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A Pilot Study on Fabric-based Pneumatic Soft Gloves for Assisting Patients with Severe Brachial Plexus Injury

IEEE Transactions on Biomedical engineering 2025年 PP卷 PP页

作者： Feng, Miao Yang, Dezhi Sun, Jianing Lao, Jie Rui, Jing Gu, Guoying Shanghai Jiao Tong University Robotics Institute State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Key Laboratory of Intelligent Robotics Meta Robotics Institute Shanghai200240 China Southwest Technology and Engineering Research Institute Chongqing400039 China Fudan University Department of Hand Surgery Huashan Hospital China NHC Key Laboratory of Limbs Reconstruction Shanghai Key Laboratory of Peripheral Nerve and Microsurgery Shanghai200040 China

Objective: Robotic gloves show promise in hand assistance due to their wearability and home-based potential, yet empirical research remains limited. This pilot study presents a fabric-based pneumatic soft glove, aiming to identify its potential and challenges in clinical practice by evaluating its effectiveness in assisting patients with severe brachial plexus injury (BPI). Methods: The glove integrates a thumb abduction actuator and four bidirectional fabric-based pneumatic actuators (FPAs) with asymmetric chambers for high output force. Sixteen healthy volunteers and five individuals with BPI, all of whom lacked active hand and wrist movements, were recruited. Participants performed object grasping across 25 cm. The healthy group performed seven tasks using objects weighing up to 2 kg, with muscle activities recorded for analysis. The BPI group further performed tasks with eight objects from the action research arm test (ARAT) and twelve objects for activities of daily living (ADLs), encompassing various sizes, weights, and geometries. Results: In the healthy group, sEMG showed a decrease in 89.3% of trials, with 56.0% of these decreases being significant (p © 1964-2012 IEEE.

关键词： Pneumatic actuators

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Muscle Activation Estimation by Optimizing the Musculoskeletal Model for Personalized Strength and Conditioning Training

arXiv

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arXiv 2025年

作者： Wu, Xi Li, Chenzui Zou, Kehan Xi, Ning Chen, Fei Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Department of Industrial and Manufacturing System Engineering The University of Hong Kong Hong Kong

Musculoskeletal models are pivotal in the domains of rehabilitation and resistance training to analyze muscle conditions. However, individual variability in musculoskeletal parameters and the immeasurability of some internal biomechanical variables pose significant obstacles to accurate personalized modelling. Furthermore, muscle activation estimation can be challenging due to the inherent redundancy of the musculoskeletal system, where multiple muscles drive a single joint. This study develops a whole-body musculoskeletal model for strength and conditioning training and calibrates relevant muscle parameters with an electromyography-based optimization method. By utilizing the personalized musculoskeletal model, muscle activation can be subsequently estimated to analyze the performance of exercises. Bench press and deadlift are chosen for experimental verification to affirm the efficacy of this approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Electromyography

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Deep reinforcement learning-based trajectory planning for double pendulum cranes: Design and experiments

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Mechanical systems and Signal Processing 2025年 234卷

作者： Ding, Weili Zhang, Heng Hua, Changchun Lu, Biao Engineering Research Center of Intelligent Control System and Intelligent Equipment Ministry of Education Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Institute of Electrical Engineering Yanshan University Qinhuangdao066004 China Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

Since cranes usually possess double pendulum dynamics, the mass of the payload often changes and there are frequently lifting/lowering operations simultaneously. Moreover, most crane systems are driven by motors, whose velocities and accelerations are often limited. To solve the above problems, this paper proposes a deep reinforcement learning (DRL) reference trajectory generation method based on virtual–physical joint training. Firstly, a DRL module based on deep deterministic policy gradient (DDPG), along with a double pendulum crane dynamic model and an adaptive controller, are established within the virtual environment for training the reference trajectory. When the reward reaches the threshold, the training is switched to the physical environment to further optimize the reference trajectory, realizing the swing suppression of the hook and the payload. In addition, to satisfy the performance of the drive motors, velocity and acceleration thresholds are set to constrain the performance of the drive motors. Finally, in view of the fact that the operation process of the double pendulum crane is divided into payload transportation and payload loading/unloading, an event-triggering mechanism is designed to switch different control policies in accordance with different operation processes, thus reducing the consumption of computing resources. Through experiments on the actual double pendulum crane and comparison with the existing reference trajectories and input shapers, the superiority of this method is demonstrated. © 2025 Elsevier Ltd

关键词： Model reference adaptive control

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Domain-Adaptive Power Profiling Analysis Strategy for the Metaverse

Domain-Adaptive Power Profiling Analysis Strategy for the Me...

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作者： Li, Xiang Yang, Ning Liu, Weifeng Chen, Aidong Zhang, Yanlong Wang, Shuo Zhou, Jing Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Institute of Semiconductors Chinese Academy of Sciences Beijing China College of Robotics Beijing Union University Beijing China Research Center for Multi-Intelligent Systems Beijing Union University Beijing China Information System Laboratory Beijing Microelectronics Technology Institution Beijing China

In the surge of the digital era, the metaverse, as a groundbreaking concept, has become a focal point in the technology sector. It is reshaping human work and life patterns, carving out a new realm of virtual and real interaction. However, the rapid development of the metaverse brings along novel challenges in security and privacy. In this multifaceted and complex technological environment, data protection is of paramount importance. The innovative capabilities of high-end devices and functions in the metaverse, owing to advanced integrated circuit technology, face unique threats from side-channel analysis (SCA), potentially leading to breaches in user privacy. Addressing the issue of domain differences caused by different hardware devices, which impact the generalizability of the analysis model and the accuracy of analysis, this paper proposes a strategy of portability power profiling analysis (PPPA). Combining domain adaptation and deep learning techniques, it models and calibrates the domain differences between the profiling and target devices, enhancing the model's adaptability in different device environments. Experiments show that our method can recover the correct key with as few as 389 power traces, effectively recovering keys across different devices. This paper underscores the effectiveness of cross-device SCA, focusing on the adaptability and robustness of analysis models in different hardware environments, thereby enhancing the security of user data privacy in the metaverse environment. © 2024 John Wiley & Sons Ltd.

关键词： Data privacy

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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Driving mechanism of the morphology evolution involved in the micro-defect healing process of fused silica optics under the non-evaporative CO2 laser irradiation

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Optics and Laser Technology 2025年 188卷

作者： Yang, Zican Xu, Hongguang Zhao, Linjie Cheng, Jian Chen, Mingjun Liu, Zhichao Wang, Shengfei Geng, Feng Xu, Qiao State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Department of Mechanical Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou450041 China Research Center of Laser Fusion China Academy of Engineering Physics Chengdu610041 China

Currently, the machining of hard and brittle fused silica material still relies on material removal by contact-based machining tools. Due to the inherent unevenness of grinding wheels and abrasives, surface defects inevitably persist on precision fused silica optics. Recently, the non-contact, non-destructive CO2 laser processing of fused silica begins to attract research attentions. Through the spontaneous micro-flow of the molten materials, the fused silica surface can be smoothed and the defects can be healed without mass loss. However, the micro-flow and material rearrangement in the defect area under laser irradiation involves complex physical interactions such as laser energy deposition, heat transfer, fluid dynamics, and volumetric strain. The contribution mechanisms to the morphological evolution remain unclear, preventing precise control of defects and the achievement of high-quality surfaces. In this work, a new numerical model coupling volume strain with the traditional heat transfer and micro-flow models is established. The computational model shows a high degree of consistency with the experimental results, with a deviation of less than 5%. The contribution of various thermo-mechanical effects, including surface tension, Marangoni force and thermal modification, to the evolution of surface morphology during the defect healing process was revealed. It is found that, the surface tension and thermal strain are the primary factors dominating the surface deformation. Both calculated and experimental results demonstrated that, surface tension is the dominant contributor to the defect healing behavior, with the contribution of the other forces to the defect morphology being less than 1% of that of surface tension. This work can provide guidance for the regulation of the defect healing and other laser processing procedures of fused silica optics. © 2025 Elsevier Ltd

关键词： Irradiation

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Modelling and experimental investigation of micro-dimpled structures milling with spiral trajectory tool reciprocating motion

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Chinese Journal of Aeronautics 2025年第02期 581-600页

作者： Guangzhou WANG Linjie ZHAO Qi LIU Xiguang LI Yazhou SUN Mingjun CHEN School of Mechatronics Engineering Harbin Institute of Technology Centre for Precision Manufacturing DMEMUniversity of Strathclyde State Key Laboratory of Robotics and System Harbin Institute of Technology

To mill fine and well-defined micro-dimpled structures, a machining manner of spiral trajectory tool reciprocating motion, where the tool repeats the process of ‘feed milling±retract±cutting feed±feed milling again' along the spiral trajectory, was proposed. From the kinematics analysis, it is found that the machining quality of micro-dimpled structures is highly dependent on the machining trajectory using spiral trajectory tool reciprocating motion. To reveal this causation, simulation modelling and experimental studies were carried out. A simulation model was developed to quantitatively and qualitatively investigate the influence of the trajectory discretization strategies(constant-angle and constant-arc length) and parameters(discrete angle, discrete arc length, and pitch) on surface texture and residual height of micro-dimpled structures. Subsequently, microdimpled structures were milled under different trajectory discretization strategies and parameters with spiral trajectory tool reciprocating motion. A comprehensive comparison between the milled results and simulation analysis was made based on geometry accuracy, surface morphology and surface roughness of milled dimples. Meanwhile, the errors and factors affecting the above three aspects were analyzed. The results demonstrate both the feasibility of the established simulation model and the machining capability of this machining way in milling high-quality micro-dimpled structures. Spiral trajectory tool reciprocating motion provides a new machining way for milling micro-dimpled structures and micro-dimpled functional surfaces. And an appropriate machining trajectory can be generated based on the optimized trajectory parameters, thus contributing to the improvement of machining quality and efficiency.

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Modeling and design of 3D printed hyperelastic lattice metamaterials with bionic S-shaped stress-strain behaviors

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Materials Horizons 2025年

作者： Dong, Le Zhang, Mengjie Wang, Dong State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory of Intelligent Robotics Meta Robotics Institute Shanghai Jiao Tong University Shanghai200240 China

Lattice metamaterials made of stiff polymers, ceramics, and metals have been extensively designed to reproduce the mechanical behaviors of biological tissues, holding promising applications in biomedical devices and tissue engineering. However, lattice metamaterials composed of soft materials have been far less explored due to challenges posed by material nonlinearity and large deformations. Here, hyperelastic lattice metamaterials with curved microstructures are fabricated by 3D printing elastomers and are developed to mimic bionic S-shaped stress-strain behaviors. We propose a design framework for 3D printed hyperelastic lattice metamaterials that integrates digital geometry generation, hierarchical mechanics modeling, and validation by finite element (FE) simulations and experiments. The microstructures are modeled through deriving a Timoshenko-type beam theory governed by hyperelastic strain energy potentials. The model is then combined with the deformation and equilibrium analysis considering non-rigid connections between microstructures to predict the mechanical responses of hyperelastic lattice metamaterials. Using the developed design framework, programmable S-shaped stress-strain behaviors and high fracture strains (over 800%) are achieved. We demonstrate S-shaped stress-strain curves that match skeletal and cardiac muscles and highly stretchable lattice sensors for remote controls. This study provides design methods and theoretical guidelines for hyperelastic lattice metamaterials, holding promise for robotic sensors with bionic performance and functionality. © The Royal Society of Chemistry 2025.

关键词： Stress-strain curves

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Ultrahigh Energy Density and Rapid Response Dielectric Elastomer for Advanced Soft Robots

SSRN

引用

SSRN 2025年

作者： Du, Chengzhen Yan, Peinan Zou, Jiang Guo, Yutong Pang, Zimo Li, Sijia Chen, Yujie Fan, Qunfu Liu, Hezhou State Key Laboratory of Metal Matrix Composites School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Robotics Institute State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China National Engineering Research Center of Special Equipment and Power System for Ship and Marine Engineering Shanghai200030 China

Dielectric elastomer actuators (DEAs) require high energy density to simultaneously achieve high mechanical output while enabling compact and lightweight actuator design. However, the realization of high energy density is constrained by the high driving electric field and the significant hysteretic relaxation. Herein, we present a DE featuring a bimodal-network structure and multiple hydrogen bond interactions, which possesses outstanding properties, including a low Young's modulus, optimal dielectric properties, and low mechanical loss. The DE achieves a maximum actuation strain of 200% at a low driving electric field of 60 V μm-1 with a rapid response. Remarkably, it exhibits an output force of 0.95 N and an ultrahigh specific energy of 283 J kg-1, approximately 7 times that of natural muscle. A series of soft robots with different structures and functions are fabricated to showcase the immense potential of our DE in various applications. This work provides a novel strategy to fabricate high performance artificial muscles for soft robotics. © 2025, The Authors. All rights reserved.

关键词： Elastomers

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Exploring Eye-tracking based Biomarkers to Assess Cognitive Abilities in Autistic Children: A Feasibility Study

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Zhang, Hanlin Hu, Chunchun Wang, Zhiyong Zhou, Bingrui Wang, Xinming Nie, Wei Ye, Qinyi Lin, Ruihan Xu, Xiu Liu, Honghai Children's Hospital of Fudan University Department of Child Health Care Shanghai201102 China Harbin Institute of Technology State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Shenzhen518055 China Peng Cheng Laboratory Shenzhen518000 China

Cognitive assessment can reveal a person's cognitive processing and behavioral patterns, making it an indispensable component of autism intervention and prognosis. Existing machine-assisted cognitive assessment methods primarily focus on children's performance outcomes, overlooking distinctive behavioral models, particularly characteristics of eye movement behavior, which have been demonstrated as the most direct indicators of cognitive abilities. In this study, we explore eye-tracking biomarkers for assisting cognitive assessment through a series of meticulously designed multi-level human-computer interaction protocols, encompassing three cognitive abilities: pairing and categorization, emotion recognition, and social interaction. A platform embedded with an eye-tracking module has been developed to reliably collect and analyze eye movement data, even in the presence of unrestricted large head movements in children. Experimental results indicate that there are significant group differences between autism and typically developing children in the eye-tracking features of total fixation duration, response latency, time to first fixation, mean fixation duration, and visit count in the absence of significant intergroup differences in the Wechsler Preschool and Primary Scale of Intelligence (WPPSI) and Wechsler Intelligence Scale for Children (WISC) assessment results. In addition, certain eye movement features in each group are correlated with WPPSI/WISC scale scores, enabling clinical cognitive assessments within each group based on these eye movement features. This study suggests that using eye-tracking features as biomarkers to assist detailed cognitive assessments holds significant potential for the intervention and prognosis of autism. © 2013 IEEE.

关键词： Diseases

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