scholarly journals Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shijia Zha ◽  
Tianyi Li ◽  
Lidan Cheng ◽  
Jihua Gu ◽  
Wei Wei ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Target Position ◽  
Interaction Force ◽  
Human Motion ◽  
Sensor Data ◽  
Prediction Algorithm ◽  
Human Machine Interaction ◽  
Predictive Algorithm ◽  
Machine Interaction

The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through an algorithm simulation test and two-link simulation experiment, the algorithm improves the prediction accuracy by 14.23 ± 0.5%, and the sensor data is smooth. Input the predicted signal into the two-link model, and use the calculated torque method to verify the prediction accuracy data and smoothness. The simulation results showed that the algorithm can predict the joint angle of the human body and can be used for the follow-up control of the swinging legs of the exoskeleton.

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 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.

Design and Validation of a Compatible 3-Degrees of Freedom Shoulder Exoskeleton With an Adaptive Center of Rotation

2014 ◽  
Vol 136 (7) ◽  
Author(s):  
Hua Yan ◽  
Canjun Yang ◽  
Yansong Zhang ◽  
Yiqi Wang
Keyword(s):  
Soft Tissues ◽  
Dimensional Space ◽  
Interaction Force ◽  
Shoulder Abduction ◽  
Upper Arm ◽  
Human Machine Interaction ◽  
Center Of Rotation ◽  
Custom Made ◽  
Flexion Extension ◽  
Machine Interaction

This paper outlines an experimentally based design method for a compatible 3-DOF shoulder exoskeleton with an adaptive center of rotation (CoR) by matching the mechanical CoR with the anatomical CoR to reduce human–machine interaction forces and improve comfort during dynamic humeral motion. The spatial–temporal description for anatomical CoR motion is obtained via a specific experimental task conducted on six healthy subjects. The task is comprised of a static section and a dynamic section, both of which are recorded with an infrared motion capture system using body-attached markers. To reduce the influence of human soft tissues, a custom-made four-marker group block was placed on the upper arm instead of using discrete markers. In the static section, the position of anatomical CoR is kept stationary and calculated using a well-known functional method. Based on the static results, the dynamic section determines the statistical relationship between the dynamic CoR position and the humeral orientation using an optimization method when subjects move their upper arm freely in the sagittal and coronal planes. Based on the resolved anatomical CoR motion, a new mechanical CoR model derived from a traditional ball-and-socket joint is applied to match the experimental results as closely as possible. In this mechanical model, the CoR motion in three-dimensional space is adjusted by translating two of the three intersecting joint axes, including the shoulder abduction/adduction and flexion/extension. A set of optimal translation parameters is obtained through proper matching criterion for the two CoRs. Based on the translation parameters, a compatible shoulder exoskeleton was manufactured and compared with a traditional shoulder exoskeleton with a fixed CoR. An experimental test was conducted to validate the CoR motion adaptation ability by measuring the human–machine interaction force during passive shoulder joint motion. The results provide a promising direction for future anthropomorphic shoulder exoskeleton design.

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.

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 An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1956 ◽  
Author(s):  
Sami Kabir ◽  
Raihan Ul Islam ◽  
Mohammad Shahadat Hossain ◽  
Karl Andersson
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Integrated Approach ◽  
Sensor Data ◽  
Weather Data ◽  
Bayesian Probability ◽  
Prediction Algorithm ◽  
Rule Base ◽  
Belief Rule Base

Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

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