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arXiv 2023年

作者： Zhang, Bo Jiang, Boyu Zheng, Rong Zhang, Xiaoping Li, Jun Xu, Qiang Dept. of Computing and Software McMaster University HamiltonON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada Huawei Human Machine Interaction Lab MarkhamON Canada

Continuous monitoring of human vital signs using non-contact mmWave radars is attractive due to their ability to penetrate garments and operate under different lighting conditions. Unfortunately, most prior research requires subjects to stay at a fixed distance from radar sensors and to remain still during monitoring. These restrictions limit the applications of radar vital sign monitoring in real life scenarios. In this paper, we address these limitations and present Pi-ViMo, a non-contact Physiology-inspired Robust Vital Sign Monitoring system, using mmWave radars. We first derive a multi-scattering point model for the human body, and introduce a coherent combining of multiple scatterings to enhance the quality of estimated chest-wall movements. It enables vital sign estimations of subjects at any location in a radar’s field of view (FoV). We then propose a template matching method to extract human vital signs by adopting physical models of respiration and cardiac activities. The proposed method is capable to separate respiration and heartbeat in the presence of micro-level random body movements (RBM) when a subject is at any location within the field of view of a radar. Experiments in a radar testbed show average respiration rate errors of 6% and heart rate errors of 11.9% for the stationary subjects, and average errors of 13.5% for respiration rate and 13.6% for heart rate for subjects under different RBMs. © 2023, CC BY.

关键词： Errors

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Virtual Reality Pōwhiri—Practicing an indigenous welcoming ceremony 23

Virtual Reality Pōwhiri—Practicing an indigenous welcoming...

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Proceedings of the 35th Australian computer-human interaction Conference

作者： Laurence Lloyd-Jones Claudia Ott Holger Regenbrecht Hēmi Whaanga Human Computer Interaction (HCI) Lab University of Otago New Zealand Information Science University of Otago New Zealand Department of Information Science University of Otago New Zealand Te P?tahi-a-Toi Massey University New Zealand

ISBN: (纸本)9798400717079

Pōwhiri is a traditional welcoming ceremony in Māori culture in Aotearoa New Zealand. Certain protocols (tikanga) have to be followed and must be learned. There is a general lack of understanding regarding this due to the scarcity of pōwhiri. The immersive and experiential nature of Virtual Reality (VR) can be used as a tool to increase understanding and confidence. We have implemented a VR learning tool in the reconstructed context of Te Rau Aroha marae in Bluff which allows for safe practice of pōwhiri before applying it in a real welcoming ceremony. A cultural evaluation study was conducted first followed by a user study to determine understanding and confidence gains, highlighting the complexity of the topic. The user study clearly showed that the system was useful to increase participants’ understanding in regards to pōwhiri as well as their confidence surrounding pōwhiri. We are confident that our experiences and key findings from the studies can be used to drive further development of VR tools in the context of visualising cultural ceremonies.

关键词： 3D Reconstruction Indigenous User Interfaces VR Learning Volumetric Video

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An HMM-based Real-time Intervention Methodology for a Social Robot Supporting Learning

An HMM-based Real-time Intervention Methodology for a Social...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Jauwairia Nasir Mortadha Abderrahim Barbara Bruno Pierre Dillenbourg Human-Centered Artificial Intelligence (HCAI) Lab University of Augsburg Germany Computer-Human Interaction in Learning and Instruction (CHILI) Lab Swiss Federal Institute of Technology in Lausanne (EPFL) Switzerland Socially Assistive Robotics with Artificial Intelligence (SARAI) Lab Karlsruhe Institute of Technology (KIT) Germany

To make social robots effective in education, they need to be autonomous both in terms of assessing the student’s engagement state as well as intervening effectively in soft real-time when necessary. Hidden Markov Model (HMM) is an interpretable machine learning technique for modeling temporal data that is commonly used post-hoc to analyse latent learning processes. In this paper, we contribute by proposing an HMM-based intervention methodology for assessing and classifying the state of the student as either productive or unproductive in soft real-time. The system identifies and tracks states and patterns not conducive to learning, and a robot intervention is triggered whenever a too-high non-productive engagement is detected. In a pilot study with 22 children, we evaluate this methodology in terms of both 1) the effectiveness of the interventions on the students’ learning gains and on behaviors found conducive to learning, and 2) the students’ perception of the robotic interventions. Results suggest that the robot interventions have a positive effect on the post-test scores relative to the baseline robot, although there isn’t a significant difference in the learning gains. Moreover, interventions that try to induce reflective behaviors are most effective in inducing the required learning behavior, followed by communication-inducing interventions. Lastly, students’ perception of intervention usefulness does not reflect their actual effectiveness.

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How a Social Robot’s Vocalization Affects Children’s Speech, Learning, and interaction

How a Social Robot’s Vocalization Affects Children’s Speec...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Lauren L. Wright Aditi Kothiyal Kai O. Arras Barbara Bruno Computer-Human Interaction in Learning and Instruction (CHILI) Lab Swiss Federal Institute of Technology in Lausanne (EPFL) Switzerland Bosch Corporate Research Stuttgart

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

A wider incorporation of robots into classrooms is hampered by current technological limitations on full autonomy in social robots. Automated speech recognition, for example, a key enabler for vocal communication, is still unable to perform with sufficient accuracy. Past studies have shown that humans adjust their speech patterns to accommodate less skilled interlocutors. If such a response holds in human-robot interactions as well, we may be able to exploit it to lessen the burden on social robots and enable rich, autonomous vocal communication. In this paper we explore whether a robot’s speaking ability could have an impact on children’s speech patterns, learning, and engagement by designing an interaction where a child and a robot collaborate on a Tower of Hanoi puzzle. Sixteen children aged 7-14 completed this collaborative task partnered with a social robot that communicated with either high verbal (full sentences), low verbal (short phrases or single words), or nonverbal (sound-based utterances) vocalization. While we found no significant impact on children’s speech patterns or learning due to the robot’s method of vocalization, children in the non-verbal condition had a significantly lower perception of the robot’s intelligence along with higher rates of providing feedback and more instances of undoing its moves. This suggests that a link may exist between a robot’s perceived speaking ability and children’s confidence in that robot’s overall intelligence and capability in a collaborative task, as well as their empathy towards a peer they perceive as less skilled in the task.

关键词： Social robots Poles and towers Education Collaboration Speech recognition Aging Speech

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Designing Online Multiplayer Games with Haptically and Virtually Linked Tangible Robots to Enhance Social interaction in Therapy

Designing Online Multiplayer Games with Haptically and Virtu...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Arzu Guneysu Ozgur Hala Khodr Mehdi Akeddar Michael Roust Pierre Dillenbourg Computer Human Interaction in Learning and Instruction Lab (CHILI) EPFL Lausanne Switzerland Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

The social aspects of therapy and training are important for patients to avoid social isolation and must be considered when designing a platform, especially for home-based rehabilitation. We proposed an online version of the previously proposed tangible Pacman game for upper limb training with haptic-enabled tangible Cellulo robots. Our main objective is to enhance motivation and engagement through social integration and also to form a gamified multiplayer rehabilitation at a distance. Thus, allowing relatives, children, and friends to connect and play with their loved ones while also helping them with their training from anywhere in the world. As well as connecting therapists to their patients through haptically linking capabilities. This is especially relevant when there are social distancing measures which might isolate the elderly population, a majority of all rehabilitation patients.

关键词： Training Pediatrics Sociology Medical treatment Games human factors Social factors

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Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Future Self Agents to Enhance Young Adults’ Career Exploration

arXiv

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arXiv 2025年

作者： Jeon, Hayeon Yoon, Suhwoo Lee, Keyeun Kim, Seo Hyeong Kim, Esther Hehsun Cho, Seonghye Ko, Yena Yang, Soeun Dabbish, Laura Zimmerman, John Kim, Eun-Mee Lim, Hajin hci+d lab. Seoul National University Korea Republic of Department of Communication Seoul National University Korea Republic of Human-Computer Interaction Institute Carnegie Mellon University United States HCI Institute Carnegie Mellon University United States

Young adults often encounter challenges in career exploration. Self-guided interventions, such as the letter-exchange exercise, where participants envision and adopt the perspective of their future selves by exchanging letters with their envisioned future selves, can support career development. However, the broader adoption of such interventions may be limited without structured guidance. To address this, we integrated Large Language Model (LLM)-based agents that simulate participants’ future selves into the letter-exchange exercise and evaluated their effectiveness. A one-week experiment (N=36) compared three conditions: (1) participants manually writing replies to themselves from the perspective of their future selves (baseline), (2) future-self agents generating letters to participants, and (3) future-self agents engaging in chat conversations with participants. Results indicated that exchanging letters with future-self agents enhanced participants’ engagement during the exercise, while overall benefits of the intervention on future orientation, career self-concept, and psychological support remained comparable across conditions. We discuss design implications for AI-augmented interventions for supporting young adults’ career exploration. Copyright © 2025, The Authors. All rights reserved.

关键词： Chatbots

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Keyhan Rayati Amirhossein Feizi Alireza Beigy Pourya Shahverdi Mehdi Tale Masouleh Ahmad Kalhor Wing-Yue Geoffrey Louie Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran Intelligent Robotics Laboratory Oakland University Michigan USA

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot’s responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot’s imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs.

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Semantic Visual Simultaneous Localization and Mapping: A Survey

arXiv

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arXiv 2022年

作者： Chen, Kaiqi Xiao, Junhao Liu, Jialing Tong, Qiyi Zhang, Heng Liu, Ruyu Zhang, Jianhua Ajoudani, Arash Chen, Shengyong Institute of Computer Vision College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China Human-Robot Interfaces and Interaction Lab Istituto Italiano di Tecnologia Genoa Italy National University of Defense Technology China Università di Genova Genoa Italy School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Institute of Computer Vision School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

Visual Simultaneous Localization and Mapping (vSLAM) has achieved great progress in the computer vision and robotics communities, and has been successfully used in many fields such as autonomous robot navigation and AR/VR. However, vSLAM cannot achieve good localization in dynamic and complex environments. Numerous publications have reported that, by combining with the semantic information with vSLAM, the semantic vSLAM systems have the capability of solving the above problems in recent years. Nevertheless, there is no comprehensive survey about semantic vSLAM. To fill the gap, this paper first reviews the development of semantic vSLAM, explicitly focusing on its strengths and differences. Secondly, we explore three main issues of semantic vSLAM: the extraction and association of semantic information, the application of semantic information, and the advantages of semantic vSLAM. Then, we collect and analyze the current state-of-the-art SLAM datasets which have been widely used in semantic vSLAM systems. Finally, we discuss future directions that will provide a blueprint for the future development of semantic vSLAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Practice-based teacher education with ELK: A role-playing simulation for eliciting learner knowledge 14

Practice-based teacher education with ELK: A role-playing si...

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14th International Conference of the Learning Sciences: The Interdisciplinarity of the Learning Sciences, ICLS 2020

作者： Wang, Xu Thompson, Meredith Yang, Kexin Roy, Dan Koedinger, Kenneth Rose, Carolyn Reich, Justin Human-Computer Interaction Institute Carnegie Mellon University Teaching Systems Lab Massachusetts Institute of Technology

ISBN: (纸本)9781732467286

Compared to other helping professions, teacher training typically lacks sufficient opportunities for novices to practice new skills. When teachers learn, they listen to people talk about teaching, or talk about teaching themselves, but they very rarely do the work of teaching. Games and simulations offer a promising framework to advance practice-based professional training for complex skills such as teaching. In this work, we built a role-playing simulation ELK to help teachers develop effective questioning strategies to elicit learner prior knowledge. We evaluate ELK with 76 pre-service teachers in two modes, "Chat" and "Code". We find that in both modes ELK raises teachers' awareness about student misconceptions and the importance of student thinking, and teachers demonstrate a modest increase in effective questioning strategies after three rounds of playing. © 2020 International Society of the Learning Sciences (ISLS). All rights reserved.

关键词： Personnel training

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

arXiv

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arXiv 2024年

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 Zhidayuan AI Lab. 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

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. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal encoding

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