# Classification and Regression of Muscle Neural Signals on Human Lower Extremities via BP_AdaBoost
###### Abstract  
Electromyography (EMG) signals are widely applied in the classification of human motion and intention recognition as having the characteristic of earlier than actual limb motion. In this article, to improve its accuracy of classification and prediction, we firstly analyze the relationship between muscle length and joint movement and select rectus femorts and biceps femorts as the experimental muscles to collect neural signals by means of musculoskeletal analysis software. EMG sensors are used to measure those muscles' EMG signals of five kinds of knee movements, including thigh-raising, calf-raising, squatting, knee bending on chair, and walking. We designed a BP_AdaBoost algorithm with the BP neural network as a weak classifier and weak regressor, and a muscle neural activation is used as the input for recognition. It is a negative correlation between the length of the rectus femoris and the biceps femoris during gait. Their muscle neural signals are used as the input of the recognition algorithm. The experiment results show that the proposed algorithm improves the rate of BP neural network from 78.82% to 93.52%. The thigh EMG signal successfully maps the knee joint angle by utilizing BP_AdaBoost; its error in identifying five kinds of motion modes is lowest compared with other regression algorithms. EMG signals; muscle neural activation; knee movement; identification and regression; BP_AdaBoost +
Footnote †: journal: Applied  
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human motion recognition based on the EMG signal is still in the primary stage, which also makes researchers work on designing different classifiers to improve the recognition efficiency [11; 12]. [PERSON] takes the EMG signal as input and motion classification, noise tolerance, and computational complexity as criteria to recognize movements through genetic algorithm. He proved the possibility of recognizing human motion by the EMG signal [13]. [PERSON] et al. used the BP neural network to establish a nonlinear regression model between the EMG characteristics and the joint motion angle. Their experimental results show that the extracted signal feature information is better than the traditional principal component analysis method [14]. The existing research on gait recognition technology indicate that the stronger the stability of knee motion, the more reliable the gait features obtained from knee joint, and the higher the gait recognition rate relying on knee motion [13; 14], which provides a theoretical basis for the study of knee joint movement and lower extremity EMG signals. Based on this, this article focuses on the knee movement and muscle neural signals. Firstly, according to the anatomical principle, this article analyzes the thigh muscles related to the knee movement by OpenSim (a musculoskeletal analysis software) and selects the rectus femoris muscle in the front of the thigh and the biceps femoris muscle in the back of the thigh as the research object through SPSS (Statistical Product Service Solutions) correlation analysis. Then, the EMG signals of the two muscles are obtained by experiment. The muscle activity is calculated as the characteristic value of the EMG signal after filtering and noise reduction. Finally, this article seeks to solve the problem that the classification performance of the BP neural network would be affected by the separability of samples and to explore the recognition and prediction of knee motion with the thigh EMG signal. This article designs a BP_AdaBoost by BP as the weak classifier and predictor to recognize the typical motion of human lower limbs and predict the angle of the knee joint. The results show that the BP_AdaBoost strong classifier can improve the recognition of different motion patterns.