(1) Background: In the case of quick picking and heavy lifting, the carrying action results in a much more active myoelectric signal in the lower back than in an upright stationary one, and there is a high risk of back muscle injury without proper handling skills and equipment. (2) Methods: To reduce the risk of LBP during manual handing tasks, a hip active exoskeleton is designed to assist human manual lifting. A power control method is introduced into the control loop in the process of assisting human transportation. The power curve imitates the semi-squat movement of the human body as the output power of the hip joint. (3) Results: According to the test, the torque can be output according to the wearer’s movement. During the semi-squat lifting process, the EMG (electromyogram) signal of the vertical spine at L5/S1 was reduced by 30–48% and the metabolic cost of energy was reduced by 18% compared the situation of without EXO. (4) Conclusion: The exoskeleton joint output torque can change in an adaptive manner according to the angular velocity of the wearer’s joint. The exoskeleton can assist the waist muscles and the hip joint in the case of the reciprocating semi-squat lifting movement.

No abstract

The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through an algorithm simulation test and two-link simulation experiment, the algorithm improves the prediction accuracy by 14.23 ± 0.5%, and the sensor data is smooth. Input the predicted signal into the two-link model, and use the calculated torque method to verify the prediction accuracy data and smoothness. The simulation results showed that the algorithm can predict the joint angle of the human body and can be used for the follow-up control of the swinging legs of the exoskeleton.

Inspired by the visual properties of the human eyes, the depth information of visual attention is integrated into the saliency detection to effectively solve problems such as low accuracy and poor stability under similar or complex background interference. Firstly, the improved SLIC algorithm was used to segment and cluster the RGBD image. Secondly, the depth saliency of the image region was obtained according to the anisotropic center-surround difference method. Then, the global feature saliency of RGB image was calculated according to the colour perception rule of human vision. The obtained multichannel saliency maps were weighted and fused based on information entropy to highlighting the target area and get the final detection results. The proposed method works within a complexity of O(N), and the experimental results show that our algorithm based on visual bionics effectively suppress the interference of similar or complex background and has high accuracy and stability.