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2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

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

ISBN: (纸本)9798350322972

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. © 2023 IEEE.

关键词： Large dataset

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

A surgical dataset from the da Vinci Research Kit for task a...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

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

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Unsupervised identification of stereotypical premotor firing patterns for the decoding of hand and mouth movements

Unsupervised identification of stereotypical premotor firing...

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Complexity in Engineering (COMPENG)

作者： Elena Hilary Rondoni Matteo Pizzinga Francesca Lanzarini Monica Maranesi Davide Albertini Luca Bonini Eleonora Russo Alberto Mazzoni The Biorobotics Institute Scuola Superiore Sant’Anna Pisa Italy Dept. of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa Italy Department of Medicine and Surgery University of Parma Parma Italy Ernst Strüngmann Institute (ESI) for neuroscience with Max Planck Society

ISBN: (数字)9798350382792

ISBN: (纸本)9798350382808

Cortical decoding of motor intentions is a classic challenge in neural engineering. A candidate solution for tetraplegic patients is to decode motor intention from the activity of the frontal cortex. Here, we develop an algorithm to decode motor intentions from electrophysiological multi-single unit recordings. The algorithm identifies stereotypical spiking patterns for each of the recorded neurons and uses them as reference points to express and decode neuronal activity on different tasks. We tested the algorithm on recordings from the premotor cortex of two macaques performing different types of hand grasps and mouth movements associated with rewards. In the time window of highest performance, the decoder achieved average accuracies of 96%, 82%, and 67% in discriminating 2, 4, and 7 movement classes, respectively. Additionally, movement classes were decoded significantly above chance already in the window of 1000 to 500 ms before the grasp started, demonstrating decoding of motor intentions and the potential applicability of the approach for neuroprosthetic applications.

关键词： Firing Conferences Neurons Mouth Neuroprostheses Neural engineering Motors Decoding Recording Complexity theory

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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

关键词： Large dataset

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Deep Learning Approach for EEG Classification in Lower-Limb Movement Phases: Towards Enhanced Brain-Computer Interface Control

Deep Learning Approach for EEG Classification in Lower-Limb ...

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IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Cristian Felipe Blanco-Diaz Cristian David Guerrero-Mendez Guilherme Rangel Furtado Teodiano Bastos-Filho Rafhael Milanezi de Andrade Dept. of Excellence and AI The BioRobotics Institute Scuola Superiore Sant’Anna Pisa Italy Dept. Electronics and Biomedical Engineering Universidade Estadual de Campinas Campinas SP Brazil Dept. of Electrical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil Posgraduate Program in Electrical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil Graduate Program in Mechanical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

Embedded Brain-Computer Interfaces (BCI) have emerged as an alternative for rehabilitation/assistance of people with neuromotor impairments. Data-driven methods are usually implemented to design controllers to (de)activate these systems using electroencephalography (EEG), which may limit usability and degrees of freedom. Thus, the development of more adaptive classifiers remains a challenge. This study focuses on the implementation of a Deep Learning (DL) method to classify EEG signals during different phases of gait and pedaling. To address this, public and in-house databases were used, which contain information on EEG and kinematic data during walking on a treadmill and pedaling on a minibike. Kinematic labels of the phases were extracted using k-means and classified by a Convolutional Neural Network (CNN), which demonstrated accuracy above 60%. Furthermore, a negative correlation was found between velocities and classifier performance metrics, suggesting that faster movements can hinder EEG decoding. Our proposal is a starting point to identify current states of lower-limb movement, which can be used for designing state machine-based controllers of robotic BCIs with rehabilitation or assistance purposes, such as motorized mini exercise bikes or leg exoskeletons.

关键词： Legged locomotion Deep learning Mechatronics Kinematics Electroencephalography Brain-computer interfaces Convolutional neural networks Proposals Usability Robots

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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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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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Modeling and analysis of bus contention for hardware accelerators in FPGA SoCs 32

Modeling and analysis of bus contention for hardware acceler...

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32nd Euromicro Conference on Real-Time Systems, ECRTS 2020

作者： Restuccia, Francesco Pagani, Marco Biondi, Alessandro Marinoni, Mauro Buttazzo, Giorgio TeCIP Insitute Dept. of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa Italy TeCIP Institute Scuola Superiore Sant’Anna Pisa Italy Université de Lille CNRS Centrale Lille UMR 9189 CRIStAL Lille France

ISBN: (纸本)9783959771528

FPGA System-on-Chips (SoCs) are heterogeneous platforms that combine general-purpose processors with a field-programmable gate array (FPGA) fabric. The FPGA fabric is composed of a programmable logic in which hardware accelerators can be deployed to accelerate the execution of specific functionality. The main source of unpredictability when bounding the execution times of hardware accelerators pertains the access to the shared memories via the on-chip bus. This work is focused on bounding the worst-case bus contention experienced by the hardware accelerators deployed in the FPGA fabric. To this end, this work considers the AMBA AXI bus, which is the de-facto standard communication interface used in most the commercial off-the-shelf (COTS) FPGA SoCs, and presents an analysis technique to bound the response times of hardware accelerators implemented on such platforms. A fine-grained modeling of the AXI bus and AXI interconnects is first provided. Then, contention delays are studied under hierarchical bus infrastructures with arbitrary dept.s. Experimental results are finally presented to validate the proposed model with execution traces on two modern FPGA-based SoC produced by Xilinx (Zynq-7000 and Zynq-Ultrascale+ families) and to assess the performance of the proposed analysis. © Francesco Restuccia, Marco Pagani, Alessandro Biondi, Mauro Marinoni, and Giorgio Buttazzo.

关键词： System-on-chip

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Tracking Emotions: Intrinsic Motivation Grounded on Multi-Level Prediction Error Dynamics

arXiv

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arXiv 2020年

作者： Schillaci, Guido Ciria, Alejandra Lara, Bruno BioRobotics Institute Dept. of Excellence in Robotics & AI Scuola Superiore SantAnna Pisa Italy Facultad de Psicología Univ. Nacional Autónoma de México México City Mexico Centro de Investigación en Ciencias Univ. Autónoma del Estado de Morelos Cuernavaca Mexico

How do cognitive agents decide which is the relevant information to learn and how goals are selected to gain this knowledge? Cognitive agents need to be motivated to perform any action. We discuss that emotions arise when differences between expected and actual rates of progress toward a goal are experienced. Therefore, the tracking of prediction error dynamics has a tight relationship with emotions. Here, we suggest that the tracking of prediction error dynamics allows an artificial agent to be intrinsically motivated to seek new experiences but constrained to those that generate reducible prediction error. We present an intrinsic motivation architecture that generates behaviors towards self-generated and dynamic goals and that regulates goal selection and the balance between exploitation and exploration through multi-level monitoring of prediction error dynamics. This new architecture modulates exploration noise and leverages computational resources according to the dynamics of the overall performance of the learning system. Additionally, it establishes a possible solution to the temporal dynamics of goal selection. The results of the experiments presented here suggest that this architecture outperforms intrinsic motivation approaches where exploratory noise and goals are fixed and a greedy strategy is applied. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Tactile sensing with gesture-controlled collaborative robot

Tactile sensing with gesture-controlled collaborative robot

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Workshop on Metrology for Industry 4.0 and IoT

作者： Francesca Sorgini Giuseppe airò Farulla Nikola Lukic Ivan Danilov Loris Roveda Milos Milivojevic Terrin Babu Pulikottil Maria Chiara Carrozza Paolo Prinetto Tullio Tolio Calogero Maria Oddo Petar B. Petrovic Bozica Bojovic Dept. of Excellence in Robotics & A.I. The BioRobotics Institute Scuola Superiore Sant' Anna Pisa Italy Politecnico di Torino Turin Italy Production Engineering Department Faculty of Mechanical Engineering University of Belgrade Belgrade Serbia Istituto Dalle Molle di studi sull'intelligenza artificiale IDSIA-SUPSI Manno Switzerland Faculty of Mechanical Engineering University of Belgrade Belgrade Serbia Ist. di Sistemi e Tecn Industriali per il Manifatturiero Avanzato STIIMA CNR Milano Italy The BioRobotics Institute Scuola Superiore Sant' Anna Pisa Italy Politecnico di Torino CINI AsTech National Lab Torino Italy Politecnico di Milano Milano Italy

ISBN: (数字)9781728148922

ISBN: (纸本)9781728148939

Sensors and human machine interfaces for collaborative robotics will allow smooth interaction in contexts ranging from industry to tele-medicine and rescue. This paper introduces a bidirectional communication system to achieve multisensory telepresence during the gestural control of an industrial robotic arm. Force and motion from the robot are converted in neuromorphic haptic stimuli delivered on the user's hand through a vibro-tactile glove. Untrained personnel participated in an experimental task benchmarking a pick-and-place operation. The robot end-effector was used to sequentially press six buttons, illuminated according to a random sequence, and comparing the tasks executed without and with tactile feedback. The results demonstrated the reliability of the hand tracking strategy developed for controlling the robotic arm, and the effectiveness of a neuronal spiking model for encoding hand displacement and exerted forces in order to promote a fluid embodiment of the haptic interface and control strategy. The main contribution of this paper is in presenting a robotic arm under gesture-based remote control with multisensory telepresence, demonstrating for the first time that a spiking haptic interface can be used to effectively deliver on the skin surface a sequence of stimuli emulating the neural code of the mechanoreceptors beneath.

关键词： collaborative robotics telepresence neuromorphic vibrotactile feedback human-robot interaction hand tracking gesture-based teleoperation REMOTE CONTROL Human machine interaction Touch Robot robotic arm Touch Perceptions Haptic Device Component Mechanoreceptors

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