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14th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2024

作者： KhalilianMotamed Bonab, Ali Camardella, Cristian Frisoli, Antonio Leonardis, Daniele Institute of Mechanical Intelligence and the Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa Italy

ISBN: (纸本)9783031700606

Wearable haptic devices have been proposed to convey guidance and feedback information in a variety of applications, ranging from navigation to virtual interaction and prosthetics. A design approach for armband devices using an actuated fabric belt and gearmotors actuators has been proposed and widely experimented in the field. In this work we experiment whether the use of direct-drive actuation in place of servomotors proves effective in rendering lower intensity, but cleaner linear feedback. Interestingly, the method allows for modulation of vibrotactile feedback as well, with the same actuation design. Here we present the design of a wearable haptic device for the upper limb, implementing a 2 degrees of freedom direct drive transmission and a soft belt interface. Experiments evaluate the capability of the haptic feedback to guide participants in two different motor tasks, exploiting the two haptic modalities the device can render: clenching and lateral stretch. Moreover, the addition of modulated vibrotactile feedback to the conventional linear motor activation is explored as a viable modality to enhance the perception and effectiveness of the perceived stimuli. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linear motors

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The Effect of User Learning for Online EEG Decoding of Upper-Limb Movement Intention

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 633-641页

作者： Ceradini, Matteo Tortora, Stefano Micera, Silvestro Tonin, Luca Scuola Superiore Sant’Anna BioRobotics Institute Department of Excellence in Robotics and AI Pisa56127 Italy University of Padova Department of Information Engineering Padua Neuroscience Center Padua35122 Italy Neuro-X Institute Ecole Poly-technique Federale de Lausanne Translational NeuroEngineering Laboratory Lausanne1015 Switzerland Università Vita-Salute San Raffaele Modular Implantable Neuroprostheses Laboratory Milan20132 Italy

Electroencephalography (EEG) based brain-computer interfaces (BCIs) offer a promising way for individuals with motor impairments to control prosthetic or rehabilitation devices. Accurately decoding movement intention (MI) is crucial for translating subjects’ motor execution plans into action. Common challenges in EEG-based BCIs include performance discrepancies, often requiring frequent recalibration of decoding algorithms. The objective of this study was enhancing BCI decoding performance of upper-limb MI identification by exploiting both machine and subjects’ learning and maintaining stable decoding algorithms. Significant performance improvements were observed across most subjects from the first to the last session of the experiment. Some subjects also demonstrated stable performance without requiring any model recalibration between sessions. All subjects achieved high efficacy in online decoding of movement intention, as reflected in improvement of the F1 score from 0.58±0.26 in the first session, to 0.84±0.13 in the final session. We emphasize the critical importance of allowing users sufficient time to improve their performance in BCIs for upper-limb MI decoding. Unlike existing studies, we specifically evaluate the effect of stable decoding strategies in online and longitudinal BCI sessions, which are key to achieving more reliable and effective BCIs. © 2018 IEEE.

关键词： Brain computer interface

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Neuroethology of natural actions in freely moving monkeys

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Science (New York, N.Y.) 2025年第6730期387卷 214-220页

作者： Lanzarini, Francesca Maranesi, Monica Rondoni, Elena Hilary Albertini, Davide Ferretti, Elena Lanzilotto, Marco Micera, Silvestro Mazzoni, Alberto Bonini, Luca Department of Medicine and Surgery University of Parma Parma Italy Biorobotics Institute Department of Excellence in AI and Robotics Scuola Superiore Sant'Anna Pisa Italy Interdisciplinary Health Center and Department of Excellence in AI and Robotics Scuola Superiore Sant'Anna Pisa Italy Modular Implantable Neuroprostheses Laboratory Università Vita-Salute San Raffaele & Scuola Superiore Sant'Anna Milan Italy Bertarelli Foundation Chair in Translational Neuroengineering School of Engineering Ecole Polytechnique Federale de Lausanne Genève Switzerland

The current understanding of primate natural action organization derives from laboratory experiments in restrained contexts (RCs) under the assumption that this knowledge generalizes to freely moving contexts (FMCs). In this work, we developed a neurobehavioral platform to enable wireless recording of the same premotor neurons in both RCs and FMCs. Neurons often encoded the same hand and mouth actions differently in RCs and FMCs. Furthermore, in FMCs, we identified cells that selectively encoded actions untestable during RCs and others that displayed mixed selectivity for multiple actions, which is compatible with an organization based on cortical motor synergies at different levels of complexity. Cross-context decoding demonstrated that neural activity in FMCs is richer and more generalizable than in RCs, which suggests that neuroethological approaches are better suited to unveil the neural bases of behavior.

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Physical Disturbance Rejection Methods in Myokinetic Control of Prosthetic Limbs

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Paggetti, F. Gherardini, M. Ianniciello, V. Cirelli, S. Cipriani, C. Scuola Superiore Sant'Anna Italy Scuola Superiore Sant'Anna BioRobotics Institute Italy Scuola Superiore Sant'Anna Department of Excellence in Robotics and AI Italy

Objective: The myokinetic control interface for limb protheses involves implanting small permanent magnets in the residual muscles of the stump, retrieving their displacement induced by muscle contraction using external magnetic sensors, and mapping such displacements to motor commands. We have previously shown the feasibility of tracking several magnets implanted in an anatomically relevant workspace. However, to clinically translate the interface, strategies to address different kinds of external disturbances compromising its functioning are mandatory. Among these, physical disturbances, viz. relative displacement between the sensors and the magnets not due to voluntary contraction, could significantly hinder the interface usability. Methods: Here we propose three rejection methods sought to mitigate this problem: one aims to realign the sensors and the magnets when a disturbance is detected, through numerical approximation methods;the others exploit differential measurements. We applied these concepts to upper limb prostheses, by mimicking the presence of physical disturbances in simulations and through a physical setup, reproducing four target muscles of a human forearm to be potentially implanted. Finally, we evaluated the rejection ability of one of those methods during the first-in-human implementation of the myokinetic interface. Results: All methods proved capable of rejecting the disturbances, showing median localization errors below 10% the displacement undergone during contraction. Conclusion: Results suggest that the optimal rejection method is application-specific, and provide hints for assessing different factors influencing the best choice. Significance: Other than being crucial for the myokinetic interface development, these outcomes also provide interesting insights for many biomedical applications exploiting remote magnetic tracking. © 1964-2012 IEEE.

关键词： Disturbance rejection

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Adaptive Drift Compensation for Soft Sensorized Finger Using Continual Learning

arXiv

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arXiv 2025年

作者： Kushawaha, Nilay Pathan, Radan Pagliarani, Niccolò Cianchetti, Matteo Falotico, Egidio The BioRobotics Institute Scuola Superiore Sant’Anna Pontedera Italy Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa Italy

Strain sensors are gaining popularity in soft robotics for acquiring tactile data due to their flexibility and ease of integration. Tactile sensing plays a critical role in soft grippers, enabling them to safely interact with unstructured environments and precisely detect object properties. However, a significant challenge with these systems is their high non-linearity, time-varying behavior, and long-term signal drift. In this paper, we introduce a continual learning (CL) approach to model a soft finger equipped with piezoelectric-based strain sensors for proprioception. To tackle the aforementioned challenges, we propose an adaptive CL algorithm that integrates a Long Short-Term Memory (LSTM) network with a memory buffer for rehearsal and includes a regularization term to keep the model’s decision boundary close to the base signal while adapting to time-varying drift. We conduct nine different experiments, resetting the entire setup each time to demonstrate signal drift. We also benchmark our algorithm against two other methods and conduct an ablation study to assess the impact of different components on the overall performance. © 2025, CC BY.

关键词： Grippers

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T-REX: A Framework to Build Trustworthy Recommenders of Evidence Explanation

T-REX: A Framework to Build Trustworthy Recommenders of Evid...

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2024 Discovery Science Late Breaking Contributions, DS-LB 2024

作者： Fedele, Andrea de’ Cavalieri, Mattia Franchi Landi, Cristiano Punzi, Clara Tramacere, Stefano University of Pisa Pisa Italy KDD Lab ISTI-CNR Pisa Italy The BioRobotics Institute Scuola Superiore Sant’Anna Pisa Italy Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa Italy Scuola Normale Superiore Pisa Italy LIDER-Lab Scuola Superiore Sant’Anna Pisa Italy

The initial enthusiasm for eXplainable Artificial Intelligence (Xai) has been tempered by concerns about the effectiveness and reliability of its explanations. Studies show that some explanations are no more reliable than random ones. Tim Miller suggests a paradigm shift in Xai to address issues of cognitive biases, such as automation bias, which can affect decision-making processes. He advocates for hypothesis-driven support systems to align ai explanations with human cognitive processes. Addressing these issues, we propose the Trustworthy Recommenders of Evidence eXplanations (T-REX) framework. This approach aims to enhance Xai by moving from statistical explanations to those based on trustworthy scientific evidence, enabling ai systems to tackle complex tasks more effectively. © 2022 Copyright for this paper by its authors.

关键词： Explainable ai Human-Machine Interaction Trustworthy ai

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A novel approach for remote ultrasonography of knee osteoarthritis

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Biocybernetics and Biomedical Engineering 2025年第3期45卷 319-328页

作者： Sorriento, Angela Guachi-Guachi, Lorena Marola, Giorgia Spinnato, Paolo Rabusseau, Fabien Cabras, Paolo Dumont, Erik Ricotti, Leonardo Cafarelli, Andrea The BioRobotics Institute Scuola Superiore Sant'Anna Pisa 56127 Italy Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa 56127 Italy IRCCS Istituto Ortopedico Rizzoli Diagnostic and Interventional Radiology Bologna Italy IGT Image Guided Therapy Pessac France Université de Strasbourg CNRS INSERM UMR7357 ICube Strasbourg France

In this study, we propose innovative technologies and a novel standardized procedure, enabling at-home, periodic and reliable ultrasound monitoring of knee osteoartrithis. Anatomical and ultrasound data were collected on a population of 27 volunteers with different anthropometric features, leading to the development of two technological solutions: i) three probe positioning systems (wearable brace, adhesive film, temporary tattoo), provided with predefined openings to ensure accurate and repeatable placement of an ultrasound probe;ii) an image selection algorithm for automatic identification of the correct ultrasound images acquired by non-expert users. The effectiveness and usability of these solutions were tested on 20 volunteers and 20 caregivers, with no prior experience in ultrasound imaging. In the first step, an expert radiologist acquired reference ultrasound images. In the second step, all volunteers exploited the positioning systems to place the probe and acquire informative images (automatically extracted by the algorithm from a video). The average usability percentage score was highest for the wearable brace (98.7%), followed by the tattoo (94.8%), and film (89.6%). Overall, the three positioning systems proved to be effective in guiding ultrasound acquisitions for inexperienced subjects. The highest average rate of informative images successffully acquired among all volunteers was achieved by using the brace (70%), while the highest average score among the caregivers was obtained using the tattoo (76%). These findings highlight the possibility to perform reliable remote ultrasound acquisitions for non-expert users and opens the way to novel tele-ultrasound procedures, enabling a precise, asynchronous, and repeatable monitoring of knee osteoarthritis. © 2025

关键词： Cartilage monitoring Convolutional neural network Tele-echography Tele-ultrasonography Ultrasound imaging

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Tactile Object Recognition With Recurrent Neural Networks Through a Perceptive Soft Gripper

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IEEE robotics and Automation Letters 2025年第7期10卷 7023-7030页

作者： Enrico Donato David Pelliccia Matin Hosseinzadeh Mahmood Amiri Egidio Falotico BRAin-Inspired Robotics (BRAIR) Lab The BioRobotics Institute Pontedera (PI) Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Department of Computer Science University of Pisa Pisa Italy Medical Technology Research Center Institute of Health Technology Kermanshah University of Medical Sciences Kermanshah Iran

Soft robot perception integrates information from distributed, multi-modal sensors, broadening their application to active interaction. Our work introduces recurrent learning models for tactile-based object recognition, demonstrating comparable performance in virtual and real-world scenarios. The work focuses on soft grippers, which facilitate adaptation to objects of varying shapes and sizes thanks to passive finger compliance. Our model successfully identifies over sixteen heterogeneous objects. Findings underscore the significance of sensory multi-modality over single. We highlight how spatial distribution and sensory signal dynamics influence overall estimation accuracy, and what the minimal grasp set is to achieve certain recognition.

关键词： Robot sensing systems Object recognition Sensors Grippers Shape Long short term memory Classification algorithms Robots Training Fingers

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Design of a Soft Robotic Artificial Cardiac Wall

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Artificial Organs 2025年第0期 2025 Mar 12页

作者： Zrinscak, Debora De Chirico, Claudia M. Lorenzon, Lucrezia Coluccia, Fabiola De Luca, Mauro Maselli, Martina Kluin, Jolanda Overvelde, Johannes T. B. Cianchetti, Matteo The BioRobotics Insitute and Department of Excellence of Robotics & AI Scuola Superiore Sant'Anna Pontedera Italy Department of Cardiothoracic Surgery Thorax Center Erasmus MC Rotterdam GD Rotterdam Netherlands AMOLF Amsterdam Netherlands

Background: In cardiovascular engineering, the recent introduction of soft robotic technologies sheds new light on the future of implantable cardiac devices, enabling the replication of complex bioinspired architectures and motions. To support human heart function, assistive devices and total artificial hearts have been developed. However, the system's functionality, hemocompatibility, and overall implantability are still open challenges. Methods: Here, the design of a soft robotic artificial cardiac wall is presented: the action of a bioinspired myocardium of pneumatic McKibben actuators in a double helix is coupled with an engineered passive and deformable endocardial layer made of silicone. The correlation between the helix angle of the actuators and the ejection fraction of the artificial cardiac wall was preliminarily studied with a simplified analytical model. A FEM model was introduced to represent the complex deformation of the endocardial layer during the actuation of the cardiac wall. Results: Experimental tests report an ejection fraction of 68%, i.e., 77.2 ± 0.4 mL against 90 mmHg, satisfying the minimum physiological requirements and, therefore, proving the concept's functionality. Conclusions: The conceived device paves the way for a new generation of innovative approaches where engineered bioinspiration might be the key to future artificial cardiac pumps that could support or even substitute the human failing heart. © 2025 The Author(s). Artificial Organs published by International Center for Artificial Organ and Transplantation (ICAOT) and Wiley Periodicals LLC.

关键词： Silicones

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Sensorimotor Control Strategies for Tactile robotics

arXiv

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arXiv 2025年

作者： Donato, Enrico Preti, Matteo Lo Beccai, Lucia Falotico, Egidio The BioRobotics Institute Sant’Anna School of Advanced Studies Pontedera56025 Italy Department of Excellence in Robotics and AI Sant’Anna School of Advanced Studies Pisa56125 Italy Advanced Robotics Centre National University of Singapore Singapore117575 Singapore Italian Institute of Technology Genova16163 Italy

How are robots becoming smarter at interacting with their surroundings? Recent advances have reshaped how robots use tactile sensing to perceive and engage with the world. Tactile sensing is a game-changer, allowing robots to embed sensorimotor control strategies to interact with complex environments and skillfully handle heterogeneous objects. Such control frameworks plan contact-driven motions while staying responsive to sudden changes. We review the latest methods for building perception and control systems in tactile robotics while offering practical guidelines for their design and implementation. We also address key challenges to shape the future of intelligent robots. © 2025, CC BY-NC-SA.

关键词： Intelligent robots

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