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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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Realizing high-speed target tracking by using multi-rate feedforward predictive control for the acquisition, tracking, and pointing system

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Defence Technology 2025年

作者： Li, Hang Peng, Gaoliang Shan, Xiaobiao Zhao, Mingyuan Zhang, Wei Wang, Jinghan Cheng, Feng State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China College of Information Science and Technology Eastern Institute of Technology Ningbo315200 China

The acquisition, tracking, and pointing (ATP) system is widely used in target tracking, counter-UAV operations, and other related fields. As UAV technology develops, there is a growing demand to enhance the tracking capabilities of ATP systems. However, in practical applications, ATP systems face various design constraints and functional limitations, making it infeasible to indefinitely improve hardware performance to meet tracking requirements. As a result, tracking algorithms are required to execute increasingly complex tasks. This study introduces a multi-rate feedforward predictive controller to address issues such as low image feedback frequency and significant delays in ATP systems, which lead to tracking jitter, poor tracking performance, low precision, and target loss. At the same time, the proposed approach aims to improve the tracking capabilities of ATP systems for high-speed and highly maneuverable targets under conditions of low sampling feedback rates and high feedback delays. The method suggested is also characterized by its low order, fast response, and robustness to model parameter variations. In this study, an actual ATP system is built for target tracking test, and the proposed algorithm is fully validated in terms of simulation and actual system application verification. Results from both simulations and experiments demonstrate that the method effectively compensates for delays and low sampling rates. For targets with relative angular velocities ranging from 0 to 90°/s and angular accelerations between 0 and 470°/s2, the system improved tracking accuracy by 70.0%–89.9% at a sampling frequency of 50 Hz and a delay of 30 m s. Moreover, the compensation algorithm demonstrated consistent performance across actuators with varying characteristics, further confirming its robustness to model insensitivity. In summary, the proposed algorithm considerably enhances the tracking accuracy and capability of ATP systems for high-speed and highly maneuverable ta

关键词： Predictive control systems

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Synthesized colonoscopy dataset from high-fidelity virtual colon with abnormal simulation

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Computers in Biology and Medicine 2025年 186卷 109672-109672页

作者： He, Dongdong Liu, Ziteng Yin, Xunhai Liu, Hao Gao, Wenpeng Fu, Yili School of Life Science and Technology Harbin Institute of Technology Harbin150080 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Department of Gastroenterology The First Affiliated Hospital of Harbin Medical University Harbin150001 China

With the advent of the deep learning-based colonoscopy system, the need for a vast amount of high-quality colonoscopy image datasets for training is crucial. However, the generalization ability of deep learning models is challenged by the limited availability of colonoscopy images due to regulatory restrictions and privacy concerns. In this paper, we propose a method for rendering high-fidelity 3D colon models and synthesizing diversified colonoscopy images with abnormalities such as polyps, bleeding, and ulcers, which can be used to train deep learning models. The geometric model of the colon is derived from CT images. We employed dedicated surface mesh deformation to mimic the shapes of polyps and ulcers and applied texture mapping techniques to generate realistic, lifelike appearances. The generated polyp models were then attached to the inner surface of the colon model, while the ulcers were created directly on the inner surface of the colon model. To realistically model blood behavior, we developed a simulation of the blood diffusion process on the colon's inner surface and colored vertices in the traversed region to reflect blood flow. Ultimately, we generated a comprehensive dataset comprising high-fidelity rendered colonoscopy images with the abnormalities. To validate the effectiveness of the synthesized colonoscopy dataset, we trained state-of-the-art deep learning models on it and other publicly available datasets and assessed the performance of these models in abnormal classification, detection, and segmentation. Notably, the models trained on the synthesized dataset exhibit an enhanced performance in the aforementioned tasks, as evident from the results. © 2025 Elsevier Ltd

关键词： Digital elevation model

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Design of deployable bridge using multistable miura-ori structure for emergency rescue

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Automation in Construction 2025年 176卷

作者： Wang, Wei Li, Xu Yan, Peng Huang, Hailin Li, Bing School of Robotics and Advanced Manufacture Harbin Institute of Technology Shenzhen518052 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518052 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China

Rapid deployment equipment is crucial in emergency rescue operations during natural disasters such as earthquakes and floods. However, traditional equipment often has limitations such as complex operations, long deployment times, and high manpower requirements. This paper designs a deployable bridge for post-disaster rescue, with its core component being a bistable Miura-ori unit. Bistable characteristics of the Miura-ori unit are shown. Then, finite element model (FEM) and prototype experiments are used to demonstrate the load-bearing capacity and bistable characteristics of the bistable Miura-ori unit. By arranging the bistable Miura-ori units, a deployable bridge with multistable characteristics is achieved, enabling rapid deployment with a single drive. The load-bearing and impact resistance capabilities of the bridge are also tested. This design significantly simplifies system complexity, reduces labor requirements, and ensures high load-bearing capacity and impact resistance, providing a new solution for emergency rescue. © 2025 Elsevier B.V.

关键词： Emergency services

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UMIENet: Underwater image enhancement based on multi-degradation knowledge integration

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Optics and Lasers in Engineering 2025年 193卷

作者： Lv, Pin Zha, Fusheng Wang, Xiangji Li, Rongchao Li, Mantian Wang, Pengfei Guo, Wei Sun, Lining State key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Lanzhou University of Technology Lanzhou China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Shenzhen China

Due to the absorption and scattering effects of water on light, underwater images generally suffer from multiple degradations such as blur, color cast, and non-uniform illumination, which severely affect image quality and visual processing tasks. Therefore, underwater image enhancement (UIE) has gained widespread application in various marine exploration tasks. While supervised learning-based methods currently dominate this field, existing methods have two main problems. The limited availability of real paired images and the incompleteness of the degradation types of synthetic datasets restricts the model training performance, and most UIE models are designed for specific types of degradation, lacking systematic processing of multiple underwater degradations. These problems lead to poor model performance. In this work, we construct an Underwater Multi-Degradation Knowledge Integration dataset, called UMDKI, it models multiple degradation factors including blur, color cast, and non-uniform illumination by incorporating a revised image formation model and point light mathematical modeling. Besides, we propose an Underwater Multi-degradation Image Enhancement Network, called UMIENet, it integrates the advantages of various traditional methods and achieves collaborative enhancement of multiple degradations. Extensive experiments demonstrate that the proposed UMIENet achieves excellent performance on multiple benchmarks and shows good effectiveness in real underwater vision tasks. © 2025 Elsevier Ltd

关键词： Photointerpretation

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Centra-Net: A Centralized Network for Visual Localization Spanning Multiple Scenes

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IEEE Transactions on Multimedia 2025年

作者： Jiang, Zhiqiang Dai, Kun Wang, Ke Xie, Tao Fan, Zhendong Li, Ruifeng Kang, Peng Zhao, Lijun Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin150006 China Harbin Institute of Technology Zhengzhou Research Institute Zhengzhou450000 China Jianghuai Advance Technology Center Hefei230000 China

We present Centra-Net, a centralized network that concurrently optimizes visual localization over numerous scenes under heterogeneous dataset domains. Centra-Net exemplifies storage efficiency by amalgamating multiple models with task-shared parameters into a singular cohesive structure. Technically, we develop a basic feature extraction unit (BFEU) with two parallel branches: one dedicated to local feature extraction and the other adept at adaptively generating a task-specific attention mask for feature calibration, thus bolstering its feature extraction capability across diverse scenes. Based on the BFEU, we introduce a filter-wise sharing mechanism (FSM) that adaptively determines parameter sharing within the unit, thus facilitating fine-grained parameter allocation. The key insight of FSM resides in reconceptualizing the parameter sharing of the unit as a learnable paradigm, enabling the determination of shared parameters to be made post-training. Finally, we suggest a complexity-prioritized gradient algorithm (CPGA) that capitalizes on task complexity to attain a harmonious learning space for various tasks, thus safeguarding optimal performances across all tasks. Through rigorous experiments on numerous benchmarks, Centra-Net demonstrates a notable edge over existing state-of-the-art works while operating with a significantly reduced parameter footprint. © 1999-2012 IEEE.

关键词： Deep learning Feature extraction Task complexity Visual localization

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Design of a SMA actuated freeplay elimination device for stiffness enhancement of a folding fin

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Mechanical systems and Signal Processing 2025年 234卷

作者： Zhang, Rongru Chen, Gang Yue, Honghao Si, Mingyu State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China Beijing Institute of Electronic System Engineering Beijing100854 China

The folding fin, essential for controlling aircraft flight attitude and maintaining stability, is extensively employed in aerospace vehicles. Inevitable freeplay within the structure, due to design, installation, and mechanical wear, reduce the stiffness of the folding fins and impair the aircraft's aerodynamic performance. Therefore, eliminating the freeplay in the folding fin is crucial for enhancing the aircraft's flight accuracy and stability. This paper introduces a novel freeplay elimination device utilizing SMA columns, which neither increases mass nor alters the aircraft's external dimensions. The device effectively alleviates nonlinear issues associated with gaps in the fin system and enhances both the dynamic and static stiffness of the folding fin. A parametric design model for the gap elimination device has been developed, delineating the relationship between the structure and layout of the SMA columns, the actuation temperature, load, and the target gap for elimination. We conducted static load tests along with modal tests at room and elevated temperatures to evaluate the stiffness enhancement performance of the folding fin before and after gap elimination. The experiments verified that the designed gap elimination device significantly enhances both the static and dynamic stiffness of the folding fin. Additionally, the first-order frequency of the folding fin increased by 40 % at room temperature and by 36.7 % at 200 °C. This paper provides quantifiable guidance for the structural and parametric design of gap elimination devices in folding fins. Optimal structural parameters, determined based on aerodynamic loads and gap elimination requirements, enhance the stiffness of the folding fin, thus improving the aircraft's flight performance. © 2025

关键词： Freewing aircraft

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Aeroengine remaining useful life prediction via integrating enhanced inverted transformer and spatiotemporal graph learning

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ISA Transactions 2025年

作者： Sun, Shilong Ding, Hao Zhao, Zida Zhou, Yu Wang, Dong Xu, Wenfu School of Robotics and Advanced Manufacturing College of Artificial Intelligence Harbin Institute of Technology Shenzhen China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai200240 China

Accurate prediction of aeroengine Remaining Useful Life (RUL) is critical for ensuring flight safety, minimizing maintenance costs, and improving operational efficiency. This study proposes a novel model, the Fourier-Enhanced Inverted Transformer with Graph-Augmented Spatiotemporal Modeling (FIT-GSTM), to enhance RUL prediction performance. FIT-GSTM combines an inverted Transformer with a Spatiotemporal Graph Convolutional Network (STGCN) to effectively capture global spatiotemporal dependencies across multi-sensor data. To further enrich feature representation, the model incorporates Fast Fourier Transform (FFT) to extract frequency-domain information and fuses it with time-domain features, enhancing robustness to noise. Additionally, the integration of Memory Tokens and Reversible Instance Normalization (RevIN) strengthens the model's ability to retain long-term dependencies and adapt to heterogeneous data distributions. Experimental evaluations on the C-MAPSS dataset demonstrate that FIT-GSTM achieves superior RUL prediction accuracy and generalization compared to existing methods, highlighting its potential for real-world deployment in aeroengine health management. © 2025 ISA

关键词： Fast Fourier transforms

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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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An Iterative Task-Driven Framework for Resilient LiDAR Place Recognition in Adverse Weather

arXiv

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arXiv 2025年

作者： Zhao, Xiongwei Wang, Yang Sun, Qihao Bai, Haojie Xie, Xingxiang Shenzhen518071 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China School of Information Communication Technology Shenzhen Institute of Information Technology Shenzhen518071 China

LiDAR place recognition (LPR) plays a vital role in autonomous navigation. However, existing LPR methods struggle to maintain robustness under adverse weather conditions such as rain, snow, and fog, where weather-induced noise and point cloud degradation impair LiDAR reliability and perception accuracy. To tackle these challenges, we propose an Iterative Task-Driven Framework (ITDNet), which integrates a LiDAR Data Restoration (LDR) module and a LiDAR Place Recognition (LPR) module through an iterative learning strategy. These modules are jointly trained end-to-end, with alternating optimization to enhance performance. The core rationale of ITDNet is to leverage the LDR module to recover the corrupted point clouds while preserving structural consistency with clean data, thereby improving LPR accuracy in adverse weather. Simultaneously, the LPR task provides feature pseudo-labels to guide the LDR module’s training, aligning it more effectively with the LPR task. To achieve this, we first design a task-driven LPR loss and a reconstruction loss to jointly supervise the optimization of the LDR module. Furthermore, for the LDR module, we propose a Dual-Domain Mixer (DDM) block for frequency-spatial feature fusion and a Semantic-Aware Generator (SAG) block for semantic-guided restoration. In addition, for the LPR module, we introduce a Multi-Frequency Transformer (MFT) block and a Wavelet Pyramid NetVLAD (WPN) block to aggregate multi-scale, robust global descriptors. Finally, extensive experiments on the Weather-KITTI, Boreas, and our proposed Weather-Apollo datasets demonstrate that, demonstrate that ITDNet outperforms existing LPR methods, achieving state-of-the-art performance in adverse weather. The datasets and code will be made publicly available at https://***/Grandzxw/ITDNet. © 2025, CC BY.

关键词： Image coding

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