Recognition of Human Motion Intentions Based on Bayesian-Optimized XGBOOST Algorithm

In order to improve the recognition rate for lower extremity motion patterns, this study designs a recognition method for such patterns, which integrates electromyography (EMG) and inertial measurement unit (IMU) signals in three posture modes, including walking on the ground, squatting, and extending seated legs, to address the difficulty with obtaining high signal-to-noise ratio EMG and IMU signals synchronously. Besides, this study proposes a synchronous analysis method for EMG and IMU dual-mode information to correct antipower frequency interference accelerometer signals. The collected signals are preprocessed to extract eigenvalues. And by using the kernel principal component analysis (KPCA), the information on these eigenvalues is fused. Finally, according to the characteristics of the data, a Bayesian-optimized XGBOOST algorithm is designed. Lower-limb movement patterns are classified with the feature vector put into the optimization algorithm. Multiperson experimental results show that the average recognition accuracy for different poses can reach 94.42%, the average F1 value 95.33%, and the average return value 95.68%, proving that the model proposed can be used to identify human motion intentions and its generalization ability can detect individual differences in human bodies.