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.

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.

Industry 4.0: Some Challenges and Opportunities for Reliability Engineering

2019 ◽  
Vol 2 (1) ◽  
pp. 23-34
Author(s):  
Mohammad Ali Farsi ◽  
Enrico Zio
Keyword(s):  
Industry 4.0 ◽  
Health Management ◽  
System Modeling ◽  
Cyber Physical System ◽  
Reliability Engineering ◽  
Human Machine Interaction ◽  
New Directions ◽  
Challenges And Opportunities ◽  
Future Challenges ◽  
Machine Interaction

According to the development of Industry 4.0 and increase the integration of digital, physical and human worlds, reliability engineering must evolve for addressing the existing and future challenges about that. In this paper, the principle of Industry 4.0 is presented and some of these challenges and opportunities for reliability engineering are discussed. New directions for research in system modeling, big data analysis, health management, cyber-physical system, human-machine interaction, uncertainty, jointly optimization, communication, and interfaces are proposed. Each topic can be investigated individually, but this paper summarizes them and prepared a vision about reliability engineering for consideration and discussion by the interested scientific community.

Supersensitive all-fabric pressure sensors using printed textile electrode arrays for human motion monitoring and human–machine interaction

2018 ◽  
Vol 6 (48) ◽  
pp. 13120-13127 ◽  
Author(s):  
Ziqiang Zhou ◽  
Ying Li ◽  
Jiang Cheng ◽  
Shanyong Chen ◽  
Rong Hu ◽  
...  
Keyword(s):  
Cotton Fabric ◽  
Pressure Sensors ◽  
Human Motion ◽  
Electrode Arrays ◽  
Human Machine Interaction ◽  
Silver Paste ◽  
Textile Electrode ◽  
Human Motion Monitoring ◽  
Machine Interaction ◽  
Coated Cotton

Supersensitive all-fabric pressure sensors with a bottom interdigitated textile electrode screen-printed using silver paste and a top bridge of AgNW-coated cotton fabric are successfully fabricated for human motion monitoring and human–machine interaction.

Adaptive Robust Cascade Force Control of 1-DOF Joint Exoskeleton for Human Performance Augmentation

2015 ◽  
Author(s):  
Shan Chen ◽  
Bin Yao ◽  
Zheng Chen ◽  
Xiaocong Zhu ◽  
Shiqiang Zhu
Keyword(s):  
Force Control ◽  
Motion Tracking ◽  
Human Performance ◽  
Control Method ◽  
Interaction Force ◽  
Human Motion ◽  
Adaptive Robust Control ◽  
Human Machine Interaction ◽  
High Level ◽  
Machine Interaction

The control objective of exoskeleton for human performance augmentation is to minimize the human machine interaction force while carrying external loads and following human motion. This paper addresses the dynamics and the interaction force control of a 1-DOF hydraulically actuated joint exoskeleton. A spring with unknown stiffness is used to model the human-machine interface. A cascade force control method is adopted with high-level controller generating the reference position command while low level controller doing motion tracking. Adaptive robust control (ARC) algorithm is developed for both two controllers to deal with the effect of parametric uncertainties and uncertain nonlinearities of the system. The proposed adaptive robust cascade force controller can achieve small human-machine interaction force and good robust performance to model uncertainty which have been validated by experiment.

scholarly journals A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

2021 ◽  
Vol 15 ◽  
Author(s):  
Baichun Wei ◽  
Zhen Ding ◽  
Chunzhi Yi ◽  
Hao Guo ◽  
Zhipeng Wang ◽  
...  
Keyword(s):  
Human Motion ◽  
Human Machine Interaction ◽  
Motion Patterns ◽  
Decoding Algorithms ◽  
Gait Phase ◽  
Limb Kinematics ◽  
Phase Recognition ◽  
Machine Interaction ◽  
Lower Limb Kinematics ◽  
Discrete Motion

The interaction between human and exoskeletons increasingly relies on the precise decoding of human motion. One main issue of the current motion decoding algorithms is that seldom algorithms provide both discrete motion patterns (e.g., gait phases) and continuous motion parameters (e.g., kinematics). In this paper, we propose a novel algorithm that uses the surface electromyography (sEMG) signals that are generated prior to their corresponding motions to perform both gait phase recognition and lower-limb kinematics prediction. Particularly, we first propose an end-to-end architecture that uses the gait phase and EMG signals as the priori of the kinematics predictor. In so doing, the prediction of kinematics can be enhanced by the ahead-of-motion property of sEMG and quasi-periodicity of gait phases. Second, we propose to select the optimal muscle set and reduce the number of sensors according to the muscle effects in a gait cycle. Finally, we experimentally investigate how the assistance of exoskeletons can affect the motion intent predictor, and we propose a novel paradigm to make the predictor adapt to the change of data distribution caused by the exoskeleton assistance. The experiments on 10 subjects demonstrate the effectiveness of our algorithm and reveal the interaction between assistance and the kinematics predictor. This study would aid the design of exoskeleton-oriented motion-decoding and human–machine interaction methods.

scholarly journals Human motion intention recognition based on EMG signal and angle signal

2021 ◽  
Vol 3 (1) ◽  
pp. 37-47
Author(s):  
Baixin Sun ◽  
Guang Cheng ◽  
Quanmin Dai ◽  
Tianlin Chen ◽  
Weifeng Liu ◽  
...  
Keyword(s):  
Human Motion ◽  
Emg Signal ◽  
Intention Recognition ◽  
Motion Intention
Sign in / Sign up

Export Citation Format

Share Document