scholarly journals Real-Time Evaluation of the Signal Processing of sEMG Used in Limb Exoskeleton Rehabilitation System

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Baofeng Gao ◽  
Chao Wei ◽  
Hongdao Ma ◽  
Shu Yang ◽  
Xu Ma ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Training System ◽  
Human Anatomy ◽  
Semg Signal ◽  
Average Delay ◽  
Rehabilitation Training ◽  
Upper Limb Exoskeleton ◽  
Emg Signal ◽  
Exoskeleton Robot

As an important branch of medical robotics, a rehabilitation training robot for the hemiplegic upper limbs is a research hotspot of rehabilitation training. Based on the motion relearning program, rehabilitation technology, human anatomy, mechanics, computer science, robotics, and other fields of technology are covered. Based on an sEMG real-time training system for rehabilitation, the exoskeleton robot still has some problems that need to be solved in this field. Most of the existing rehabilitation exoskeleton robotic systems are heavy, and it is difficult to ensure the accuracy and real-time performance of sEMG signals. In this paper, we design a real-time training system for the upper limb exoskeleton robot based on the EMG signal. It has four main characteristics: light weight, portability, high precision, and low delay. This work includes the structure of the rehabilitation robotic system and the method of signal processing of the sEMG. An experiment on the accuracy and time delay of the sEMG signal processing has been done. In the experimental results, the recognition accuracy of the sEMG is 94%, and the average delay time is 300 ms, which meets the accuracy and real-time requirements.

Kinematics analysis and gait planning for a hemiplegic exoskeleton robot

Cobot ◽  
2022 ◽  
Vol 1 ◽  
pp. 1
Author(s):  
Pengbo Li ◽  
Can Wang ◽  
Bailin He ◽  
Jiaqing Liu ◽  
Xinyu Wu
Keyword(s):  
Teaching Method ◽  
Mechanical Design ◽  
Training System ◽  
Advanced Technology ◽  
Kinematics Analysis ◽  
Gait Planning ◽  
Rehabilitation Training ◽  
Movement Data ◽  
Exoskeleton Robot ◽  
Calculation Results

Background: As the world's aging population increases, the number of hemiplegic patients is increasing year by year. At present, in many countries with low medical level, there are not enough rehabilitation specialists. Due to the different condition of patients, the current rehabilitation training system cannot be applied to all patients. so that patients with hemiplegia cannot get effective rehabilitation training. Methods: Through a motion capture experiment, the mechanical design of the hip joint, knee joint and ankle joint was rationally optimized based on the movement data. Through the kinematic analysis of each joint of the hemiplegic exoskeleton robot, the kinematic relationship of each joint mechanism was obtained, and the kinematics analysis of the exoskeleton robot was performed using the Denavit-Hartenberg (D-H) method. The kinematics simulation of the robot was carried out in automatic dynamic analysis of mechanical systems (ADAMS), and the theoretical calculation results were compared with the simulation results to verify the correctness of the kinematics relationship. According to the exoskeleton kinematics model, a mirror teaching method of gait planning was proposed, allowing the affected leg to imitate the movement of the healthy leg with the help of an exoskeleton robot. Conclusions: A new hemiplegic exoskeleton robot designed by Shenzhen Institute of Advanced Technology (SIAT-H) is proposed, which is lightweight, modular and anthropomorphic. The kinematics of the robot have been analyzed, and a mirror training gait is proposed to enable the patient to form a natural walking posture. Finally, the wearable walking experiment further proves the feasibility of the structure and gait planning of the hemiplegic exoskeleton robot.

scholarly journals Action recognition and control of mechanical simulated arm: electromyographic signal detection

2020 ◽  
Vol 11 ◽  
pp. 10
Author(s):  
Genlai Lv
Keyword(s):  
Real Time ◽  
Recognition Accuracy ◽  
Human Motion ◽  
Real Time Control ◽  
Semg Signal ◽  
Zero Crossing ◽  
Training Time ◽  
The Real ◽  
Time Control ◽  
Emg Signal

Electromyography (EMG) signal contains a large amount of human motion information, which can be used to classify human actions. In this study, based on the detection of surface electromyography (sEMG) signal, three actions were designed, the sEMG signal was collected by the EMG acquisition system. Four feature values, including root-mean-square value, average absolute value (MAV), wavelength, and Zero crossing point, were extracted from the signal. Then these values were taken as the input of Back-Propagation neural network (BPNN) to recognize different actions, thereby realizing the real-time control of mechanical simulated arm. The experiment found that the training time of the BPNN method designed in this study was short, 11.36 s, and the average recognition accuracy rate reached 92.2%. In the real-time control experiment of mechanical simulated arm, the recognition accuracy of different actions reached more than 90%, and the running time was short. The experimental results verifies the effectiveness of the proposed method and make some contributions to the efficient control of the mechanical simulation arm.

scholarly journals Automatic Detection of Noisy Signals in sEMG Grids Using Statistical Thresholding

2021 ◽  
Vol 58 (1) ◽  
Author(s):  
Khalil Ullah ◽  
Khalid Shah
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Visual Inspection ◽  
Automatic Detection ◽  
Ground Truth ◽  
Noisy Channels ◽  
Perfect Agreement ◽  
Emg Signal ◽  
Signal Processing Algorithms ◽  
Processing Algorithms

Electromyogram (EMG) signal is often processed offline, after its acquisition, using digital signal processing algorithms to extract muscle anatomical and physiological information. As most of the signal processing algorithms work on an adequate quality of the signals, thus quality checking of the EMG in real-time during its acquisition is of immense importance. In multi-channel sEMG signals, usually there are some noisy or bad channels. If the noise is of low level, it is of little concern but high level of noise can limit the usefulness of the EMG. To make sure acquisition of a good quality EMG signal in terms of SNR, one way to detect noisy channels is through visual inspection by an expert human operator, however visual inspection of multiple electrodes in real-time is not possible and is also expensive both in terms of time and cost. In this research study, we propose a novel method for automatic detection of noisy channels in multi-channel surface EMG signals based on statistical thresholding of several parameters. The results of the proposed method are in perfect agreement with the ground truth for simulated EMG signals, with an accuracy of 98.6%.

scholarly journals Design of Rehabilitation Training System for Pelvic Floor Dysfunction

2021 ◽  
Vol 96 ◽  
pp. 01009
Author(s):  
Yongfeng Liu ◽  
Chunna Li ◽  
Jun Zhong ◽  
Liming Cai ◽  
Kai Guo
Keyword(s):  
Electrical Stimulation ◽  
Pelvic Floor ◽  
Pelvic Floor Muscle ◽  
Pelvic Floor Dysfunction ◽  
Mode Selection ◽  
Training System ◽  
Rehabilitation Training ◽  
Female Patients ◽  
Emg Signal ◽  
Maintenance Time

Pelvic floor dysfunction has caused pain to the lives of the majority of female patients, and repeated visits to the hospital for treatment have also caused inconvenience to patients. Therefore, a portable pelvic floor dysfunction rehabilitation training system is designed to help female patients with personalized and private treatment. The pelvic floor dysfunction rehabilitation system realizes the independent training mode selection through the APP client, assists the pelvic floor muscle rehabilitation training through the electrical stimulation module, and can realize the recovery situation of the pelvic floor muscle strength in real time through the AD acquisition module. The experimental results show that the pelvic floor dysfunction rehabilitation training system implements bipolar electrical stimulation pulses, and the EMG signals collected at the same time clearly show the EMG signal strength and strength maintenance time.

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