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共 142 条记录，以下是1-10 订阅

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Exploring the potential of using growing materials in design through workshops 24

Exploring the potential of using growing materials in design...

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17th International Symposium on Visual Information Communication and interaction

作者： Huang, Yi-Pin Lin, Ting-Wei Chou, Chih-Chan Hsu, Chun Cheng Shih Chien Univ Coll Design VISION BASE Taipei Taiwan Natl Yang Ming Chiao Tung Univ Inst Appl Arts Hsinchu Taiwan BioCreator Lab New Taipei Taiwan

ISBN: (纸本)9798400709678

In the field of design, material application is everywhere, and the potential to use living biological materials is beginning to be explored. Incorporating growing materials into design presents numerous challenges, particularly in managing the dynamic processes of change, shaping, and decay. This study aims to explore these possibilities through a workshop format, examine the potential applications and roles of biological materials in design, and experiment with the integration of growing materials with mechanical equipment. The research findings highlight the unique characteristics of biological design in narrative construction, and the ways machines can interact with growing materials to achieve morphological changes.

关键词： Design Biodesign Interactivity human computer interaction

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Envisioning the Next-Generation AI Coding Assistants: Insights & Proposals 1

Envisioning the Next-Generation AI Coding Assistants: Insigh...

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1st IDE workshop (IDE)

作者： Khanh Nghiem Anh Minh Nguyen Bui, Nghi D. Q. FPT Software AI Ctr Hanoi Vietnam Fulbright Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9798400705809

As a research-product hybrid group in AI for Software Engineering (AI4SE), we present four key takeaways from our experience developing in-IDE AI coding assistants. AI coding assistants should set clear expectations for usage, integrate with advanced IDE capabilities and existing extensions, use extendable backend designs, and collect app data responsibly for downstream analyses. We propose open questions and challenges that academia and industry should address to realize the vision of next-generation AI coding assistants.

关键词： IDE Artificial Intelligence human-computer interaction Large Language Models Docify AI CodeVista GitHub Copilot AI4SE

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human-robot collaboration in healthcare: new programming and interaction techniques 9

Human-robot collaboration in healthcare: new programming and...

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Joint of the workshops, Work in Progress Demos and Doctoral Consortium at the 9th International Symposium on End-user Development, WWDD@IS-EUD 2023

作者： Gargioni, Luigi Department of Information Engineering University of Brescia Via Branze 38 Brescia25123 Italy

In the field of healthcare, collaborative robots can be utilized to enhance productivity and efficiency. This research will delve into the development and investigation of new techniques to enable effective interaction with collaborative robots. The focus will be on designing innovative techniques that can be easily understood and implemented by individuals with no technical knowledge in computer science or robotics. Collaborative robots can be used to automate repetitive tasks, allowing healthcare professionals to focus on more complex procedures. For instance, by leveraging this technology, it is possible to significantly increase the efficiency and speed of therapy preparation, ultimately improving patient outcomes. The study will explore various approaches to simplify and streamline the programming process, reducing the need for technical knowledge and expertise. © 2023 Copyright for this paper by its authors. use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Efficiency

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Low Obstacle Avoidance for Lower Limb Exoskeletons 9

Low Obstacle Avoidance for Lower Limb Exoskeletons

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9th Italian workshop on Artificial Intelligence and Robotics, AIRO 2022

作者： Trombin, Edoardo Tortora, Stefano Bettella, Francesco Del Felice, Alessandra Tonin, Luca Menegatti, Emanuele Department of Information Engineering University of Padova 6b G. Gradenigo Padova35131 Italy Padova Neuroscience Center University of Padova 2 G. Orus Padova35131 Italy Department of Neuroscience University of Padova 160 Belzoni Padova35131 Italy

Powered lower limb exoskeletons (LLEs) are innovative wearable robots that allow independent walking in people with severe gait impairments. Despite the recent advancements, the use of this promising technology is still restricted to clinical settings;uptake in real-life conditions as a device to promote user independence is still lacking due to the difficulty of controlling these devices in unstructured and complex environments. In this work, we propose a vision-assisted method for low obstacle avoidance to enhance the autonomy of LLEs. The exoskeleton collects information from the surroundings through a RGB-D camera to recognize and segment objects on the ground that might affect the walking pattern. Then, the method identifies suitable foothold positions. In addition, a novel iterative gait trajectory generator is proposed to automatically compute collision-free walking paths. We believe that re-thinking exoskeletons as semi-autonomous agents will represent not only the cornerstone to promote a more symbiotic human-exoskeleton interaction but may also pave the way for the use of this technology in the everyday life. © 2022 Copyright for this paper by its authors.

关键词： computer vision

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Beyond Active Learning: Leveraging the Full Potential of human interaction via Auto-Labeling, human Correction, and human Verification

Beyond Active Learning: Leveraging the Full Potential of Hum...

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IEEE workshop on Applications of computer vision (WACV)

作者： Nathan Beck Krishnateja Killamsetty Suraj Kothawade Rishabh Iyer University of Texas Dallas

Active Learning (AL) is a human-in-the-loop framework to interactively and adaptively label data instances, thereby enabling significant gains in model performance compared to random sampling. AL approaches function by selecting the hardest instances to label, often relying on notions of diversity and uncertainty. However, we believe that these current paradigms of AL do not leverage the full potential of human interaction granted by automated label suggestions. Indeed, we show that for many classification tasks and datasets, most people verifying if an automatically suggested label is correct take 3× to 4× less time than they do changing an incorrect suggestion to the correct label (or labeling from scratch without any suggestion). Utilizing this result, we propose Clarifier (aCtive LeARnIng From tIEred haRdness), an Interactive Learning framework that admits more effective use of human interaction by leveraging the reduced cost of verification. By targeting the hard (uncertain) instances with existing AL methods, the intermediate instances with a novel label suggestion scheme using submodular mutual information functions on a per-class basis, and the easy (confident) instances with highest-confidence auto-labeling, Clarifier can improve over the performance of existing AL approaches on multiple datasets – particularly on those that have a large number of classes – by almost 1.5× to 2× in terms of relative labeling cost.

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REHAB 2022 - Proceedings of the 6th workshop on ICTS for Improving Patients Reahbilitation Research Techniques, co-located with Conference on human-computer interaction, Interaccion 2022

REHAB 2022 - Proceedings of the 6th Workshop on ICTS for Imp...

引用

6th workshop on ICTS for Improving Patients Reahbilitation Research Techniques, REHAB 2022

The proceedings contain 8 papers. The topics discussed include: solution for immersive vestibular rehabilitation in patients with multiple sclerosis by using virtual reality and its use within the tele-rehabilitation paradigm;affective computing from digital health: a literature review;the role of ICT in the development of children’s’ with disabilities;evaluation of user satisfaction in a new virtual reality system for rehabilitation in children;model of emotional measurement in children with autism spectrum disorder using biometric sensors;eye tracking for usability analysis and application to rehabilitation;and prototype of a no invasive rehabilitation system to training impairing vision problem caused by amblyopia in early children.

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ALTRUIST 2022 - Proceedings of the 2nd workshop on sociAL roboTs for peRsonalized, continUous and adaptIve aSsisTance, co-located with the 14th International Conference on Social Robotics, ICSR 2022

ALTRUIST 2022 - Proceedings of the 2nd Workshop on sociAL ro...

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2nd workshop on sociAL roboTs for peRsonalized, continUous and adaptIve aSsisTance, ALTRUIST 2022

The proceedings contain 10 papers. The topics discussed include: towards enhancing social navigation through contextual and human-related knowledge;Physiosmart: a preliminary study about the quality of rehabilitation using a computer vision approach;please ASTRO, can you follow me? design of a social assistive robot for monitoring gait parameters;an environment to collect personal memories of older adults and use them to personalize serious games with humanoid robots;pedestrian and autonomous vehicles interaction: towards affective crossing;identification of the highest wrinkle grasping point of a folded hospital gown;investigating the role of different social cues in the human perception of a social robotic arm;initial design thinking for a robotic poker dealer;and towards adaptation of humanoid robot behavior in serious game scenarios using reinforcement learning.

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Design and Implementation of a vision-Based Vehicle Control System for Operational Commands via human Keypoints Recognition

Design and Implementation of a Vision-Based Vehicle Control ...

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Intelligent Systems, International workshop on

作者： Ge Cao Yue Teng Yu Shen Qing Tang Tran Tien Dat Ashraf Uddin Russo Adri Priadana Kanghyun Jo Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan Korea Republic of Hyundai Motor Technology & Engineering Center (China) LTD. Department of Mechanical and Automotive Engineering University of Ulsan Ulsan Korea Republic of Data Science Group INTERX Ulsan Korea Republic of

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

With the advent of new energy vehicles, enhanced energy storage capabilities have provided a robust operating environment for complex intelligent systems, which rely heavily on onboard computers and a multitude of sensors. This paper introduces a vision-based intelligent system in vehicles that utilizes onboard vehicle cameras as input sensors and leverages multiple neural network architectures along with meticulously designed algorithms to facilitate human-vehicle interaction. Within this intelligent system, we sequentially employ a human detection model and a human keypoints recognition model to predict the position of target keypoints, marking their trajectories in real-time video streams for use in critical command recognition algorithms. Furthermore, this study explores the computational resource consumption and the processing power of onboard computers, affirming that the system can be applied in real time to the majority of current onboard computing systems.

关键词： computers Target recognition Computational modeling Neural networks Predictive models Prediction algorithms Real-time systems Sensor systems Intelligent systems Intelligent sensors

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Fusing Components for an Attentive and Emotionally Expressive Companion Robot: Meet ZENIT

Fusing Components for an Attentive and Emotionally Expressiv...

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IEEE International workshop on Robot and human Communication (ROMAN)

作者： Christian Felix Purps Matthias Wölfel Institute for Intelligent Interaction and Immersive Experience Karlsruhe University of Applied Sciences Karlsruhe Germany University of Hohenheim Stuttgart Germany

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

This paper introduces a new companion robot as an open research platform for studying human-robot interaction: The ZENIT Enabling Natural interaction Technology. Open-source solutions for companion robots are often complicated to build or not thought of holistically. ZENIT is based on a few low-cost components, namely a smartphone and a stationary robot arm in conjunction with an average computer and a separate camera. This concept makes it possible not to be tied to specific hardware. Our platform aims to enable easy use of the multimodality of human communication channels such as body language, spatial behavior, facial expressions and speech so that ZENIT can understand its interlocutor and express itself appropriately. The fusion of various open-source software components in an adaptable, lightweight and resource-efficient distributed system simplifies its replication. The system demonstrated an adequate response time (speech: 2.69s, facial expressions: 0.55s, bodily movements: 0.32s) for perception and expression combined, as well as reliability in public testing. Evaluation of the robot’s expressive capabilities in an online survey (N=23) revealed users were able to understand six of eight emotions conveyed by ZENIT using facial expressions and/or body language (except for contempt and fear) and perceived its non-verbal feedback as significantly more expressive when facial expressions were accompanied with bodily movements.

关键词： Surveys Robot vision systems human-robot interaction Manipulators Hardware Software reliability Time factors Robots Open source software Testing

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Automatic RGB Inference Based on Facial Emotion Recognition

Automatic RGB Inference Based on Facial Emotion Recognition

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2021 Scholar's Yearly Symposium of Technology, Engineering and Mathematics, SYSTEM 2021

作者： Brandizzi, Nicolo Bianco, Valerio Castro, Giulia Russo, Samuele Wajda, Agata Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00135 Italy Department of Psychology Sapienza University of Rome Via Ariosto 25 Rome00135 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland

Recently, Facial Emotion Recognition (FER) has been one of the most promising and growing field in computer vision and human-robot interaction. In this work, a deep learning neural network is introduced to address the problem of facial emotion recognition. In particular, a CNN+RNN architecture has been designed to capture both spatial features and temporal dynamics of facial expressions. Experiments are performed on CK+ dataset. Furthermore, we present a possible application of the proposed Facial Emotion Recognition system in human-robot interaction. A method for dynamically changing ambient light or LED colors, based on recognized emotions is presented. Indeed, it is proven that equipping robots with the ability of perceiving emotions and accordingly reacting by introducing suitable emphatic strategies significantly improves human-robot interaction performances. Possible scenarios of application are education, healthcare and autism therapy where such kind of emphatic strategies play a fundamental role. Copyright for this paper by its authors. use permitted under Creative.

关键词： Emotion Recognition

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