Research on Gesture Recognition Method Based on EMG Signal and Design of Rehabilitation Training System

Author(s):  
Junyu Shi ◽  
Zhitao Dai
2021 ◽  
Vol 96 ◽  
pp. 01009
Author(s):  
Yongfeng Liu ◽  
Chunna Li ◽  
Jun Zhong ◽  
Liming Cai ◽  
Kai Guo

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.


2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Baofeng Gao ◽  
Chao Wei ◽  
Hongdao Ma ◽  
Shu Yang ◽  
Xu Ma ◽  
...  

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.


2014 ◽  
Vol 998-999 ◽  
pp. 1062-1065
Author(s):  
Hong Hu ◽  
Jian Gang Chao ◽  
Zai Qian Zhao

With the fast development of vision-based hand gesture recognition, it is possible to apply the technology to astronaut virtual training. In order to solve problems of hand gesture recognition in future virtual training and to provide an unrestricted natural training for astronauts, this paper proposed a vision-based hand gesture recognition method, and implemented a hierarchical gesture recognition system to provide a gesture-driven interactive interface for astronaut virtual training system. The experiment results showed that this recognition system can be used to help astronaut training.


2020 ◽  
Vol 29 (6) ◽  
pp. 1153-1164
Author(s):  
Qianyi Xu ◽  
Guihe Qin ◽  
Minghui Sun ◽  
Jie Yan ◽  
Huiming Jiang ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Kai Guo ◽  
Senhao Zhang ◽  
Shasha Zhao ◽  
Hongbo Yang

This work takes the production and usage scenarios of the data glove as the research object and studies the method of applying the flexible sensor to the data glove. Many studies are also devoted to exploring the transplantation of flexible sensors to data gloves. However, this type of research still lacks the display of specific application scenarios such as gesture recognition or hand rehabilitation training. A small amount of experimental data and theoretical analysis are difficult to promote the development of flexible sensors and flexible data gloves design schemes. Therefore, this study uses the self-made flexible sensor of the research group as the core sensing unit to produce a flexible data glove to monitor the bending changes of the knuckles and then use it for simple gesture recognition and rehabilitation training.


Sign in / Sign up

Export Citation Format

Share Document