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Design of a Novel exoskeleton with Passive Magnetic Spring Self-locking and Spine Lateral Balancing

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Journal of Bionic Engineering 2024年第1期21卷 236-255页

作者： Jhon F.Rodríguez-León Betsy D.M.Chaparro-Rico Daniele Cafolla Francesco Lago Eduardo Castillo-Castañeda Giuseppe Carbone Centro de Investigacion en Ciencia Aplicada y Tecnologia Avanzada Unidad Querétaro MecatronicaInstituto Politécnico NacionalCerro Blanco 14176090 Colinas del CimatarioQuerétaroMexico Department of Mechanical Energy and Management EngineeringUniversity of CalabriaVia P.Bucci Cubo 46 C87036 RendeItaly Faculty of Science and Engineering Department of Computer ScienceSwansea UniversitySwanseaUK CESTER University of Cluj-NapocaCluj-NapocaRomania

This paper proposes a new upper-limb exoskeleton to reduce worker physical strain.The proposed design is based on a novel PRRRP(P-Prismatic;R-Revolute)kinematic chain with 5 passive Degrees of Freedom(DoF).Utilizing a magnetic spring,the proposed mechanism includes a specially designed locking mechanism that maintains any desired task posture.The proposed exoskeleton incorporates a balancing mechanism to alleviate discomfort and spinal torsional effects also helping in limb weight relief.This paper reports specific models and simulations to demonstrate the feasibility and effectiveness of the proposed design.An experimental characterization is performed to validate the performance of the mechanism in terms of forces and physical strain during a specific application consisting of ceiling-surface drilling tasks.The obtained results preliminarily validate the engineering feasibility and effectiveness of the proposed exoskeleton in the intended operation task thereby requiring the user to exert significantly less force than when not wearing it.

关键词： Assistive mechanism exoskeleton Locking mechanism Bionic robot

STGNN-LMR:A Spatial–Temporal Graph Neural Network Approach Based on sEMG Lower Limb Motion Recognition

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Journal of Bionic Engineering 2024年第1期21卷 256-269页

作者： Weifan Mao Bin Ma Zhao Li Jianxing Zhang Yizhou Lu Zhuting Yu Feng Zhang Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China University of Chinese Academy of Sciences Beijing 101408China University of Science and Technology of China Hefei 230026China Shanghai Zhongyan Jiuyi Technology Co. Ltd.Shanghai 201821China Academy of Military Science Bejing 100091China

Lower limb motion recognition techniques commonly employ Surface Electromyographic Signal(sEMG)as input and apply a machine learning classifier or Back Propagation Neural Network(BPNN)for classification.However,this artificial feature engineering technique is not generalizable to similar tasks and is heavily reliant on the researcher’s subject expertise.In contrast,neural networks such as Convolutional Neural Network(CNN)and Long Short-term Memory Neural Network(LSTM)can automatically extract features,providing a more generalized and adaptable approach to lower limb motion recognition.Although this approach overcomes the limitations of human feature engineering,it may ignore the potential correlation among the sEMG channels.This paper proposes a spatial–temporal graph neural network model,STGNN-LMR,designed to address the problem of recognizing lower limb motion from multi-channel sEMG.STGNN-LMR transforms multi-channel sEMG into a graph structure and uses graph learning to model spatial–temporal features.An 8-channel sEMG dataset is constructed for the experimental stage,and the results show that the STGNN-LMR model achieves a recognition accuracy of 99.71%.Moreover,this paper simulates two unexpected scenarios,including sEMG sensors affected by sweat noise and sudden failure,and evaluates the testing results using hypothesis testing.According to the experimental results,the STGNN-LMR model exhibits a significant advantage over the control models in noise scenarios and failure scenarios.These experimental results confirm the effectiveness of the STGNN-LMR model for addressing the challenges associated with sEMG-based lower limb motion recognition in practical scenarios.

关键词： Lower limb motion recognition exoskeleton sEMG.Graph neural network Noise Sensor failure

Robust Machine Learning Mapping of sEMG Signals to Future Actuator Commands in Biomechatronic Devices

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Journal of Bionic Engineering 2024年第1期21卷 270-287页

作者： Ali Nasr Sydney Bell Rachel L.Whittaker Clark R.Dickerson John McPhee Systems Design Engineering University of WaterlooWaterloo N2L 3G1Canada Kinesiology and Health Sciences University of WaterlooWaterloo N2L3G3Canada

A machine learning model for regression of interrupted Surface Electromyography(sEMG)signals to future control-oriented signals(e.g.,robot’s joint angle and assistive torque)of an active biomechatronic device for high-level myoelectric-based hierarchical control is proposed.A Recurrent Neural Network(RNN)was trained using output data,initially obtained from offline optimization of the biomechatronic(human–robot)device and shifted by the prediction horizon.The input of the RNN consisted of interrupted sEMG signals(to mimic signal disconnections)and previous kinematic signals of the assistive system.The RNN with a 0.1-s prediction horizon could predict the control-oriented joint angle and assistive torque with 92%and 86.5%regression accuracy,respectively,for the test dataset.This proposed approach permits a fast,predictive,and direct estimation of control-oriented signals instead of an iterative process that optimizes assistive torque in the inverse dynamic simulation of a multibody human–robot system.Training with these interrupted input signals significantly improves the regression accuracy in the case of sEMG signal disconnection.This Robust Predictive Control-oriented Machine Learning(Robust-MuscleNET)model can support volitional high-level myoelectric-based control of biomechatronic devices,such as exoskeletons,prostheses,and assistive/resistive robots.Future work should study the application to prosthesis control as well as the repeatability of the high-level controller with electrode shift.The low-level hierarchical controller that manages the human–robot interaction,the assistance/resistance strategy,and the actuator coordination should also be studied.

关键词： Myoelectric-based control-Surface electromyography Machine learning Multibody system dynamics exoskeleton Bionic

State of the art in movement around a remote point:a review of remote center of motion in robotics

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Frontiers of Mechanical Engineering 2024年第2期19卷 1-28页

作者： Wuxiang ZHANG Zhi WANG Ke MA Fei LIU Pengzhi CHENG Xilun DING School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China Ningbo Institute of Technology Beihang UniversityNingbo 315832China Intelligent Aerospace Manufacturing Engineering Technology Co. Ltd.Beijing 100191China

The concept of remote center of motion(RCM)is pivotal in a myriad of robotic applications,encompa-ssing areas such as medical robotics,orientation devices,and exoskeletal systems.The efficacy of RCM technology is a determining factor in the success of these robotic domains.This paper offers an exhaustive review of RCM technologies,elaborating on their various methodologies and practical implementations.It delves into the unique characteristics of RCM across different degrees of freedom(DOFs),aiming to distill their fundamental principles.In addition,this paper categorizes RCM approaches into two primary classifications:design based and control based.These are further organized according to their respective DOFs,providing a concise summary of their core methodologies.Building upon the understanding of RCM’s versatile capabilities,this paper then transitions to an in-depth exploration of its applications across diverse robotic fields.Concluding this review,we critically analyze the existing research challenges and issues that are inherently present in both RCM methodologies and their applications.This discussion is intended to serve as a guiding framework for future research endeavors and practical deployments in related areas.

关键词： remote center of motion mechanism robotics medical robot orientation device exoskeleton

Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system

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Journal of Systems Engineering and Electronics 2024年第3期35卷 769-779页

作者： QIN Weiwei GUO Wenxin HU Chen LIU Gang SONG Tainian School of Nuclear Engineering Rocket Force University of EngineeringXi’an 710025China

This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.

关键词： human motion capture kinematic calibration exoskeleton gyroscopic drift expectation maximization(EM)

Human-exoskeleton Joint Coordination Assessment:A Case Study on the Shoulder and Elbow Joints

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Journal of Bionic Engineering 2022年第6期19卷 1712-1721页

作者： Pablo Delgado Clarissa Rincon Yimesker Yihun Mechanical Engineering Wichita State University1845 FairmountWichitaKS67260USA Biomedical Engineering Wichita State University1845 FairmountWichitaKS67260USA

This study is aimed at developing a methodology to assess and quantify the human limb motions and interactions with the exoskeleton in relation to alignment.Three different basic and common upper arm joint movements and their interaction with a joint-based exoskeleton are considered:shoulder vertical and horizontal abduction-adduction,and elbow flexion-extension.The exoskeleton and the human model are aligned to the respective joints inside a Musculoskeletal Modeling software.Within the range of motion,the linear and angular displacement errors were analyzed,and the effect of those errors on the length of the associated tendons was studied.Results have shown a noticeable variation of the muscle-tendon lengths up to 65.7 mm and a change in the pattern on different muscle groups of the shoulder.Similarly,about 4 mm muscle-tendon length changes observed particularly at the elbow anconeus muscle-tendon.These changes of length could cause unwanted stresses at the joints,specially for people with disabilities due to stroke that might not have the flexibility to accommodate those extra pose variations imposed by the exoskeleton.

关键词： exoskeleton Upper limb Rehabilitation Misalignment OpenSim Computational modeling Bioinspiration

Design of the Pneumatic Pressure Smart Shoes for an Ankle-Assisted exoskeleton

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Journal of Bionic Engineering 2023年第4期20卷 1613-1625页

作者： Tianwen Yao Jiliang Lv Liang Yang Aimin Xu Shengguan Qu School of Mechanical and Automotive Engineering South China University of TechnologyGuangzhou510640China

Ankle injury is one of the most common joint diseases that people experience during exercise.Most people have suffered ankle injuries at least once in their lives.The studies have shown that the ankle joint provides the most power and torques during the act of walking,compared to the knee and hip joints.This paper presents an ankle joint exoskeleton device,which is mainly used to provide assistance and protection to the human ankle joint with a pneumatic assist drive during walking.The pneumatic pressure smart shoes for this ankle exoskeleton were designed for detecting the human gaits to control the exoskeleton with certain supporting forces to the ankle joints at the appropriate timing.Each smart shoe has two sensors placed in between the wearable layer and the sole.The changes of the foot pressures were measured by the sensors for a microcontroller to control the exoskeleton.Two sets of experimental tests which were 2-month trials and gait selection were used to test the shoes.The experiments of 2-month trials were made to evaluate the stability of the shoes.The results showed that the shoes had no damages,no air leakage,and no malfunctions after the trials.The trials of gait selection were made to test the recognition rate which reached at 99.9%for the shoe system.The results showed that the design of the pneumatic smart shoes for the ankle-assisted exoskeleton met the requirements.

关键词： exoskeleton Pneumatic pressure sensor Gait detection Ankle joint Bionic

Design and Control of a Quasi-direct Drive Actuated Knee exoskeleton

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Journal of Bionic Engineering 2022年第3期19卷 678-687页

作者： Yi Long Yajun Peng Foshan Graduate School of Northeastern University Foshan528000China Faculty of Robot Science and Engineering Northeastern UniversityShenyang110000China Zhongshan Walkbest Intelligent Medical Device Co.Ltd Zhongshan528400China

This paper describes the design and control of a portable and lightweight knee exoskeleton for people with knee dysfunction.The knee exoskeleton is designed based on our custom quasi-direct drive actuation composed of a DC motor unit and a transmission mechanism encompassing three gears.An online gait generation method based on gait step calculation and direct measurement using Inertial Measurement Unit(IMU)sensors is proposed to provide continuous assistance during walking.Based on the generated gait trajectory online,Active Disturbance Rejection Control(ADRC)incorporating the feedforward compensation approach is proposed to help the human leg move in consideration of external disturbances.The developed knee exoskeleton has been employed successfully to assist impaired users with knee dysfunction to walk in a health recovery center.The experimental results indicate that the online gait generation method and the proposed control method are suitable for the knee exoskeleton.The maximum value of the target knee angular position is approximately 50°and the mean of the control torque for the knee joint is located in the interval of[−2 Nm,4 Nm].The developed knee exoskeleton has the potential to regain normal gait to improve strength and endurance during walking in their activities of daily life.

关键词： exoskeleton Knee dysfunction Quasi-direct drive Active disturbance rejection control Gait generation

Real-Time Control Strategy of exoskeleton Locomotion Trajectory Based on Multi-modal Fusion

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Journal of Bionic Engineering 2023年第6期20卷 2670-2682页

作者： Tao Zhen Lei Yan College of Engineering Beijing Forestry UniversityNo.35East Qinghua RoadBeijing100083China

The exoskeleton robot is a typical man–machine integration system in the human loop.The ideal man–machine state is to achieve motion coordination,stable output,strong personalization,and reduce man–machine confrontation during motion.In order to achieve an ideal man–machine state,a Time-varying Adaptive Gait Trajectory Generator(TAGT)is designed to estimate the motion intention of the wearer and generate a personalized gait trajectory.TAGT can enhance the hybrid intelligent decision-making ability under human–machine collaboration,promote good motion coordination between the exoskeleton and the wearer,and reduce metabolic consumption.An important feature of this controller is that it utilizes a multi-layer control strategy to provide locomotion assistance to the wearer,while allowing the user to control the gait trajectory based on human–robot Interaction(HRI)force and locomotion information.In this article,a Temporal Convolutional Gait Prediction(TCGP)model is designed to learn the personalized gait trajectory of the wearer,and the control performance of the model is further improved by fusing the predefined gait trajectory method with an adaptive interactive force control model.A human-in-the-loop control strategy is formed with the feedback information to stabilize the motion trajectory of the output joints and update the system state in real time based on the feedback from the inertial and interactive force signal.The experimental study employs able-bodied subjects wearing the exoskeleton for motion trajectory control to evaluate the performance of the proposed TAGT model in online adjustments.Data from these evaluations demonstrate that the controller TAGT has good motor coordination and can satisfy the subject to control the motor within a certain range according to the walking habit,guaranteeing the stability of the closed-loop system.

关键词： exoskeleton Multi-layer control strategy Human–machine collaboration Time-varying adaptive gait Hybrid intelligent