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

作者： Luca Beber Edoardo Lamon Giacomo Moretti Daniele Fontanelli Matteo Saveriano Luigi Palopoli Department of Information Engineering and Computer Science Università di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy Department of Industrial Engineering Università di Trento Trento Italy

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper introduces a new method for online estimating the penetration of the end-effector and the viscoelastic properties of a soft body, through palpation exams using a collaborative robotic arm. The estimator is based on the dimensionality reduction method that simplifies the nonlinear Hunt-Crossley model. In addition, in our algorithm, the model parameters can be found without a force sensor, leveraging only the robotic arm controller data. An extended Kalman filter is employed to achieve online estimation, which embeds the dynamic contact model. The algorithm is tested with various types of silicone, a material that resembles biological tissues, including samples with hard intrusions to simulate cancerous cells within a softer tissue. The results indicate that this technique can accurately determine the model parameters and estimate the penetration of the end-effector into the soft body. These promising preliminary results demonstrate robots’ potential to be an effective tool for early-stage cancer diagnostics.

关键词： Dimensionality reduction Biological system modeling Heuristic algorithms Biological tissues Estimation Collaboration Robot sensing systems Information filters End effectors Kalman filters

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How context and design shape human-robot trust and attributions

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Paladyn 2021年第1期12卷 74-86页

作者： Biermann, Hannah Brauner, Philipp Ziefle, Martina Chair for Communication Science and Human-Computer Interaction Center RWTH Aachen University Campus-Boulevard 57 Aachen52074 Germany

In increasingly digitized working and living environments, human-robot collaboration is growing fast with human trust toward robotic collaboration as a key factor for the innovative teamwork tosucceed. This article explores the impact of design factors of the robotic interface (anthropomorphic vs functional) and usage context (production vs care) on human-robot trust and attributions. Theresults of a scenario-based survey with N = 228 N=228 participants showed a higher willingness to collaborate with production robots compared to care. Context and design influenced the trust attributed to the robots: robots with a technical appearance in production were trusted more than anthropomorphic robots or robots in the care context. The evaluation of attributions by means of a semanticdifferential showed that differences in robot design were less pronounced for the production context in comparison to the care context. In the latter, anthropomorphic robots were associated with positive attributes. The results contribute to a better understanding of the complex nature of trust in automation and can be used to identify and shape use case-specific risk perceptions as well as perceived opportunities to interacting with collaborative robots. Findings of this study are pertinent to research (e.g., experts in human-robot interaction) and industry, with special regard given tothe technical development and design. © 2021 Hannah Biermann et al., published by De Gruyter.

关键词： Risk perception

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A Passivity-Based Variable Impedance Controller for Incremental Learning of Periodic Interactive Tasks

A Passivity-Based Variable Impedance Controller for Incremen...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Matteo Dalle Vedove Edoardo Lamon Daniele Fontanelli Luigi Palopoli Matteo Saveriano Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In intelligent manufacturing, robots are asked to dynamically adapt their behaviours without reducing productivity. human teaching, where an operator physically interacts with the robot to demonstrate a new task, is a promising strategy to quickly and intuitively reconfigure the production line. However, physical guidance during task execution poses challenges in terms of both operator safety and system usability. In this paper, we solve this issue by designing a variable impedance control strategy that regulates the interaction with the environment and the physical demonstrations, explicitly preventing at the same time passivity violations. We derive constraints to limit not only the exchanged energy with the environment but also the exchanged power, resulting in smoother interactions. By monitoring the energy flow between the robot and the environment, we are able to distinguish between disturbances (to be rejected) and physical guidance (to be accomplished), enabling smooth and controlled transitions from teaching to execution and vice versa. The effectiveness of the proposed approach is validated in wiping tasks with a real robotic manipulator.

关键词： Surface impedance Incremental learning Tracking Education End effectors Safety Impedance Usability Robots Oscillators

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On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook

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IEEE Journal of Biomedical and Health Informatics 2025年第6期PP卷 PP页

作者： Bucur, Ana-Maria Moldovan, Andreea-Codrina Parvatikar, Krutika Zampieri, Marcos Khudabukhsh, Ashiqur R. Dinu, Liviu P. University of Bucharest Interdisciplinary School of Doctoral Studies Bucharest050663 Romania Universitat Politècnica de València PRHLT Research Center València46020 Spain Rochester Institute of Technology RochesterNY14623 United States George Mason University Fairfax22030 United States University of Bucharest Human Language Technology Research Center Department of Computer Science Faculty of Mathematics and Computer Science Bucharest050663 Romania

Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple reviews have been published on this topic, providing the community with comprehensive accounts of the research in this area. Among all mental health conditions, depression is the most widely studied due to its worldwide prevalence. The COVID-19 global pandemic, starting in early 2020, has had a great impact on mental health worldwide. Harsh measures employed by governments to slow the spread of the virus (e.g., lockdowns) and the subsequent economic downturn experienced in many countries have significantly impacted people's lives and mental health. Studies have shown a substantial increase of above 50% in the rate of depression in the population. In this context, we present a review on natural language processing (NLP) approaches to modeling depression in social media, providing the reader with a post-COVID-19 outlook. This review contributes to the understanding of the impacts of the pandemic on modeling depression in social media. We outline how state-of-the-art approaches and new datasets have been used in the context of the COVID-19 pandemic. Finally, we also discuss ethical issues in collecting and processing mental health data, considering fairness, accountability, and ethics. © 2013 IEEE.

关键词： COVID-19

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Correction to: Object search by a concept-conditioned object detector (Neural Computing and Applications, (2024), 36, 26, (16001-16021), 10.1007/s00521-024-09914-5)

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Neural Computing and Applications 2025年 1-2页

作者： Rigoni, Davide Serafini, Luciano Sperduti, Alessandro Department of Mathematics "Tullio Levi-Civita" University of Padova Via Trieste 63 PD Padua35131 Italy Human Inspired Technology Research Centre University of Padova Via Luzzatti 4 PD Padua35121 Italy Augmented Intelligence Center Bruno Kessler Foundation Via Sommarive 18 TN Povo38123 Italy Department of Pharmaceutical and Pharmacological Sciences University of Padova Via Marzolo 5 PD Padua35131 Italy Department of Information Engineering and Computer Science University of Trento Via Sommarive 9 TN Povo38123 Italy

Correction To: Neural Computing and Applications (2024) 36:16001–16021 https://***/10.1007/s00521-024-09914-5 In this article, the sentence ‘The models conditioned with the concepts are highlighted with the dove gray color’ in the footnote of Tables 1, 4 and 5 should be removed. Similarly, the sentence ‘Concept-conditioned models are highlighted with the dove gray color’ in the footnote of Tables 2 and 3 should be removed. In the section ‘Standard vs. concept-conditioned object detectors before filtering’, the sentence ‘Models conditioned with the concepts are highlighted with the dove gray color’ should be removed. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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Machine learning-based prediction of low-value care for hospitalized patients

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Intelligence-Based Medicine 2023年 8卷 100115-100115页

作者： King, Andrew J. Tang, Lu Davis, Billie S. Preum, Sarah M. Bukowski, Leigh A. Zimmerman, John Kahn, Jeremy M. Department of Critical Care Medicine University of Pittsburgh School of Medicine Pittsburgh PA United States Department of Biostatistics University of Pittsburgh School of Public Health Pittsburgh PA United States Department of Computer Science Dartmouth College Hanover NH United States Human-Computer Interaction Institute Carnegie Mellon University School of Computer Science Pittsburgh PA United States

Objective: Low-value care (i.e., costly health care treatments that provide little or no benefit) is an ongoing problem in United States hospitals. Traditional strategies for reducing low-value care are only moderately successful. Informed by behavioral science principles, we sought to use machine learning to inform a targeted prompting system that suggests preferred alternative treatments at the point of care but before clinicians have made a decision. Methods: We used intravenous administration of albumin for fluid resuscitation in intensive care unit (ICU) patients as an exemplar of low-value care practice, identified using the electronic health record of a multi-hospital health system. We divided all ICU episodes into 4-h periods and defined a set of relevant clinical features at the period level. We then developed two machine learning models: a single-stage model that directly predicts if a patient will receive albumin in the next period;and a two-stage model that first predicts if any resuscitation fluid will be administered and then predicts albumin only among the patients with a high probability of fluid use. Results: We examined 87,489 ICU episodes divided into approximately 1.5 million 4-h periods. The area under the receiver operating characteristic curve was 0.86 for both prediction models. The positive predictive value was 0.21 (95% confidence interval: 0.20, 0.23) for the single-stage model and 0.22 (0.20, 0.23) for the two-stage model. Applying either model in a targeted prompting system could prevent 10% of albumin administrations, with an attending physician receiving one prompt every 4.2 days of ICU service. Conclusion: Prediction of low-value care is feasible and could enable a point-of-care, targeted prompting system that offers suggestions ahead of the moment of need before clinicians have already decided. A two-stage approach does not improve performance but does interject new levers for the calibration of such a system. © 2023 The Authors

关键词： Clinical decision support Evidence-based medicine Low-value care Machine learning Resuscitation

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Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport

Bilingual Lexicon Induction for Low-Resource Languages using...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Marchisio, Kelly Saad-Eldin, Ali Duh, Kevin Priebe, Carey Koehn, Philipp Department of Computer Science Johns Hopkins University United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Biomedical Engineering Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. We improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision. © 2022 Association for Computational Linguistics.

关键词： Natural language processing systems

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Eye Drop Instillation Success and Hand Function in Adults with Glaucoma: A Pilot Study

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Ophthalmology Glaucoma 2025年第3期8卷 275-284页

作者： Weber, Madeline K. Kabil, Gül G. Niziol, Leslie M. Urrego, Daniel Duque Haire, Cameron Cain, Stephen M. Sample, Alanson P. Burke, David T. Brown, Susan H. Newman-Casey, Paula Anne University of Michigan Medical School Ann Arbor MI United States University of Michigan School of Kinesiology Ann Arbor MI United States Department of Ophthalmology and Visual Sciences University of Michigan W.K. Kellogg Eye Center Ann Arbor MI United States Department of Chemical and Biomedical Engineering West Virginia University Morgantown WV United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States Department of Human Genetics University of Michigan Ann Arbor MI United States

Purpose: To investigate hand function and eye drop instillation success in adults with and without glaucoma. Design: Cross-sectional pilot study. Subjects: Adults aged ≥ 65 years with glaucoma who use eye drops daily and adults aged 65+ without glaucoma who do not regularly use eye drops. Methods: Hand function was evaluated using the Saehan Hydraulic Pinch Gauge, Jamar Hand Dynamometer, Grooved Pegboard Test (GPT), Arthritis Hand Function Tests (AHFT), Semmes-Weinstein Monofilament Test, and a tactile pattern recognition device. Eye drop instillation success was analyzed through videography. Main Outcome Measures: Proportion of successful eye drop instillation trials assessed by (1) overall success in getting at least one drop in the eye;(2) average number of drops dispensed;and (3) bottle tip contact with the eye, eyelashes, or skin. Hand function measures: mean pinch and grip strength, GPT and AHFT completion times, smallest detected monofilament, tactile pattern identification time, and accuracy. Results: A total of 25 participants with glaucoma and 79 participants without glaucoma were included. There was no difference in the proportion of trials where at least one drop was successfully instilled, disregarding bottle tip contact (glaucoma: 0.95, nonglaucoma: 0.91;P = 0.88). Participants with glaucoma were more likely to make contact between the bottle tip and the eye, eyelashes, or skin compared with those without (glaucoma: 0.49, nonglaucoma: 0.28;P = 0.01). Participants with glaucoma dispensed a similar number of drops as those without (glaucoma: 1.37, nonglaucoma: 1.46;P = 0.47). Participants with glaucoma had significantly reduced pinch strength (glaucoma: 4.8 kg, nonglaucoma 6.1 kg, P = 0.01), grip strength (glaucoma: 23.4 kg, nonglaucoma: 27.7 kg, P = 0.02), longer completion times for the GPT (glaucoma: 113.5 seconds, nonglaucoma: 85.5 seconds, P = 0.02), and specific AHFT tasks of fastening/unfastening buttons (glaucoma: 36.6 seconds, nonglaucoma: 27.7

关键词： Eye drop instillation success Glaucoma Hand function Sensorimotor deficits

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Learning Multimodal Latent Dynamics for human-Robot interaction

arXiv

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arXiv 2023年

作者： Prasad, Vignesh Heitlinger, Lea Koert, Dorothea Stock-Homburg, Ruth Peters, Jan Chalvatzaki, Georgia Department of Computer Science TU Darmstadt Germany Chair for Marketing and Human Resource Management Department of Law and Economics TU Darmstadt Germany Department of Computer Science TU Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Germany Darmstadt Germany Interactive Robot Perception and Learning group Department of Computer Science TU Darmstadt Germany Center for Mind Brain & Behavior Uni. Marburg JLU Giessen Germany

This article presents a method for learning wellcoordinated human-Robot interaction (HRI) from human- human interactions (HHI). We devise a hybrid approach using Hidden Markov Models (HMMs) as the latent space priors for a Variational Autoencoder to model a joint distribution over the interacting agents. We leverage the interaction dynamics learned from HHI to learn HRI and incorporate the conditional generation of robot motions from human observations into the training, thereby predicting more accurate robot trajectories. The generated robot motions are further adapted with Inverse Kinematics to ensure the desired physical proximity with a human, combining the ease of joint space learning and accurate task space reachability. For contact-rich interactions, we modulate the robot's stiffness using HMM segmentation for a compliant interaction. We verify the effectiveness of our approach deployed on a humanoid robot via a user study. Our method generalizes well to various humans despite being trained on data from just two humans. We find that Users perceive our method as more human-like, timely, and accurate and rank our method with a higher degree of preference over other baselines. © 2023, CC BY-NC-SA.

关键词： Anthropomorphic robots

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miR-204 ameliorates osteoarthritis pain by inhibiting SP1-LRP1 signaling and blocking neuro-cartilage interaction

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Bioactive Materials 2023年第8期26卷 425-436页

作者： Ke Lu Qingyun Wang Liuzhi Hao Guizheng Wei Tingyu Wang William WLu Guozhi Xiao Liping Tong Xiaoli Zhao Di Chen Research Center for Computer-aided Drug Discovery Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Faculty of Pharmaceutical Sciences Shenzhen Institute of Advanced TechnologyShenzhen518055China Research Center for Human Tissues and Organs Degeneration Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Department of Pharmacy Shanghai Ninth People’s HospitalShanghai Jiao-Tong University School of MedicineShanghai200011China Department of Orthopaedics and Traumatology The University of Hong KongHong Kong 999077China School of Medicine Southern University of Science and TechnologyShenzhen518055China

Osteoarthritis(OA)is a painful degenerative joint disease and is the leading cause of chronic disability among elderly *** improve the quality of life for patients with OA,the primary goal for OA treatment is to relieve the *** OA progression,nerve ingrowth was observed in synovial tissue and articular *** abnormal neonatal nerves act as nociceptors to detect OA pain *** molecular mechanisms for transmitting OA pain in the joint tissues to the central nerve system(CNS)is currently *** miR-204 has been demonstrated to maintain the homeostasis of joint tissues and have chondro-protective effect on OA ***,the role of miR-204 in OA pain has not been *** this study,we investigated interactions between chondrocytes and neural cells and evaluated the effect and mechanism of miR-204 delivered by exosome in the treatment of OA pain in an experimental OA mouse *** findings demonstrated that miR-204 could protect OA pain by inhibition of SP1-LDL Receptor Related Protein 1(LRP1)signaling and blocking neuro-cartilage interaction in the *** studies defined novel molecular targets for the treatment of OA pain.

关键词： miR-204 LRP1 Neuro-cartilage interaction Osteoarthritis Pain

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