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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mekni, Amal Narayan, Jyotindra Gritli, Hassene Borj Cedria Tunis1164 Tunisia Indian Institute of Technology Guwahati Mechtronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India University of Tunis El Manar le Belvédère Laboratory of Robotics Informatics and Complex Systems RISC Lab-LR16ES07 National Engineering School of Tunis Tunis1002 Tunisia

ISBN: (纸本)9798350370249

Walking is a fundamental human activity, yet understanding its intricacies is crucial for diagnosing and treating various gait abnormalities and musculoskeletal disorders. This study investigates the machine learning-based classification of gait phases into five subphases with two training methods. Using data from 100 individuals obtained from an open-source platform. The gait cycle was divided into the following subphases: Initial Contact, Loading Response, Mid Stance, Terminal Stance, and Pre-Swing. In the first approach, a stratified random sampling method was employed, allocating 80% of data from each subphase for training and 20% for testing. The second method involved training with data from 80% participants and testing with data from 20% participants. Out of different ML algorithms, the Random Forest (RF) achieves the highest performance (99.2%) with similar results across both methods. The k-Nearest Neighbors (k-NN), the Logistic Regression (LR), the Decision Tree (DT), and the Support Vector Machine (SVM) show comparable performance ( 91%) with marginal improvement with the second method. Naive Bayes (NB) has a significantly lower performance ( 77.7%). These findings suggest that RF is a robust and effective algorithm for this task. Furthermore, the study indicates that applying machine learning techniques to datasets divided by subjects presents promising opportunities for creating precise gait analysis systems. This can assist clinicians and researchers in enhancing patient care and gaining deeper insights into human locomotion. © 2024 IEEE.

关键词： Adversarial machine learning

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Detection and classification of failures as an emergent behavior in a machinery system modelled as a system of systems

Detection and classification of failures as an emergent beha...

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IEEE International Conference on System of systems engineering (SoSE)

作者： Roberto Sacile Mohamed Sallak Enrico Zero Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genoa Italy Department Universite de Technologie de Compiègne (UTC) Compiègne France

In Industry 4.0 context predictive maintenance is a hot topic with several challenges that can be investigated by a system of systems engineering approach where the emergent behaviour can be related to possible system failures. The Internet of Things (IoT) can be also used to monitor and control the different system components. This work proposes a simple approach to detect, monitor, and control emergent behavior by IoT sensors. Starting from the raw data which were daily extracted from sensors, an analysis of the collected signals is performed to help decision-making concerning its maintenance. In the case study related to a door system in a bus, the proposed method can recognize the different damage states analysing the input parameters: temperature, pressure, and humidity of door systems. Some recent reliability performance indicators are used to evaluate the damage classification.

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Integrating Function and Connection: Two Perspectives to Telepresence Robot Design for Classroom 25

Integrating Function and Connection: Two Perspectives to Tel...

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2025 ACM International Conference on Supporting Group Work, GROUP Companion 2025

作者： Berhane, Roda Akiode, Deo Adejoke Joshi, Swapna Elmimouni, Houda School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Field Robotics Lab Northeastern University BostonMA United States Department of Computer Science University of Manitoba WinnipegMB Canada Information Systems and New Media University of Siegen Siegen Germany

ISBN: (纸本)9798400711879

This study explores robotic telepresence in classrooms, from the perspectives of both the remote students (operators) and their in-person classmates. Analyzing a subset of field study data from 35 participants (22 operators, 13 classmates), we identified differing priorities: operators of the robot emphasize the need for improved functional aspects like camera control, while co-present classmates value social presence, interaction and identification, advocating for features like personalization and effective communication channels. These insights underline the need for telepresence robots that balance functional efficacy with social integration to provide a holistic educational experience. This work, though incomplete, enriches Group and CSCW literature on telepresence robots to facilitate collaborative interactions by highlighting the distinct needs of remote and in-person students, proposing design recommendations for telepresence robots that are technically proficient and socially responsive. © 2025 Copyright held by the owner/author(s).

关键词： Students

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How 3D scaffolds with different mechanical properties affect the activity of neuronal networks in in vitro models*

How 3D scaffolds with different mechanical properties affect...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Francesca Callegari Martina Brofiga Mariateresa Tedesco Paolo Massobrio Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Genova Italy Co-Founder and CTO of ScreenNeuroPharm s.r.l Sanremo Italy

Three-dimensionality has been proven extensively to be critical in the development of a reliable model for different anatomical compartments and for many diseases. Currently, we can produce implantable structures that help in the regeneration of different tissues such as bone and heart. Different is the situation when we consider the neuronal compartment. As it is still difficult to understand exactly how the brain computes, to conceive how the complex chain of neuronal events can generate conscious behavior, a comprehensive and workable model of neuronal tissue still has to be found. In this perspective, in the present work, we developed and compared different 3D scaffolds to understand the effects produced by the mechanical and material properties of four different scaffolds on a 3D neuronal network. To help in preclinical testing procedure, the scalability and ease-of-use of the different approaches were also taken into *** Relevance— By comparing different 3D scaffolds for the creation of neuronal constructs, the results in this paper move towards understanding the best strategy to develop functional 3D neuronal units for reliable pre-clinical studies.

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The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach

arXiv

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arXiv 2024年

作者： Benedetti, Giacomo Cofano, Serena Brighente, Alessandro Conti, Mauro Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy IMT School for Advanced Studies Lucca Italy Department of Mathematics University of Padua Italy Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

The Software Supply Chain (SSC) security is a critical concern for both users and developers. Recent incidents, like the SolarWinds Orion compromise, proved the widespread impact resulting from the distribution of compromised software. The reliance on open-source components, which constitute a significant portion of modern software, further exacerbates this risk. To enhance SSC security, the Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. However, despite its promise, SBOMs are not without limitations. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies, leading to the creation of erroneous or incomplete representations of the SSC. Despite existing studies exposing these limitations, their impact on the vulnerability detection capabilities of security tools is still unknown. In this paper, we perform the first security analysis on the vulnerability detection capabilities of tools receiving SBOMs as input. We comprehensively evaluate SBOM generation tools by providing their outputs to vulnerability identification software. Based on our results, we identify the root causes of these tools' ineffectiveness and propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings. PIP-sbom provides improved accuracy in component identification and dependency resolution. Compared to best-performing state-of-the-art tools, PIP-sbom increases the average precision and recall by 60%, and reduces by ten times the number of false positives. © 2024, CC BY-SA.

关键词： Input output programs

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Advances in Wireless,Batteryless,Implantable Electronics for Real‑Time,Continuous Physiological Monitoring

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Nano-Micro Letters 2024年第3期16卷 254-302页

作者： Hyeonseok Kim Bruno Rigo Gabriella Wong Yoon Jae Lee Woon‑Hong Yeo IEN Center for Wearable Intelligent Systems and Healthcare Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Tech and Emory University School of MedicineAtlantaGA 30332USA Parker H.Petit Institute for Bioengineering and Biosciences Institute for MaterialsInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

This review summarizes recent progress in developing wireless,batteryless,fully implantable biomedical devices for real-time continuous physiological signal monitoring,focusing on advancing human health *** considerations,such as biological constraints,energy sourcing,and wireless communication,are discussed in achieving the desired performance of the devices and enhanced interface with human *** addition,we review the recent achievements in materials used for developing implantable systems,emphasizing their importance in achieving multi-functionalities,biocompatibility,and *** wireless,batteryless devices offer minimally invasive device insertion to the body,enabling portable health monitoring and advanced disease ***,we summarize the most recent practical applications of advanced implantable devices for human health care,highlighting their potential for immediate commercialization and clinical uses.

关键词： Implantable electronics Biomedical systems Batteryless devices Wireless electronics Physiological signal monitoring

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Whole-Body MPC for Highly Redundant Legged Manipulators: Experimental Evaluation with a 37 DoF Dual-Arm Quadruped

Whole-Body MPC for Highly Redundant Legged Manipulators: Exp...

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IEEE-RAS International Conference on Humanoid Robots

作者： Ioannis Dadiotis Arturo Laurenzi Nikos Tsagarakis Humanoid and Human-Centered Mechatronics Research Line Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy

Recent progress in legged locomotion has rendered quadruped manipulators a promising solution for performing tasks that require both mobility and manipulation (loco-manipulation). In the real world, task specifications and/or environment constraints may require the quadruped manipulator to be equipped with high redundancy as well as whole-body motion coordination capabilities. This work presents an experimental evaluation of a whole-body Model Predictive Control (MPC) framework achieving real-time performance on a dual-arm quadruped platform consisting of 37 actuated joints. To the best of our knowledge this is the legged manipulator with the highest number of joints to be controlled with real-time whole-body MPC so far. The computational efficiency of the MPC while considering the full robot kinematics and the centroidal dynamics model builds upon an open-source DDP-variant solver and a state-of-the-art optimal control problem formulation. Differently from previous works on quadruped manipulators, the MPC is directly interfaced with the low-level joint impedance controllers without the need of designing an instantaneous whole-body controller. The feasibility on the real hardware is showcased using the CENTAURO platform for the challenging task of picking a heavy object from the ground. Dynamic stepping (trotting) is also showcased for first time with this robot. The results highlight the potential of replanning with whole-body information in a predictive control loop.

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Detecting the Unseen: Exploiting Radar-Sonar Sensor Fusion for Visual Detection of Low-Profile Naval Drones 16th

Detecting the Unseen: Exploiting Radar-Sonar Sensor Fusion ...

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16th International Conference on ICT Innovations, ICT Innovations 2024

作者： Longo, Giacomo Cantelli-Forti, Alessandro Russo, Enrico Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Genoa Italy Parma Italy

ISBN: (纸本)9783031861611

Recent advancements in naval technology have introduced agile and stealthy anti-ship drones. Characterized by rapid movements and compact above-water hulls, these drones often exhibit small radar cross sections (RCS), making them difficult to detect using traditional methods. To counter this emerging challenge, this research explores the integration of radar and sonar sensors, harnessing their complementary strengths to counter such threats via enhanced visualization capabilities. We illustrate how combining measurements from both radars and sonars, despite their different operational ranges and angular coverages, can significantly increase the surveillance effectiveness beyond their individual contributions. The proposed approach is expected to enhance the accuracy and reliability of visual detection systems, enabling better detection and tracking capabilities for these elusive naval drones. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drones Radar detection Sensor fusion Sonar

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Extended gate organic field effect transistor for calcium ion sensing towards biomedical applications

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Sensors and Actuators Reports 2025年 9卷

作者： Viola, F․ A. Hatami, D. Mariani, F. Gualandi, I. Terranova, F. Scavetta, E. Bonfiglio, A. Spanu, A. Department of Electrical and Electronic Engineering University of Cagliari via Marengo Cagliari09123 Italy Piazza della Vittoria 15 Pavia27100 Italy Department of Industrial Chemistry "Toso Montanari" University of Bologna Via Piero Gobetti 85 Bologna Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

The measurement of calcium ions (Ca2+) concentration is crucial for several biomedical applications because it plays a key role in various physiological processes, including cellular signalling, muscle contraction, bone metabolism, and the release of neurotransmitters. In this paper, we propose an innovative flexible sensor based on an Extended Gate Organic Field Effect Transistor (ExG-OFET) coupled with a liquid-processable ion-selective membrane. This novel approach enables highly sensitive (exceeding 60 mV dec‑1) and selective ion detection, with a limit of detection in the low μM range, outperforming conventional ion-sensitive electrodes (ISEs) due to the intrinsic electronic amplification of the transistor architecture. Moreover, the simple fabrication process and the use of flexible, cost-effective materials, make this device a versatile, low-cost solution for a wide range of biomedical applications, from in vitro electrophysiology to epidermal electronics. This study highlights the potential of the ExG-OFET platform as a promising alternative to traditional ion detection systems. © 2025 The Author(s)

关键词： Bone

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Toward a universal concept of artificial personality: implementing robotic personality in a Kinova arm

arXiv

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arXiv 2025年

作者： Nardelli, Alice Landolfi, Lorenzo Pasquali, Dario Sgorbissa, Antonio Rea, Francesco Recchiuto, Carmine Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via all’Opera Pia 13 Genoa16150 Italy Via Enrico Melen Genoa16150 Italy Via Enrico Melen Genoa16150 Italy

The fundamental role of personality in shaping interactions is increasingly being exploited in robotics. A carefully designed robotic personality has been shown to improve several key aspects of Human-Robot Interaction (HRI). However, the fragmentation and rigidity of existing approaches reveal even greater challenges when applied to non-humanoid robots. On one hand, the state of the art is very dispersed;on the other hand, Industry 4.0 is moving towards a future where humans and industrial robots are going to coexist. In this context, the proper design of a robotic personality can lead to more successful interactions. This research takes a first step in that direction by integrating a comprehensive cognitive architecture built upon the definition of robotic personality—validated on humanoid robots—into a robotic Kinova Jaco2 arm. The robot personality is defined through the cognitive architecture as a vector in the three-dimensional space encompassing Conscientiousness, Extroversion, and Agreeableness, affecting how actions are executed, the action selection process, and the internal reaction to environmental stimuli. Our main objective is to determine whether users perceive distinct personalities in the robot, regardless of its shape, and to understand the role language plays in shaping these perceptions. To achieve this, we conducted a user study comprising 144 sessions of a collaborative game between a Kinova Jaco2 arm and participants, where the robot’s behavior was influenced by its assigned personality. Furthermore, we compared two conditions: in the first, the robot communicated solely through gestures and action choices, while in the second, it also utilized verbal interaction. © 2025, CC BY.

关键词： Anthropomorphic robots

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