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Nature Reviews Electrical Engineering 2024年第5期1卷 282-283页

作者： Christian Cipriani The BioRobotics Institute Scuola Superiore Sant’Anna Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa Italy

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EEG-Based Multi-Class Classification for Recognizing Pedaling Velocities: A Promising Approach for Brain-Computer Interface-Enhanced Lower-Limb Robotic Rehabilitation

EEG-Based Multi-Class Classification for Recognizing Pedalin...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： A.X. Gonzalez-Cely C.F. Blanco-Diaz D. Delisle-Rodriguez Teodiano Bastos-Filho Faculty of Electrical Engineering Federal University of Espirito Santo Vitoria-ES Brazil Department of Excellence and AI The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy Postgraduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neuroscience Santos Dumont Institute 1560- Zona Rural Macaiba-RN Brazil

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Lower-limb robotic rehabilitation devices have shown promising results in restoring functional activities of individuals with motor impairments. Motorized Mini-Exercise Bikes (MMEBs) are robotic devices that provide functional and repetitive flexion/extension exercises through Passive Pedaling (PP), improving muscular activity, coordination, and functional independence. These technologies activated by Brain-Computer Interfaces (BCIs) can be useful during therapeutic interventions for individuals who cannot voluntarily perform a movement. This study proposes a multi-class classification strategy for recognizing different pedaling velocities using electroencephalography signals. To provide a meaningful task, we conducted a protocol with ten able-bodied subjects who received PP from an MMEB configured at 30, 45, and 60 rpm. We provide a practical demonstration by implementing a feature extraction stage based on Common Spatial Patterns, Power Spectral Density, and a classification system based on four Machine Learning techniques: Linear Discriminant Analysis, Support Vector Machine, k-nearest Neighbors, and Decision Tree. Our strategy achieved a mean accuracy of 0.86, false positive rate of 0.13, and kappa index of 0.64, we concluded that the system has potential to be used in the design of robust controllers for robotic devices. Further research will be centered on real-time evaluation of our strategy to implement an MMEB-based BCI for personalized rehabilitation of people with lower-limb limitations, such as stroke or spinal cord injury survivors.

关键词： Support vector machines Protocols Accuracy Robot kinematics Stroke (medical condition) Feature extraction Real-time systems Electroencephalography Brain-computer interfaces Spinal cord injury

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A hardware-aware neural architecture search algorithm targeting low-end microcontrollers

A hardware-aware neural architecture search algorithm target...

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Ph.D. Research in Microelectronics and Electronics (PRIME)

作者： Andrea Mattia Garavagno Edoardo Ragusa Antonio Frisoli Paolo Gastaldo Department of Electrical Electronic Telecommunication Engineering and Naval Architecture DITEN University of Genoa Genoa Italy Department of Excellence of Robotics and AI Scuola Superiore Sant’Anna Institute of Mechanical Intelligence Pisa Italy

Hardware-aware neural architecture search (HW NAS), the process of automating the design of neural architectures taking into consideration hardware constraints, has already outperformed the best human designs on many tasks. However, it is known to be highly demanding in terms of hardware, thus limiting access to non-habitual neural network users. Fostering its adoption for the next-generation IoT and wearable devices design, we propose an HW NAS that can be run on laptops, even if not mounting a GPU. The proposed technique, designed to have both a low search cost and resource usage, produces tiny convolutional neural networks (CNNs) targeting low-end microcontrollers. It achieves state-of-the-art results in the human-recognition tasks, on the Visual Wake Word dataset a standard TinyML benchmark, in just 3:37:0 hours on a laptop mounting an 11th Gen Intel(R) Core(TM) i7-11370H CPU @ 3. 30GHz equipped with 16 GB of RAM and 512 GB of SSD, without using a GPU.

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An evaluation of "Crash Prediction Networks" (CPN) for autonomous driving scenarios in carla simulator

An evaluation of "Crash Prediction Networks" (CPN) for auton...

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2021 Workshop on Artificial Intelligence Safety, Safeai 2021

作者： Nair, Saasha Shafaei, Sina Auge, Daniel Knoll, Alois TeCIP Institute Scuola Superiore Sant’Anna Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa Italy Technical University of Munich Germany

Safeguarding the trajectory planning algorithms of autonomous vehicles is crucial for safe operation in mixed traffic scenarios. This paper proposes the use of an ensemble of neural networks to work together under the moniker of "Crash Prediction Networks". The system comprises multiple independent networks, each focusing on a different subset of sensory inputs. The aim is for the networks to work together in unison to reach a consensus of whether a vehicle might enter a catastrophic state to trigger an appropriate intervention. The proposed approach would act as an additional layer of safety by supervising the decision making module of an autonomous vehicle. Though the proposed approach encompasses all the sensors and allied paraphernalia, the scope of this paper is exclusively limited to exploring safety monitors for visual sensors. The approach can, however, be extrapolated to other sensors. The evaluation was conducted using the CARLA simulator for simple driving scenarios studying the benefits of modeling temporal features to capture the motion in the environment. Additionally, the paper studies the importance of ‘accounting for uncertainty’ in models dealing with vehicle safety. Copyright © 2021for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4 0

关键词： Autonomous vehicles

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Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost

arXiv

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arXiv 2024年

作者： Nayab, Sania Rossolini, Giulio Simoni, Marco Saracino, Andrea Buttazzo, Giorgio Manes, Nicolamaria Giacomelli, Fabrizio Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa Italy Institute of Informatics and Telematics National Research Council of Italy Italy Sapienza Università di Roma Italy Mediavoice Srl - Roma e Napoli Italy

Today’s large language models (LLMs) can solve challenging question-answering tasks, and prompt engineering techniques, such as chain-of-thought (CoT), have gained attention for enhancing the explanation and correctness of outputs. However, many models and techniques tend to produce excessively verbose and lengthy answers, leading to issues with both conciseness and generation time. To address this, this paper analyzes the impact of output lengths on LLM inference pipelines by introducing and proposing novel metrics to evaluate the correct conciseness of a model and related prompting techniques. Then, we examine the impact of controlling output length through a refined prompt engineering strategy, Constrained-CoT (CCoT), which encourages the model to produce more concise outputs. To better understand the effects of such a prompt, we also introduce two additional scores for analyzing the conciseness, measured in terms of redundancy and information flow in generated answers. Experiments on pretrained LLMs and multiple datasets demonstrate the benefits of the proposed metrics and the effectiveness of CCoT across different models. © 2024, CC BY-NC-SA.

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Controlling and powering a fully implantable artificial pancreas refillable by ingestible pills

Controlling and powering a fully implantable artificial panc...

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

作者： Giulia Ballardini Izadyar Tamadon Daniele Guarnera Hind Al-Haddad Veronica Iacovacci Francesco Mariottini Simone Ricciardi Alessio Cucini Alberto Dalla Libera Fabio Vistoli Arianna Menciassi Paolo Dario Claudio Cobelli Leonardo Ricotti The Department of Excellence in Robotics & AI BioRobotics Institute Scuola Superiore Sant’Anna Pisa Italy WAVECOMM s.r.l Italy Department of Information Engineering University of Padova Padova Italy Division of General and Transplant Surgery Azienda Ospedaliera Universitaria Pisana University of Pisa Pisa Italy Department of Woman and Child’s Health University of Padova Padova Italy

Over the past decade, there has been a growing interest in the development of an artificial pancreas for intraperitoneal insulin delivery. Intraperitoneal implantable pumps guarantee more physiological glycemic control than subcutaneous wearable ones, for the treatment of type 1 diabetes. In this work, a fully implantable artificial pancreas refillable by ingestible pills is presented. In particular, solutions enabling the communication between the implanted pump and external user interfaces and novel control algorithms to intraperitoneally release an adequate amount of insulin based on glycemic data are shown. In addition, the powering and the wireless battery recharging are addressed. Specifically, the design and optimization of a customized transcutaneous energy transfer with two independent wireless channels are presented. The system was tested in terms of recharging efficacy, possible temperature rise within the body, during the recharging process and reliability of the wireless connection in the air and in the presence of ex vivo *** Relevance— This work aims to improve the control, battery recharging, and wireless communication of a fully implantable artificial pancreas for type 1 diabetes treatment.

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Grasp force estimation from the transient EMG using high-density surface recordings

Grasp force estimation from the transient EMG using high-den...

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Journal of Neural Engineering

作者： Martinez, Itzel Jared Rodriguez Mannini, Andrea Clemente, Francesco Sabatini, Angelo Maria Cipriani, Christian Department of Excellence in Robotics and AI BioRobotics Institute Scuola Superiore sant'Anna Pisa Italy BioRobotics Institute Scuola Superiore sant'Anna Pisa Italy IRCCS Fondazione Don Carlo Gnocchi Via di Scandicci 269 Florence50143 Italy

Objective. Understanding the neurophysiological signals underlying voluntary motor control and decoding them for prosthesis control are among the major challenges in applied neuroscience and bioengineering. Usually, information from the electrical activity of residual forearm muscles (i.e. the electromyogram, EMG) is used to control different functions of a prosthesis. Noteworthy, forearm EMG patterns at the onset of a contraction (transient phase) have shown to contain predictive information about upcoming grasps. However, decoding this information for the estimation of grasp force (GF) was so far overlooked. Approach. High density-EMG signals (192 channels) were recorded from twelve participants performing a pick-and-lift task. The final GF was estimated offline using linear regressors, with four subsets of channels and ten features obtained using three channels-features selection methods. Two different evaluation metrics (absolute error and R 2), complemented with statistical analysis, were used to select the optimal configuration of the parameters. Different windows of data starting at the GF onset were compared to determine the time at which the GF can be ascertained from the EMG signals. Main results. The prediction accuracy improved by increasing the window length from the moment of the onset and kept improving until the steady state at which a plateau of performances was reached. With our methodology, estimations of the GF through 16 EMG channels reached an absolute error of 2.52% the maximum voluntary force using only transient information and 1.99% with the first 500 ms of data following the onset. Significance. The final GF estimation from transient EMG was comparable to the one obtained using steady state data, confirming our hypothesis that the transient phase contains information about the final GF. This result paves the way to fast online myoelectric controllers capable of decoding grasp strength from the very early portion of the EMG signal. © 2020 I

关键词： Decoding Prosthetics Elastic net Electrical activities grasp force HD EMG lasso Prediction accuracy Predictive information Transient information

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Deep-Learning Estimation of Weight Distribution Using Joint Kinematics for Lower-Limb Exoskeleton Control

arXiv

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arXiv 2024年

作者： Lhoste, Clément Küçüktabak, Emek Barış Vianello, Lorenzo Amato, Lorenzo Short, Matthew R. Lynch, Kevin M. Pons, Jose L. Legs and Walking Lab Shirley Ryan AbilityLab ChicagoIL United States Center for Robotics and Biosystems Department of Mechanical Engineering Northwestern University Shirley Ryan AbilityLab EvanstonIL United States Shirley Ryan AbilityLab The Biorobotics Institute Scuola Superiore Sant’Anna Pontedera56025 Italy Department of Excellence in Robotics & AI Scuola Superiore Sant’Anna Pisa56127 Italy Shirley Ryan AbilityLab Department of Biomedical Engineering Northwestern University EvanstonIL United States Center for Robotics and Biosystems Department of Mechanical Engineering Northwestern University United States Shirley Ryan AbilityLab Center for Robotics and Biosystems Department of Mechanical Engineering Department of Biomedical Engineering Department of Physical Medicine and Rehabilitation Northwestern University United States

In the control of lower-limb exoskeletons with feet, the phase in the gait cycle can be identified by monitoring the weight distribution at the feet. This phase information can be used in the exoskeleton’s controller to compensate the dynamics of the exoskeleton and to assign impedance parameters. Typically the weight distribution is calculated using data from sensors such as treadmill force plates or insole force sensors. However, these solutions increase both the setup complexity and cost. For this reason, we propose a deep-learning approach that uses a short time window of joint kinematics to predict the weight distribution of an exoskeleton in real time. The model was trained on treadmill walking data from six users wearing a four-degree-of-freedom exoskeleton and tested in real time on three different users wearing the same device. This test set includes two users not present in the training set to demonstrate the model’s ability to generalize across individuals. Results show that the proposed method is able to fit the actual weight distribution with R2 = 0.9 and is suitable for real-time control with prediction times less than 1 ms. Experiments in closed-loop exoskeleton control show that deep-learning-based weight distribution estimation can be used to replace force sensors in overground and treadmill walking. © 2024, CC BY.

关键词： Exoskeleton (robotics)

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Classifying Vocal Folds Fixation from Endoscopic Videos with Machine Learning

Classifying Vocal Folds Fixation from Endoscopic Videos with...

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

作者： Francesca Pia Villani Alberto Paderno Maria Chiara Fiorentino Alessandro Casella Cesare Piazza Sara Moccia Department of Humanities Università Degli Studi di Macerata Italy Department of Otorhinolaryngology – Head and Neck Surgery ASST Spedali Civili of Brescia University of Brescia Italy Department of Information Engineering Università Politecnica Delle Marche Italy Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy The BioRobotics Institute Scuola Superiore Sant’Anna and the Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Italy

Vocal folds motility evaluation is paramount in both the assessment of functional deficits and in the accurate staging of neoplastic disease of the glottis. Diagnostic endoscopy, and in particular videoendoscopy, is nowadays the method through which the motility is estimated. The clinical diagnosis, however, relies on the examination of the videoendoscopic frames, which is a subjective and professional-dependent task. Hence, a more rigorous, objective, reliable, and repeatable method is needed. To support clinicians, this paper proposes a machine learning (ML) approach for vocal cords motility classification. From the endoscopic videos of 186 patients with both vocal cords preserved motility and fixation, a dataset of 558 images relative to the two classes was extracted. Successively, a number of features was retrieved from the images and used to train and test four well-grounded ML classifiers. From test results, the best performance was achieved using XGBoost, with precision = 0.82, recall = 0.82, F1 score = 0.82, and accuracy = 0.82. After comparing the most relevant ML models, we believe that this approach could provide precise and reliable support to clinical *** Relevance— This research represents an important advancement in the state-of-the-art of computer-assisted otolaryngology, to develop an effective tool for motility assessment in the clinical practice.

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A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations

arXiv

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arXiv 2022年

作者： Fabisch, Alexander Uliano, Manuela Marschner, Dennis Laux, Melvin Brust, Johannes Controzzi, Marco Robotics Innovation Center DFKI GmbH Robert-Hooke-Straße 1 BremenD-28359 Germany The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy The Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy Robotics Research Group University of Bremen Germany Plan-Based Robot Control DFKI GmbH Germany

Manipulating objects with robotic hands is a complicated task. Not only the fingers of the hand, but also the pose of the robot's end effector need to be coordinated. Using human demonstrations of movements is an intuitive and data-efficient way of guiding the robot's behavior. We propose a modular framework with an automatic embodiment mapping to transfer recorded human hand motions to robotic systems. In this work, we use motion capture to record human motion. We evaluate our approach on eight challenging tasks, in which a robotic hand needs to grasp and manipulate either deformable or small and fragile objects. We test a subset of trajectories in simulation and on a real robot and the overall success rates are aligned. Copyright © 2022, The Authors. All rights reserved.

关键词： End effectors

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