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Bilateral tele-rehabilitation system with electrical stimulation by using cloud service 10

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Bilateral tele-rehabilitation system with electrical stimula...

2020 10th International Workshop on Computer Science and engineering, WCSE 2020

作者： Kawai, Yasunori Houga, Koudai Kawai, Hiroyuki Miyoshi, Takanori Department of Electrical Engineering National Institute of Technology Ishikawa College Japan Department of Robotics Kanazawa Institute of Technology Japan Department of System Safety Nagaoka University of Technology Japan

ISBN: (纸本)9789811447877

This paper considers a bilateral tele-rehabilitation system with electrical stimulation for a human lower limb by using the Amazon Web Services Internet of Things (AWS IoT) as a cloud service. The scattering matrix method can guarantee the stability for the time delay in the bilateral tele-rehabilitation. This paper applies the AWS IoT to the bilateral tele-rehabilitation system. Because the cross certification and the cipher are provided in AWS IoT, the cybersecurity is considered compared to the HTTP method in the previous research. The experimental results show that the stability can be compensated even if the time delay exist. However, the error between the paddle angle and the knee angle exist. © WCSE 2020.

关键词： Internet of things

Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction

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arXiv 2023年

作者： Li, Qi Shen, Ziyi Li, Qian Barratt, Dean C. Dowrick, Thomas Clarkson, Matthew J. Vercauteren, Tom Hu, Yipeng Centre for Medical Image Computing Wellcome/EPSRC Centre for Interventional and Surgical Sciences Department of Medical Physics and Biomedical Engineering University College London London United Kingdom State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom

Three-dimensional (3D) freehand ultrasound (US) reconstruction without using any additional external tracking device has seen recent advances with deep neural networks (DNNs). In this paper, we first investigated two identified contributing factors of the learned inter-frame correlation that enable the DNN-based reconstruction: anatomy and protocol. We propose to incorporate the ability to represent these two factors - readily available during training - as the privileged information to improve existing DNN-based methods. This is implemented in a new multitask method, where the anatomical and protocol discrimination are used as auxiliary tasks. We further develop a differentiable network architecture to optimise the branching location of these auxiliary tasks, which controls the ratio between shared and task-specific network parameters, for maximising the benefits from the two auxiliary tasks. Experimental results, on a dataset with 38 forearms of 19 volunteers acquired with 6 different scanning protocols, show that 1) both anatomical and protocol variances are enabling factors for DNN-based US reconstruction;2) learning how to discriminate different subjects (anatomical variance) and predefined types of scanning paths (protocol variance) both significantly improve frame prediction accuracy, volume reconstruction overlap, accumulated tracking error and final drift, using the proposed algorithm. © 2023, CC BY.

关键词： Deep neural networks

Constant Direct Current Triboelectric Nanogenerator Based on Soft-Contact Mode for Self-Powered Cathodic Protection

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SSRN

SSRN 2022年

作者： Li, Ming Jiang, Tianyi Ren, Yukun Jiang, Hongyuan School of Mechatronics Engineering Harbin Institute of Technology West Da-zhi Street 92 Harbin150001 China State Key Laboratory of Robotics and System Harbin Institute of Technology West Da-zhi Street 92 Heilongjiang Harbin150001 China

Triboelectric nanogenerator (TENG) has been proved to be a promising technology for harvesting decentralized energy. However, the application fields of the traditional TENGs are limited by the high crest factor. Here a constant direct current TENG (CDC-TENG) with low crest factor is proposed by connecting three rotating TENGs with specific phase difference (Δ θ ) in parallel. Excellent wear resistance and humidity resistance are demonstrated owing to the soft-contact mode and 3D encapsulated design. For the CDC-TENG (3P6E), the crest factor surprisingly decreases to 1.05 while that of the TENGs without Δ θ is up to 1.31. Meanwhile, the current density up to 3021 μA·m -2 is achieved at the rotation speed of 500 r·min -1 . Then, owing to the excellent output of constant direct current (DC), a commercial timer is directly driven by the CDC-TENG without using capacitor. Finally, the CDC-TENG is used as the power source of cathodic protection (CP) system, which exhibits an excellent corrosion protection effect. In general, this work provides a meaningful template for TENGs to broaden the marine industrial applications of constant DC demand. © 2022, The Authors. All rights reserved.

关键词： Nanogenerators

Examination of an independently actuated multi-air pad mattress for preventing pressure ulcers

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Journal of institute of Control, robotics and systems 2021年第10期27卷 695-702页

作者： Yang, Kyon-Mo Park, Min-Ro Koo, Jaewan Lee, Jong-Il Gwak, Donggi Lee, Seokjae Sohn, Dong-Seop Kim, Min-Gyu Seo, Kap-Ho Korea Institute of Robotics & Technology Convergence Korea Republic of Dept. of Robot and Smart System Engineering Kyungpook National University Korea Republic of Korea Republic of

This study examined the structure of an independently actuated multi-air pad mattress for preventing pressure ulcers, owing to lower body pressure, in specific locations of the body where pressure sores can occur. The contributions of this study are as follows: 1) The parameters were derived and simulations were performed to design air pad units of different sizes with similar heights. 2) The mattress was manufactured by examining the bending of the folding bed, with 36 air pads and three different sizes. 3) Each air pad was independently controlled to inflate, contract, and retain to reduce the pressure on specific body positions. 4) Finally, using the fabricated prototype, the proposed designs were experimentally verified. © ICROS 2021.

关键词： Diseases

Joint Counting, Detection and Re-Identification for Multi-Object Tracking

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arXiv 2022年

作者： Ren, Weihong Wu, Denglu Cao, Hui Chen, Xi'ai Han, Zhi Liu, Honghai The School of Mechanical Engineering and Automation State Key Lab of Robotics and Systems Harbin Institute of Technology Shenzhen518055 China The Shenzhen Duorou Intelligent System Co. Ltd. Shenzhen518055 China The School of Electrical Engineering Xi’an Jiaotong University Xi’an710049 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Beijing110169 China

The recent trend in 2D multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously. Despite competitive performance, in crowded scenes, joint detection and tracking usually fail to find accurate object associations due to missed or false detections. In this paper, we jointly model counting, detection and re-identification in an end-to-end framework, named CountingMOT, tailored for crowded scenes. By imposing mutual object-count constraints between detection and counting, the CountingMOT tries to find a balance between object detection and crowd density map estimation, which can help it to recover missed detections or reject false detections. Our approach is an attempt to bridge the gap of object detection, counting, and re-Identification. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to failure in crowded scenes, or depend on local correlations to build a graphical relationship for matching targets. The proposed MOT tracker can perform online and real-time tracking, and achieves the state-of-the-art results on public benchmarks MOT16 (MOTA of 79.7%), MOT17 (MOTA of 81.3%) and MOT20 (MOTA of 78.9%). Source code is available at https://***/weihong9/CountingMOT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition

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arXiv 2023年

作者： Li, Qiufu Jia, Xi Zhou, Jiancan Shen, Linlin Duan, Jinming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Computer Vision Institute Shenzhen University China School of Computer Science University of Birmingham United Kingdom Aqara Lumi United Technology Co. Ltd China Alan Turing Institute United Kingdom SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available at https://***/CVI-SZU/UniTSFace. © 2023, CC BY.

关键词： Face recognition

A Modular End Effector with Active Rolling Fingertip for Picking Cloth-Like Objects

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A Modular End Effector with Active Rolling Fingertip for Pic...

IEEE International Conference on Automation Science and engineering (CASE)

作者： Kun Zhang Yuanhang Yang Zhiming Chen Hua Chen Michael Yu Wang Wei Zhang Robotics Institute Hong Kong University of Science and Technology Clear Water Bay Hong Kong School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China Department of Mechanical & Aerospace Engineering Monash University Clayton Australia

Cloth-like items are ubiquitous in both daily life and industrial production, but their softness makes them difficult to manipulate. This paper focuses on solving the problem of automatic manipulation especially in the scenario of flat surface by introducing a novel end-effector mechanism. The gripper utilizes a modular design concept, enabling it to be reconstructed with a varying number of fingers based on specific requirements. One of the fingers has an active rolling fingertip that rolls up the object from the table to create wrinkles, for easier grasping. Additionally, there are fingertips with revolute joints that enable fine-tuning of the grasp configuration to fix the objects. To test the effectiveness of the mechanism, we manufactured a real gripper device and mounted it on a collaborative robotic arm for grasping tests. Experiments were conducted on six kinds of cloth-like objects with different materials, grasping them from above. The experimental results show that the gripper with this mechanism configuration performs well in this scenario.

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Unsupervised Learning of 3D Scene Flow from Monocular Camera

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arXiv 2022年

作者： Wang, Guangming Tian, Xiaoyu Ding, Ruiqi Wang, Hesheng Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai 200240 China

Scene flow represents the motion of points in the 3D space, which is the counterpart of the optical flow that represents the motion of pixels in the 2D image. However, it is difficult to obtain the ground truth of scene flow in the real scenes, and recent studies are based on synthetic data for training. Therefore, how to train a scene flow network with unsupervised methods based on real-world data shows crucial significance. A novel unsupervised learning method for scene flow is proposed in this paper, which utilizes the images of two consecutive frames taken by monocular camera without the ground truth of scene flow for training. Our method realizes the goal that training scene flow network with real-world data, which bridges the gap between training data and test data and broadens the scope of available data for training. Unsupervised learning of scene flow in this paper mainly consists of two parts: (i) depth estimation and camera pose estimation, and (ii) scene flow estimation based on four different loss functions. Depth estimation and camera pose estimation obtain the depth maps and camera pose between two consecutive frames, which provide further information for the next scene flow estimation. After that, we used depth consistency loss, dynamic-static consistency loss, Chamfer loss, and Laplacian regularization loss to carry out unsupervised training of the scene flow network. To our knowledge, this is the first paper that realizes the unsupervised learning of 3D scene flow from monocular camera. The experiment results on KITTI show that our method for unsupervised learning of scene flow meets great performance compared to traditional methods Iterative Closest Point (ICP) and Fast Global Registration (FGR). The source code is available at: https://***/IRMVLab/3DUnMonoFlow. Copyright © 2022, The Authors. All rights reserved.

关键词： Unsupervised learning

Design and Development of a Rapidly Deployable Low-Cost Tensegrity In-Pipe Robot

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Design and Development of a Rapidly Deployable Low-Cost Tens...

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yixiang Liu Xiaolin Dai Kai Guo Jiang Wu Rui Song Jie Zhao Yibin Li School of Control Science and Engineering Shandong University Jinan China State Key Laboratory of Mechanical Transmissions Chongqing University Chongqing China Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou Shenhao Technology Co. Ltd Hangzhou China Engineering Research Center of Intelligent Unmanned System of Ministry of Education School of Control Science and Engineering Shandong University Jinan China School of Mechanical Engineering Shandong University Jinan China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Existing in-pipe robots have insufficient adaptability when dealing with accidents in unfamiliar pipe environments. Developing a pipe robot that can be designed and manufactured quickly is one solution. The tensegrity structure is a self-stressing spatial structure formed by the interaction of rigid members and flexible cables, which has the advantages of simple structure, good flexibility, deformability, and impact resistance. Inspired by this structure, we design a novel worm-like tensegrity robot for different pipe environments, which can be manufactured rapidly at low cost. Firstly, a robotic module based on the tensegrity structure is designed inspired by the motion patterns of worm-like organisms. Then, the design process of the module is presented based on the mathematical analysis of the deformation. Finally, a prototype of the tensegrity robot is developed using simple and low-cost parts in less than an hour. To test the motion performance, load performance, and inspection capability of the tensegrity robot, we designed a series of experiments on horizontal pipes, vertical pipes, elbows, and steel pipes. Experimental results show that the worm-like tensegrity robot is simple in structure, easy to manufacture, low in cost, and good in performance.

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