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Using Gamification to Increase Healthcare Professionals’ Cybersecurity Awareness

Using Gamification to Increase Healthcare Professionals’ Cy...

International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Cybersecurity knowledge is essential in today’s increasingly digital healthcare environment to protect sensitive patient data and guarantee the integrity of healthcare systems. But conventional cybersecurity training approaches frequently fail to hold healthcare professionals’ attention, which results in inadequate application and retention of the material. In order to increase cybersecurity awareness among healthcare professionals, this study investigates the use of gamification—a technological technique that incorporates game design aspects into non-gaming contexts—as an innovative method. A mixed-methods research methodology is used in the study to assess the effectiveness of a cybersecurity training program that is gamified. To encourage user involvement and motivation, the program incorporates aspects like leaderboards, interactive scenarios, badges, and points. The study included a total of 100 healthcare professionals from various departments, offering a thorough understanding of the program’s effects across a range of positions and experience levels. Pre- and post-training surveys were used to gather quantitative data on participant changes in knowledge, attitudes, and behaviors pertaining to cybersecurity procedures. Furthermore, semi-structured interviews yielded qualitative insights that further elucidated the participants’ perspectives and experiences with the gamified training method. Comparing gamification to conventional training approaches, the results show that gamification dramatically improves engagement and knowledge retention. The willingness of the participants to acquire and use cybersecurity techniques in their daily lives has reportedly increased. Additionally, the gamified components strengthened the training objectives by fostering a competitive yet cooperative learning environment. The potential of gamification as a game-changing instrument for cybersecurity education in healthcare settings is highlighted by this study. Healthcare busines

关键词： Cybersecurity

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Uncover rock-climbing fish's secret of balancing tight adhesion and fast sliding for bioinspired robots

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National Science Review 2023年第8期10卷 222-231页

作者： Wenjun Tan Chuang Zhang Ruiqian Wang Yuanyuan Fu Qin Chen Yongliang Yang Wenxue Wang Mingjun Zhang Ning Xi Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Histology and Embryology Basic Medical CollegeChina Medical University Chengdu Institute of Biology Chinese Academy of Sciences Department of Biomedical Engineering School of Medicine Tsinghua University Emerging Technologies Institute Department of Industrial and Manufacturing Systems EngineeringUniversity of Hong Kong

The underlying principle of the unique dynamic adaptive adhesion capability of a rock-climbing fish（Beaufortia kweichowensis） that can resist a pull-off force of 1000 times its weight while achieving simultaneous fast sliding(7.83 body lengths per second（BL/S）) remains a mystery in the literature. This adhesion-sliding ability has long been sought for underwater robots. However, strong surface adhesion and fast sliding appear to contradict each other due to the need for high surface contact stress. The skillfully balanced mechanism of the tight surface adhesion and fast sliding of the rock-climbing fish is disclosed in this work. The Stefan force（0.1 mN/mm2） generated by micro-setae on pectoral fins and ventral fins leads to a 70 N/m2adhesion force by conforming the overall body of the fish to a surface to form a sealing chamber. The pull-off force is neutralized simultaneously due to the negative pressure caused by the volumetric change of the chamber. The rock-climbing fish's micro-setae hydrodynamic interaction and sealing suction cup work cohesively to contribute to low friction and high pull-off-force resistance and can therefore slide rapidly while clinging to the surface. Inspired by this unique mechanism, an underwater robot is developed with incorporated structures that mimic the functionality of the rock-climbing fish via a micro-setae array attached to a soft self-adaptive chamber, a setup which demonstrates superiority over conventional structures in terms of balancing tight underwater adhesion and fast sliding.

关键词： bioinspired underwater adhesion crawling robots rock-climbing fish Stefan adhesion underwater robots

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Gait Generation for the Multi-legged Robot with Injured Legs 41

Gait Generation for the Multi-legged Robot with Injured Legs

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第41届中国控制会议

作者： Ming Zeng Zhijing Wang Yu Ma Jiazhe Liu Baofeng Zhang Yunke Nie Institute of Robotics and Automation Systems School of Electrical and Information EngineeringTianjin University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

When multi-legged robots work in dangerous and complex environments,their legs are easily damaged,resulting in a sharp decline in their movement ability,or even completely out of *** this paper,the gait generation method combined deep reinforcement learning with bionic gait restrictions is presented for multi-legged robots with injured *** this study,we first built an ant gait video acquisition platform using the Camponotus Japonicus as the research object,and on this basis established a gait dataset of injured ***,the gait characteristics of ants in the injured state were ***,our proposed gait generation method combining bionic gait constraints with deep reinforcement learning was compared with the reinforcement-learning-based method in the MATLAB simulation *** test results show that the training reward values of our proposed algorithm are 1-2 times higher than those of the reinforcement learning method alone,and at the same time,the moving speed of the injured robot is also nearly twice as fast.

关键词： Injured multi-legged robot,Ant gait,Gait generation,Reinforcement learning Bionic constraints

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Network-aware Shared Autonomy in Bilateral Teleoperation 23

Network-aware Shared Autonomy in Bilateral Teleoperation

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Chen, Xiao Michel, Youssef Sadeghian, Hamid Haddadin, Sami Technical University of Munich Human-centered Assistive Robotics Munich Germany Munich Institute of Robotics and Machine Intelligence Robotics Science and Systems Intelligence Technical University of Munich Munich Germany University of Isfahan Faculty of Engineering Isfahan*** Iran

ISBN: (纸本)9798350373578

In this paper, an autonomy allocation approach is proposed for bilateral teleoperation systems to improve user performance under suboptimal communication networks. To achieve this, time-varying communication quality metrics such as delay and jitter are continuously monitored on the teleoperated side, and the autonomy level is dynamically adjusted based on the communication network quality. An autonomous agent is also deployed on the teleoperated side, leveraging preexisting task knowledge for shared autonomy. Additionally, a time-domain passivity approach is employed to maintain communication channel passivity, mitigating the impact of adverse network behavior on task performance. The proposed approach is validated through extensive experiments and user studies, and the result shows our approach significantly improved the performance of the subjects (p © 2024 IEEE.

关键词： Teleconferencing

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Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundus Images with 3D UNet 26

Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundu...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Marciniak, Tomasz Stankiewicz, Agnieszka Zaradzki, Przemyslaw Poznan University of Technology Division of Electronic Systems and Signal Processing Institute of Automatic Control and Robotics Department of Control Robotics and Electrical Engineering Poznan Poland

ISBN: (纸本)9798350304985

This paper presents an analysis of the effectiveness of blood vessel segmentation in human retinal images. The segmentation process was carried out for three-dimensional optical coherence tomography (OCT) scans using the author's publicly available CAVRI-C database. A 3D version of the U-Net artificial neural network was used. Various dimensions of the input blocks were investigated to determine their influence on the segmentation process. The effectiveness of the proposed solutions was evaluated using various metrics and the results obtained were compared with the 2D solution, also based on neural networks. The use of the proposed solutions allowed us to improve the segmentation even by 15% (in the case of the F1-score). © 2023 Division of Signal Processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Optical tomography

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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An Improved Object Detection Algorithm for Assembly Line Garbage Sorting 41

An Improved Object Detection Algorithm for Assembly Line Gar...

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第41届中国控制会议

作者： Ming Zeng Jiazhe Liu Xiangzhe Lu Zhijing Wang Yuanhao Li Siying Li Institute of Robotics and Automation Systems School of Electrical and Information EngineeringTianjin University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Garbage object detection is of central importance to achieve accurate garbage *** this work,we propose an improved object detection algorithm for sorting complex garbage objects on the assembly *** algorithm utilizes the Faster RCNN as the baseline and improves its performance with several tips,including deformable convolution,PAFPN,mixup,cutout,color jitter,multi-scale training and ImageNet transfer *** the lack of public datasets for the garbage object detection task,we synthesize a highly realistic multi-object garbage detection dataset,M-GarbageNet,with 20000 training images,5000 validation images and 5000 test ***,we evaluate our algorithm than Faster RCNN,and obtain a 8.2%relative improvement on the M-GarbageNet dataset.

关键词： Garbage object detection Improved Faster RCNN M-GarbageNet Image augmentation

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Reachability-Based Delayed Decision Guidance for Conventional Interceptors 63

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63rd Israel Annual Conference on Aerospace Sciences, IACAS 2024

作者： Shalumov, Vitaly Merkulov, Gleb Shima, Tal Technion – Israel Institute of Technology Haifa3200003 Israel The Technion Program for Autonomous Systems and Robotics Israel Faculty of Aerospace Engineering Israel

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Integrated Cross-Scale Manipulation and Modulable Encapsulation of Cell-Laden Hydrogel for Constructing Tissue-Mimicking Microstructures

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Research 2025年第1期2024卷 704-717页

作者： Yanfeng Zhao Xinyi Dong Yang Li Juan Cui Qing Shi Hen-Wei Huang Qiang Huang Huaping Wang Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Peking University First Hospital Xicheng DistrictBeijing 100034China Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of EducationNorth University of ChinaTaiyuan 030051China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China Laboratory for Translational Engineering Harvard Medical SchoolCambridgeMA 02139USA Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of EducationBeijing 100081China

Engineered microstructures that mimic in vivo tissues have demonstrated great potential for applications in regenerative medicine,drug screening,and cell behavior ***,current methods for engineering microstructures that mimic the multi-extracellular matrix and multicellular features of natural tissues to realize tissue-mimicking microstructures in vitro remain ***,we propose a versatile method for constructing tissue-mimicking heterogeneous microstructures by orderly integration of macroscopic hydrogel exchange,microscopic cell manipulation,and encapsulation ***,various cell-laden hydrogel droplets are manipulated at the millimeter scale using electrowetting on dielectric to achieve efficient hydrogel ***,the cells are manipulated at the micrometer scale using dielectrophoresis to adjust their density and arrangement within the hydrogel ***,the photopolymerization of these hydrogel droplets is triggered in designated regions by dynamically modulating the shape and position of the excitation ultraviolet ***,heterogeneous microstructures with different extracellular matrix geometries and components were constructed,including specific cell densities and *** resulting heterogeneous microstructure supported long-term culture of hepatocytes and fibroblasts with high cell viability(over 90%).Moreover,the density and distribution of the 2 cell types had significant effects on the cell proliferation and urea *** propose that our method can lead to the construction of additional biomimetic heterogeneous microstructures with unprecedented potential for use in future tissue engineering applications.

关键词： cell behavior explorationhowevercurrent tissue mimicking microstructures cell laden hydrogel electrowetting dielectric regenerative medicinedrug screeningand heterogeneous microstructures engineered microstructures orderly integration

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Learning Rat-Like Behavioral Interaction Using a Small-Scale Robotic Rat

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Cyborg and Bionic systems 2023年第1期4卷 225-232页

作者： Hongzhao Xie Zihang Gao Guanglu Jia Shingo Shimoda Qing Shi Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China Nagoya University Graduate School of Medicine NagoyaJapan

In this paper,we propose a novel method for emulating rat-like behavioral interactions in robots using reinforcement ***,we develop a state decision method to optimize the interaction process among 6 known behavior types that have been identified in previous research on rat *** novelty of our method lies in using the temporal difference(TD)algorithm to optimize the state decision process,which enables the robots to make informed decisions about their behavior *** assess the similarity between robot and rat behavior,we use Pearson *** then use TD-λto update the state value function and make state decisions based on *** robots execute these decisions using our dynamics-based *** results demonstrate that our method can generate rat-like behaviors on both short-and long-term timescales,with interaction information entropy comparable to that between real ***,our approach shows promise for controlling robots in robot-rat interactions and highlights the potential of using reinforcement learning to develop more sophisticated robotic systems.

关键词： robot enable similarity

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