in medical rehabilitation, smart phone, criminal investigation, navigation and positioning and other fields. With the rapid development of micro-electro mechanical systems, inertial measurement unit (IMU) has been widely used in the field of gait recognition with many advantages, such as low cost, small size, and light weight. Therefore, this paper proposes a gait recognition algorithm based on IMU, which is named as FPRF-GR. Firstly, a fusion feature engineering operator is designed to eliminate redundant and defective features, which is mainly based on Fast Fourier Transform and principal component analysis. Then, in the design of classifier, in order to meet the requirements of gait recognition model for accuracy, generalization ability, speed, and noise resistance, this paper compares random forest (RF) and several commonly used classification algorithms, and finds that the model constructed by RF can meet the requirements. FPRF-GR builds the model based on RF, and uses the tenfold cross validation method to evaluate the model. Finally, this paper proposes an optimization scheme for the two parameters of decision tree number and sample number in RF. The results show that FPRF-GR can identify five gaits (walk, stationary, run, and up and down stairs) with the average accuracy of 98.2%.