Multi-Channel sEMG Based Human Lower Limb Motion Intention Recognition Method*

2019 ◽  
Author(s):  
Yunfei Tao ◽  
Yuping Huang ◽  
Jigui Zheng ◽  
Jing Chen ◽  
Zhaojing Zhang ◽  
...  
Keyword(s):  
Lower Limb ◽  
Recognition Method ◽  
Intention Recognition ◽  
Motion Intention

scholarly journals A Novel Motion Intention Recognition Approach for Soft Exoskeleton via IMU

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2176
Author(s):  
Lu Zhu ◽  
Zhuo Wang ◽  
Zhigang Ning ◽  
Yu Zhang ◽  
Yida Liu ◽  
...  
Keyword(s):  
Inertial Sensor ◽  
Human Motion ◽  
Measurement Unit ◽  
Recognition Method ◽  
Metabolic System ◽  
Intention Recognition ◽  
Motion Modes ◽  
Deep Convolution Neural Network ◽  
Multi Mode ◽  
Motion Intention

To solve the complexity of the traditional motion intention recognition method using a multi-mode sensor signal and the lag of the recognition process, in this paper, an inertial sensor-based motion intention recognition method for a soft exoskeleton is proposed. Compared with traditional motion recognition, in addition to the classic five kinds of terrain, the recognition of transformed terrain is also added. In the mode acquisition, the sensors’ data in the thigh and calf in different motion modes are collected. After a series of data preprocessing, such as data filtering and normalization, the sliding window is used to enhance the data, so that each frame of inertial measurement unit (IMU) data keeps the last half of the previous frame’s historical information. Finally, we designed a deep convolution neural network which can learn to extract discriminant features from temporal gait period to classify different terrain. The experimental results show that the proposed method can recognize the pose of the soft exoskeleton in different terrain, including walking on flat ground, going up and downstairs, and up and down slopes. The recognition accuracy rate can reach 97.64%. In addition, the recognition delay of the conversion pattern, which is converted between the five modes, only accounts for 23.97% of a gait cycle. Finally, the oxygen consumption was measured by the wearable metabolic system (COSMED K5, The Metabolic Company, Rome, Italy), and compared with that without an identification method; the net metabolism was reduced by 5.79%. The method in this paper can greatly improve the control performance of the flexible lower extremity exoskeleton system and realize the natural and seamless state switching of the exoskeleton between multiple motion modes according to the human motion intention.

Control of Upper-Limb Power-Assist Exoskeleton Using a Human-Robot Interface Based on Motion Intention Recognition

2015 ◽  
Vol 12 (4) ◽  
pp. 1257-1270 ◽  
Author(s):  
Jian Huang ◽  
Weiguang Huo ◽  
Wenxia Xu ◽  
Samer Mohammed ◽  
Yacine Amirat
Keyword(s):  
Upper Limb ◽  
Intention Recognition ◽  
Motion Intention

scholarly journals Ultrasonography and electromyography based hand motion intention recognition for a trans-radial amputee: A case study

2020 ◽  
Vol 75 ◽  
pp. 45-48 ◽  
Author(s):  
Zheng Wang ◽  
Yinfeng Fang ◽  
Dalin Zhou ◽  
Kairu Li ◽  
Christophe Cointet ◽  
...  
Keyword(s):  
Hand Motion ◽  
Intention Recognition ◽  
Motion Intention

scholarly journals sEMG Based Human Motion Intention Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Li Zhang ◽  
Geng Liu ◽  
Bing Han ◽  
Zhe Wang ◽  
Tong Zhang
Keyword(s):  
Human Motion ◽  
Human Machine Interaction ◽  
Existing Problems ◽  
Model Based ◽  
Wearable Robots ◽  
Intention Recognition ◽  
Future Challenges ◽  
Bioelectrical Signal ◽  
Machine Interaction ◽  
Motion Intention

Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.

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