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SSRN 2023年

作者： Trucco, Diego Gibney, Rory Vannozzi, Lorenzo Lisignoli, Gina Kelly, Daniel J. Ricotti, Leonardo The BioRobotics Institute Scuola Superiore Sant’Anna Piazza Martiri della Libertà 33 Pisa56127 Italy Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Piazza Martiri della Libertà 33 Pisa56127 Italy IRCCS Istituto Ortopedico Rizzoli SC Laboratorio di Immunoreumatologia e Rigenerazione Tissutale Via di Barbiano 1/10 Bologna40136 Italy Trinity Centre for Biomedical Engineering Trinity Biomedical Sciences Institute Trinity College Dublin Ireland Department of Mechanical Manufacturing and Biomedical Engineering School of Engineering Trinity College Dublin Ireland

Due to their high-water content, biocompatibility, and ability to mimic the native tissue's extracellular matrix, hydrogels are commonly used for acellular cartilage repair applications. Hydrogels can treat irregular-shaped defects when minimally invasively injected in situ, but with relatively poor mechanical properties, an issue that limits their application in load-bearing locations. In this study, we developed fibrous structures obtained through melt electro-writing (MEW) to mechanically reinforce injectable hydrogels. We assessed the injection forces required for successfully extruding the hydrogels reinforced with MEW-printed structures, while varying their geometry (square/hexagonal pores) and pore sizes (0.6, 0.8, 1.0 mm) through needles of different diameters. Injection forces decreased in correspondence to larger pores and larger needle diameters. Hexagonal structures implied higher forces than square ones. Almost all structures could be extruded through 14G needles with forces below the clinically acceptable 10 N limit, defined by the EU-ISO-7886-1:2018 standard. Different hydrogel types were extruded with embedded square-shaped MEW structures having 1.0 mm pores, assessing their injectability and their mechanical properties. The MEW reinforcement did not affect the injection forces, but improved the mechanical properties of the injected hydrogels. These outcomes represent an advancement toward reinforced injectable hydrogels compliant with arthroscopic and other minimally invasive procedures. © 2023, The Authors. All rights reserved.

关键词： Hydrogels

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How Euglena gracilis swims: Flow field reconstruction and analysis

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Physical Review E 2021年第2期103卷 023102-023102页

作者： Nicola Giuliani Massimiliano Rossi Giovanni Noselli Antonio DeSimone SISSA–International School for Advanced Studies Via Bonomea 265 34136 Trieste Italy DTU–Department of Physics Technical University of Denmark DTU Physics Building 309 DK-2800 Kongens Lyngby Denmark The BioRobotics Institute and Dept. of Excellence in Robotics and AI Scuola Universitaria Superiore Pisa Piazza Martiri della Libertà 56127 Pisa Italy

Euglena gracilis is a unicellular organism that swims by beating a single anterior flagellum. We study the nonplanar waveforms spanned by the flagellum during a swimming stroke and the three-dimensional flows that they generate in the surrounding fluid. Starting from a small set of time-indexed images obtained by optical microscopy on a swimming Euglena cell, we construct a numerical interpolation of the stroke. We define an optimal interpolation (which we call synthetic stroke) by minimizing the discrepancy between experimentally measured velocities (of the swimmer) and those computed by solving numerically the equations of motion of the swimmer driven by the trial interpolated stroke. The good match we obtain between experimentally measured and numerically computed trajectories provides a first validation of our synthetic stroke. We further validate the procedure by studying the flow velocities induced in the surrounding fluid. We compare the experimentally measured flow fields with the corresponding quantities computed by solving numerically the Stokes equations for the fluid flow, in which the forcing is provided by the synthetic stroke, and find good matching. Finally, we use the synthetic stroke to derive a coarse-grained model of the flow field resolved in terms of a few dominant singularities. The far field is well approximated by a time-varying Stresslet, and we show that the average behavior of Euglena during one stroke is that of an off-axis puller. The reconstruction of the flow field closer to the swimmer body requires a more complex system of singularities. A system of two Stokeslets and one Rotlet, that can be loosely associated with the force exerted by the flagellum, the drag of the body, and a torque to guarantee rotational equilibrium, provides a good approximation.

关键词： Cell locomotion Swimming Coarse graining Finite-element method Lagrangian particle tracking

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A surgical dataset from the da Vinci Research Kit for task automation and recognition

arXiv

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arXiv 2021年

作者： Mariani, Andrea Rivas-Blanco, Irene Pérez-Del-Pulgar, Carlos J. Tortora, Giuseppe Reina, Antonio J. Dept. Systems Engineering and Automation University of Malaga Malaga Spain The BioRobotics Institute Dept. Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa Italy Smart Medical Theatre Lab ABzero srl Pisa Italy

The use of large datasets is essential in surgical robotics to advance in the field of recognition and automation of surgical tasks. Furthermore, public datasets allow the comparison of different algorithms and methods to evaluate their performance. The objective of this work is to provide a complete dataset of three common training surgical tasks performed with the da Vinci Research Kit (dVRK). The dataset contains a total of 206 trials performed by twelve different subjects. For each trial, the dataset includes 154 kinematic variables from the dVRK (both master and slave sides) together with the associated video recorded with a camera. All the data has been carefully timestamped and provided in a readable csv format. A MATLAB interface integrated with ROS for using and replicating the data is also provided. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

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Identifying synthetic voices qualities for conversational agents

arXiv

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arXiv 2022年

作者： Cuciniello, Marialucia Amorese, T. Cordasco, G. Marrone, S. Marulli, F. Cavallo, F. Gordeeva, O. Callejas Carrión, Z. Esposito, A. Dept of Psychology Università degli Studi della Campania "Luigi Vanvitelli" IIASS Caserta and Vietri sul Mare Italy Università degli Studi della Campania "Luigi Vanvitelli" Italy Dept of Industrial Eng Florence University BioRobotics Institute SS Sant'Anna Dep of Excellence in Robotics & AI Italy Acapela Group Belgium Universidad de Granada Spain

The present study aims to explore user' acceptance and perceptions toward different quality levels of synthetical voices. To achieve this, four voices have been exploited considering two main factors: the quality of the voices (low vs high) and their gender (male and female). 186 volunteers were recruited and subsequently allocated into four groups of different ages respectively, adolescents, young adults, middle-aged and seniors. After having randomly listened to each voice, participants were asked to fill the Virtual Agent Voice Acceptance Questionnaire (VAVAQ). Outcomes show that the two higher quality voices of Antonio and Giulia were more appreciated than the low-quality voices of Edoardo and Clara by the whole sample in terms of pragmatic, hedonic and attractiveness qualities attributed to the voices. Concerning preferences towards differently aged voices, it clearly appeared that they varied according to participants age' ranges examined. Furthermore, in terms of suitability to perform different tasks, participants considered Antonio and Giulia equally adapt for healthcare and front office jobs. Antonio was also judged to be significantly more qualified to accomplish protection and security tasks, while Edoardo was classified as the absolute least skilled in conducting household chores. © 2022, CC BY.

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Is Deep Learning ready to satisfy Industry needs?

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Procedia Manufacturing 2020年 51卷 1192-1199页

作者： Paolo Tripicchio Salvatore D’Avella Department of Excellence in Robotics & AI TeCIP Institute Scuola Superiore Sant’Anna Pisa Italy

The impact that Artificial Intelligence is having in modern society is undeniable. Many companies are now using ai to improve the throughput and automate their processes. But the challenge is that Artificial Intelligence is both a source of enthusiasm and skepticism for industries. The manuscript points out the main causes of skepticism giving at the same time some possible technical solutions to exploit at the best the potentialities of ai even in those conditions in which the data are imbalanced and the object classes are not well separated. This work also emphasizes the delicate relationship between artificial intelligence, researchers, and industries, and tries to give an overview of a possible trade-off between the two parties. The document ends up proposing an ‘interpretable learning’ approach that can be exploited as a common language between the two players. The desirable practice would be to make ai explainable, provable, and easily understandable by the companies.

关键词： Deep Learning Quality Inspection Data Imbalance Transfer Learning

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In Vivo Efficacy of an Injectable Piezoelectric Nanocomposite Hydrogel and Low-Intensity Pulsed Ultrasound in Two Preclinical Models of Osteoarthritis

SSRN

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SSRN 2025年

作者： Tschon, Matilde Codispoti, Giorgia Cabras, Paolo Cafarelli, Andrea Trucco, Diego Vannozzi, Lorenzo Manferdini, Cristina Carniato, Melania Cassiolas, Giorgio Martini, Lucia Fini, Milena D’Atri, Giovanni Jost, Carsten Fedutik, Yirij Nessim, Gilbert Daniel Dumont, Erik Lisignoli, Gina Ricotti, Leonardo IRCCS Istituto Ortopedico Rizzoli Struttura Complessa Scienze e Tecnologie Chirurgiche Bologna40136 Italy Image Guided Therapy Pessac France Université de Strasbourg CNRS INSERM ICube UMR7357 Strasbourg France The BioRobotics Institute Scuola Superiore Sant’Anna Pisa56127 Italy Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa56127 Italy IRCCS Istituto Ortopedico Rizzoli Laboratorio di Immunoreumatologia e Rigenerazione Tissutale Bologna40136 Italy IRCCS Istituto Ortopedico Rizzoli Scientific Director Bologna40136 Italy PlasmaChem GmbH Schwarzschildstraße 10 Berlin12489 Germany Department of Chemistry Institute of Nanotechnology Bar-Ilan University Ramat Gan52900 Israel

Smart hydrogels embedding mesenchymal stromal cells are receiving increasing attention as a possible solution for preventing articular cartilage degeneration in knee osteoarthritis. In this work, we demonstrated that an injectable piezoelectric hydrogel embedding autologous adipose tissue-derived mesenchymal stromal cells, stimulated through low-intensity pulsed ultrasound, was effective in reducing both macroscopic and microscopic features of knee osteoarthritis progression in two preclinical models. The former was a medium-sized rabbit model, in which sex-based differences in the therapy effectiveness were evaluated;the latter was a large-sized sheep model. Computational models guaranteed a reliable and precise delivery of a specific ultrasound dose at the target, by modeling wave propagation through tissues and considering the anatomy of the two experimental animal models. © 2025, The Authors. All rights reserved.

关键词： Cartilage

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Learning-Based Keypoint Registration for Fetoscopic Mosaicking

arXiv

引用

arXiv 2022年

作者： Casella, Alessandro Bano, Sophia Vasconcelos, Francisco David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women’s Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto "Giannina Gaslini" Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Italy

In Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the two fetuses. In the current practice, TTTS is treated surgically by closing abnormal anastomoses using laser ablation. This surgery is minimally invasive and relies on fetoscopy. Limited field of view makes anastomosis identification a challenging task for the surgeon. To tackle this challenge, we propose a learning-based framework for in-vivo fetoscopy frame registration for field-of-view expansion. The novelties of this framework relies on a learning-based keypoint proposal network and an encoding strategy to filter (i) irrelevant keypoints based on fetoscopic image segmentation and (ii) inconsistent homographies. We validate of our framework on a dataset of 6 intraoperative sequences from 6 TTTS surgeries from 6 different women against the most recent state of the art algorithm, which relies on the segmentation of placenta vessels. The proposed framework achieves higher performance compared to the state of the art, paving the way for robust mosaicking to provide surgeons with context awareness during TTTS surgery. Copyright © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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Intracortical Microstimulation Feedback Improves Grasp Force Accuracy in a Human Using a Brain-Computer Interface*

Intracortical Microstimulation Feedback Improves Grasp Force...

引用

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

作者： Kristin M. Quick Jeffrey M. Weiss Francesco Clemente Robert A. Gaunt Jennifer L. Collinger Department of Physical Medicine and Rehabilitation University of Pittsburgh Pittsburgh PA USA The BioRobotics Institute and the Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa Italy

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

After a spinal cord injury, a person may grasp objects using a brain-computer interface (BCI) to control a robot arm. However, most BCIs do not restore somatosensory percepts that would enable someone to sense grasp force. Intracortical microstimulation (ICMS) in the somatosensory cortex can evoke tactile sensations and may therefore offer a viable solution to provide grasp force feedback. We investigated whether a bidirectional BCI could improve grasp force control over a BCI using only visual feedback. When evaluating the error of the applied force during a force matching task, we found that ICMS feedback improved overall applied grasp force accuracy.

关键词： Force Grippers Visualization Decoding Task analysis Thumb Robots

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Prediction error-driven memory consolidation for continual learning. On the case of adaptive greenhouse models

arXiv

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arXiv 2020年

作者： Schillaci, Guido Miranda, Luis Schmidt, Uwe BioRobotics Institute Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa Italy Department of Biosystems Engineering Humboldt-Universität zu Berlin Germany

This work presents an adaptive architecture that performs online learning and faces catastrophic forgetting issues by means of episodic memories and prediction-error driven memory consolidation. In line with evidences from the cognitive science and neuroscience, memories are retained depending on their congruency with the prior knowledge stored in the system. Copyright © 2020, The Authors. All rights reserved.

关键词： Greenhouses

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Together We Stand-Analyzing Schooling Behavior in Naive Newborn Guppies through Biorobotic Predators

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Journal of Bionic Engineering 2020年第1期17卷 174-184页

作者： Donato Romano Hadeel Elayan Giovanni Benelli Cesare Stefanini The BioRobotics Institute Sant'Anna School of Advanced StudiesPontederaPisa 56025Italy Healthcare Engineering Innovation Center(HEIC) Khalifa UniversityAbu DhabiPO Box 127788United Arab Emirates Department of Excellence in Robotics&A.I. Sant'Anna School of Advanced StudiesPisa 56127Italy Department of Electrical and Computer Engineering University of TorontoON M5SCanada Department of Agriculture Food and EnvironmentUniversity of PisaPisa 56124Italy

A major advantage of animal aggregations concerns cooperative antipredator *** behavior emerges earlier in many fish species,especially in those cannibalizing their *** is fundamental for developing schooling ***,the cognitive ability of naive newborn fish to aggregate remains ***,Poecilia reticulata,was selected as model organism to investigate how combinations of biomimetic robotic agents and adult conspecific olfactory cues affect collective responses in *** role of white and brown backgrounds in evoking aggregations was also *** cues were sufficient for triggering aggregations in *** newborns,although robotic agents had a higher influence on the group *** combination of robotic agents and olfactory cues increased schooling behavior ***,schooling was longer in the escape compartment when robotic agents were presented,except for the combination of the male-mimicking robotic fish plus adult guppy olfactory cues,with longer schooling behavior in the exploring *** of the tested cues,newborn fish aggregated preferentially on the brown areas of the ***,this research provides novel insights on the early collective cognitive ability of newborn fish,paving the way to the use of biomimetic robots in behavioral ecology experiments,as substitutes for real predators.

关键词： animal-robot interaction technology bio-hybrid intelligent systems antipredator strategy group behavior Poecilia reticulata

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