Background: This paper focuses on the characteristics of lower limb EMG signals for common movements. Methods: We obtained length data for lower limb muscles during gait motion using software named OpenSim; statistical product and service solutions (SPSS) were utilized to study the correlation between each muscle, based on gait data. Low-correlation muscles in different regions were selected; inertial measurement unit (IMU) and EMG sensors were used to measure the lower limb angles and EMG signals when on seven kinds of slope, in five kinds of gait (walking on flat ground, uphill, downhill, up-step and down-step) and four kinds of movement (squat, lunge, raised leg and standing up). Results: After data denoising and feature extraction, we designed a double hidden-layer BP neural network to recognize the above motions according to EMG signals. Results show that EMG signals of selected muscles have a certain periodicity in the process of movement that can be used to identify lower limb movements. Conclusions: It can be seen, after the recognition of different proportions of training and testing sets that the average recognition rate of the BP neural network is 86.49% for seven gradients, 93.76% for five kinds of gait and 86.07% for four kinds of movements.

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 femoris and biceps femoris 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.

To eliminate the influence of spacesuits joint resistant torque on the operation of astronauts, an active spacesuit scheme based on the joint-assisted exoskeleton technology is proposed. Firstly, we develop a prototype of the upper limb exoskeleton robot and theoretically analyse the prototype to match astronauts motion behavior. Then, the Jiles-Atherton model is adopted to describe the hysteretic characteristic of joint resistant torque. Considering the parameter identification effects in the Jiles-Atherton model and the local optimum problem of the basic PSO (particle swarm optimization) algorithm, a SA- (simulated annealing-) PSO algorithm is proposed to identify the Jiles-Atherton model parameters. Compared with the modified PSO algorithm, the convergence rate of the designed SA-PSO algorithm is advanced by 6.25 and 20.29, and the fitting accuracy is improved by 14.45 and 46.5 for upper limb joint model. Simulation results show that the identified J-A model can show good agreements with the measured experimental data and well predict the unknown joint resistance torque.