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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Schneider, Sebastian Human-Media Interaction Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9789819635245

Social robots capable of physically or tactilely interacting with users could unlock new health applications. Despite the potential benefits, integrating physical interaction capabilities in social robot applications remains an emerging field. There is a pressing need to consolidate existing knowledge and identify gaps to guide future research and development. This paper presents a review on current research on physical interaction with social robots in therapeutic settings, particularly in rehabilitation. Despite the burgeoning interest in rehabilitation robotics, there is a noticeable scarcity of studies that delve into social robots in physiotherapy-like interactions. Most studies focus on feasibility rather than effectiveness with target populations, often featuring small sample sizes and varied settings. This limits the ability to draw robust conclusions on the impacts of haptic interaction. Three primary research areas are identified: (1) involving target user groups to ensure relevance, (2) exploring haptic feedback, e.g., through wearable devices, to enhance human-robot interaction, and (3) implementing standardized study designs for meaningful comparisons. Findings highlight the need for comprehensive research to harness social robots’ potential in therapeutic settings, improving the quality of life for individuals with physical and cognitive impairments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Microrobots

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments 14

AI-Driven Relocation Tracking in Dynamic Kitchen Environment...

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14th International Conference on computer and Knowledge Engineering, ICCKE 2024

作者： Esfahani, Arash Nasr Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Sajedi, Hedieh Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

ISBN: (纸本)9798331511272

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes. ©2024 IEEE.

关键词： Optical flows

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Benefits of Machine Learning Explanations: Improved Learning in an AI-assisted Sequence Prediction Task 25

Benefits of Machine Learning Explanations: Improved Learning...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Liang, Yu Collaris, Dennis Willemsen, Martijn C. Van Wijk, Jarke J. Human-Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Dept. Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Jheronimus Academy of Data Science Den Bosch Netherlands

ISBN: (纸本)9798400713064

Research in Explainable AI (XAI) has shown that explanations can improve users' understanding of AI models, improve user performance and potentially reduce overreliance on AI predictions. However, this is mostly evaluated by static rather than dynamic measures, and the role of XAI on learning over trials is rarely studied. In this study, we use a context-free sequence prediction task, in which 458 participants predict the next symbol in a fixed sequence (with some noise) over 80 trials. We compare performance with AI and XAI advice against no AI support, and subsequently we test for learning by taking away the AI support after 40 trials (i.e., a reversal study design). Our results show that users learn faster with XAI than with AI without explanations or no AI and are better able to recover in performance from the removal of AI. However, the benefits of XAI on learning are much smaller for more difficult tasks. This work demonstrates the benefits of repeated measures user studies and multilevel modeling to better understand learning processes in XAI. It also shows the potential of AI explanations to help users to learn and poses XAI design suggestions to support learning in human-AI collaboration. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Advanced Markov Modeling and Simulation for Safety Analysis of Autonomous Driving Functions up to SAE 5 for Development, Approval and Main Inspection 32nd

Advanced Markov Modeling and Simulation for Safety Analysis ...

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32nd European Safety and Reliability Conference, ESREL 2022

作者： Häring, Ivo Satsrisakul, Yupak Finger, Jörg Vogelbacher, Georg Köpke, Corinna Höflinger, Fabian Gelhausen, Patrick Fraunhofer EMI Freiburg Germany University of Hagen Human-Computer Interaction Faculty of Mathematics and Computer Science Hagen Germany

ISBN: (纸本)9789811851834

The development, approval and recurring testing of driving assistance, partial and conditional automation (SAE levels 1 to 3) and, above all, high and full automation (4 to 5) requires an ever-increasing effort. At the same time, there is a lack of recognized sufficiently scalable general quantitative approaches in this area, in particular simulation methods including models, software and hardware up to vehicle-in-the-loop simulations, e.g. in the context of main inspection. In this context, the paper explores the potential of non-classical Markov modeling. First, it exemplifies how vehicles, drivers and other road users can be modeled for different driving scenarios using the Systems Modeling Language (SysML). Based on this model an abstract Markov diagram is presented. The two Markov simulation methods used operate on a discrete finite state space and allow for time-dependent state transitions. When compared to the matrix solver-based simulation, the Monte Carlo based simulation method is in principle extensible to state history-dependent as well as rule-based state transitions. Also, it allows to include subsystem simulation models. The extended Markov model allows to evaluate states with respect to functionality and safety, e.g. to determine whether it is sufficiently likely to reach fail operational states. It can also be used to determine dominant critical transitions and insufficient system resolution. In addition, numerous standardized safety and reliability measures are accessible. It will be shown how such a reference model can be quantified using simple failure models, as well as how it could be fed with further transition and failure models at different levels of abstraction using simulations as well as software tests data up to field test data. Finally, the paper hints at the potential of such an extended Markov modeling, especially with respect to an understandable and efficient safety verification in different product life cycle phases including after

关键词： Markov processes

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments

arXiv

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arXiv 2025年

关键词： Optical flows

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In Time and Space: Towards Usable Adaptive Control for Assistive Robotic Arms

In Time and Space: Towards Usable Adaptive Control for Assis...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Max Pascher Kirill Kronhardt Felix Ferdinand Goldau Udo Frese Jens Gerken Human-Computer Interaction Westphalian University of Applied Sciences Gelsenkirchen Germany Human-Computer Interaction University of Duisburg-Essen Essen Germany Mathematics & Computer Science University of Bremen Bremen Germany

Robotic solutions, in particular robotic arms, are becoming more frequently deployed for close collaboration with humans, for example in manufacturing or domestic care environments. These robotic arms require the user to control several Degrees-of-Freedom (DoFs) to perform tasks, primarily involving grasping and manipulating objects. Standard input devices predominantly have two DoFs, requiring time-consuming and cognitively demanding mode switches to select individual DoFs. Contemporary Adaptive DoF Mapping Controls (ADMCs) have shown to decrease the necessary number of mode switches but were up to now not able to significantly reduce the perceived workload. Users still bear the mental workload of incorporating abstract mode switching into their workflow. We address this by providing feed-forward multimodal feedback using updated recommendations of ADMC, allowing users to visually compare the current and the suggested mapping in real-time. We contrast the effectiveness of two new approaches that a) continuously recommend updated DoF combinations or b) use discrete thresholds between current robot movements and new recommendations. Both are compared in a Virtual Reality (VR) in-person study against a classic control method. Significant results for lowered task completion time, fewer mode switches, and reduced perceived workload conclusively establish that in combination with feedforward, ADMC methods can indeed outperform classic mode switching. A lack of apparent quantitative differences between Continuous and Threshold reveals the importance of user-centered customization options. Including these implications in the development process will improve usability, which is essential for successfully implementing robotic technologies with high user acceptance.

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Recognizing Adenoid Hypertrophy from Facial Images with Multi-scale Feature Fused State Space Model 18th

Recognizing Adenoid Hypertrophy from Facial Images with Mu...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Ma, Shuai He, Jinrong Wang, Yao Bi, Yingzhou Yang, Li College of Mathematics and Computer Science Yan’an University Yan’an716000 China Yan’an University Affiliated Hospital Yan’an716000 China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China School Hospital Yan’an University Yan’an716000 China

ISBN: (纸本)9789819610709

Early diagnosis of pediatric adenoidal hypertrophy is crucial for timely identification of respiratory obstructions, thereby preventing declines in sleep quality that can lead to impaired growth, development, and cognitive function. Currently, the most common clinical diagnostic methods involve nasopharyngeal endoscopy and X-ray imaging assessments, which are time-consuming, labor-intensive, and costly. This paper presents an intelligent diagnostic model based on natural light facial images, employing a state space model (SSM). The model combines the local feature extraction capability of convolutional layers with the SSM’s ability to capture long-range dependencies for facial feature extraction. Furthermore, it incorporates a feature fusion strategy to enhance the features of facial images. Experimentation on a self-constructed dataset of adenoid facies images demonstrated that the model achieves an accuracy of 86.55% and an F1-score of 86.21%. The data and code are available at https://***/hejinrong/AF-SSM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments

AI-Driven Relocation Tracking in Dynamic Kitchen Environment...

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International eConference on computer and Knowledge Engineering (ICCKE)

作者： Arash Nasr Esfahani Hamed Hosseini Mehdi Tale Masouleh Ahmad Kalhor Hedieh Sajedi Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

关键词： YOLO Accuracy Three-dimensional displays Computational modeling Heuristic algorithms Training data Smart homes Data models Object recognition Testing

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

DRDM: A Disentangled Representations Diffusion Model for Syn...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Enbo Zhang, Yuan Huang, Faliang Zhang, Guangyu Liu, Yang Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer and Information Engineering Nanning Normal University Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350368741

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photo-realistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. The associated project can be found at https://***/lovemusiceb/DRDM. © 2025 IEEE.

关键词： Diffusion Model Disentangled Representation Human Parsing Person image synthesis

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Integrating Image Recognition and Board Games into Financial Education 16

Integrating Image Recognition and Board Games into Financial...

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16th Asia-Pacific Workshop on Mixed and Augmented Reality, APMAR 2024

作者： Hung, Yu-Han Chao, Hao-Bin Liou, Jie-Sin Han, Ping-Hsuan Hsieh, Yi-Zeng Fan, Ping-Lin Chou, Chien-Hsing Department of Mathematics and Information Education National Taipei University of Education Taiwan Department of Interaction Design National Taipei University of Technology Taiwan Department of Electrical and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Digital Technology Design National Taipei University of Education Taiwan

As financial products diversify and market structures become more complex, promoting financial education has become increasingly important. Given that the foundation of financial literacy should be established from childhood, how to cultivate students' correct financial management concepts from a young age has become a significant issue in modern society. Considering the substantial advantages of game-based learning in providing high interactivity and real-world scenario simulations, this paper aims to develop an innovative financial education board game that incorporates elements of risk assessment and insurance planning, and digitalizes it. With the assistance of an app and the application of image recognition technology, we could enhance the convenience and fun of the gameplay. Research results show that integrating digital apps into board games as a game-based learning strategy significantly improves the learning outcomes and motivation of upper elementary school students in the areas of risk and insurance knowledge, compared to traditional lecture methods. It also enhances their understanding and application abilities. Students hold a positive attitude toward this teaching approach, demonstrating its potential and advantages in financial education. © 2024 Copyright for this paper by its authors.

关键词： Decentralized finance

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