Design and Analysis of a Lower Limb Rehabilitation Training Component for Bedridden Stroke Patients

Research on Lower Limb Step Speed Recognition Method Based on Electromyography

Wearable exoskeletons play an important role in people's lives, such as helping stroke and amputation patients to carry out rehabilitation training and so on. How to make the exoskeleton accurately judge the human action intention is the basic requirement to ensure that it can complete the corresponding task. Traditional exoskeleton control signals include pressure values, joint angles and acceleration values, which can only reflect the current motion information of the human lower limbs and cannot be used to predict motion. The electromyography (EMG) signal always occurs before a certain movement; it can be used to predict the target's gait speed and movement as the input signal. In this study, the generalization ability of a BP neural network and the timing property of a hidden Markov chain are used to properly fuse the two, and are finally used in the research of this paper. Experiments show that, using the same training samples, the recognition accuracy of the three-layer BP neural network is only 91%, while the recognition accuracy of the fusion discriminant model proposed in this paper can reach 95.1%. The results show that the fusion of BP neural network and hidden Markov chain has a strong solving ability for the task of wearable exoskeleton recognition of target step speed.

Human Motion Pattern Recognition and Feature Extraction: An Approach Using Multi-Information Fusion

An exoskeleton is a kind of intelligent wearable device with bioelectronics and biomechanics. To realize its effective assistance to the human body, an exoskeleton needs to recognize the real time movement pattern of the human body in order to make corresponding movements at the right time. However, it is of great difficulty for an exoskeleton to fully identify human motion patterns, which are mainly manifested as incomplete acquisition of lower limb motion information, poor feature extraction ability, and complicated steps. Aiming at the above consideration, the motion mechanisms of human lower limbs have been analyzed in this paper, and a set of wearable bioelectronics devices are introduced based on an electromyography (EMG) sensor and inertial measurement unit (IMU), which help to obtain biological and kinematic information of the lower limb. Then, the Dual Stream convolutional neural network (CNN)-ReliefF was presented to extract features from the fusion sensors’ data, which were input into four different classifiers to obtain the recognition accuracy of human motion patterns. Compared with a single sensor (EMG or IMU) and single stream CNN or manual designed feature extraction methods, the feature extraction based on Dual Stream CNN-ReliefF shows better performance in terms of visualization performance and recognition accuracy. This method was used to extract features from EMG and IMU data of six subjects and input these features into four different classifiers. The motion pattern recognition accuracy of each subject under the four classifiers is above 97%, with the highest average recognition accuracy reaching 99.12%. It can be concluded that the wearable bioelectronics device and Dual Stream CNN-ReliefF feature extraction method proposed in this paper enhanced an exoskeleton’s ability to capture human movement patterns, thus providing optimal assistance to the human body at the appropriate time. Therefore, it can provide a novel approach for improving the human-machine interaction of exoskeletons.

Force Transmission Analysis and Optimization of Bowden Cable on Body in a Flexible Exoskeleton

The Bowden cable is a significant force transmission equipment for a flexible exoskeleton. However, the previous researches of Bowden cable had emphasized on the data from experimenting test board, instead of on human body, which produced the inaccurate assisting analysis of the flexible exoskeleton. In this paper, a flexible exoskeleton for assisting knee extension was proposed, which provided an on-body condition. Then, the friction force and its influencing factors between the wire rope and sheath of the Bowden cable from the motor to the anchor of knee have been analyzed. The segment models of force transmission with the concern of three kinds of friction modes were established, and the relationship between various lengths and bending angles of Bowden cable was fitted to the equations of curve. Furthermore, the association rule between the force transmission and the lengths of Bowden cable was obtained, based on which, the optimal force transmission efficiency was 78.68% when the length value of the Bowden cable was 475 mm. A flexible exoskeleton prototype was assembled; then, the experiments with force transmission and metabolic cost have been developed. The results showed that the force transmission efficiency had strong association with the lengths of Bowden cable, instead of the transmission velocities. Furthermore, this knee assistance exoskeleton reduced the net metabolic cost of the testees during walking. These experiments results corroborated the force transmission modeling and simulation of the Bowden cable on body we proposed in this paper.

Design and Control of Advanced Mechatronics Systems

Research and experiments on mechatronics systems, which are a synergistic integration of mechanical engineering, electronic control, and systems concepts, have contributed significantly to the design of systems, devices, processes, and products [...]

Electrically Driven Lower Limb Exoskeleton Rehabilitation Robot Based on Anthropomorphic Design

To help people with impairment of lower extremity movement regain the ability to stand and walk, and to enhance limb function, this study proposes an anthropomorphic design of an electrically driven, lower-limb exoskeleton rehabilitation robot. The angular range of the robot’s motion was determined according to the characteristics of the targeted lower-limb joints; the robot was given an active–passive anthropomorphic design with 12 degrees of freedom. The multi-degree-of-freedom hip exoskeleton, bionic artificial knee exoskeleton and passive rigid-flexible coupling ankle exoskeleton can assist patients in rehabilitation exercises with better wear comfort and exercise flexibility. A kinetic model of the seven-rod lower-limb exoskeleton rehabilitation robot was built, and data analysis of the dynamically captured motion trajectory was conducted. These provided a theoretical basis for gait planning and the control system of the lower-limb exoskeleton rehabilitation robot. The results show that the lower-limb exoskeleton rehabilitation robot system possesses sound wearing comfort and movement flexibility, and the degree of freedom of movement of the exoskeleton robot matches well with that of human movement. The robot can thus provide effective assistance to patients’ standing and walking rehabilitation training.