检索结果-内蒙古大学图书馆

Thermal phantom sensations in arm amputees and what it means for future prosthetics

Temperature (Austin, Tex.) 2024年第2期11卷 107-109页

作者： Solaiman Shokur Silvestro Micera The BioRobotics Institute Health Interdisciplinary Center and Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy. Bertarelli Foundation Chair in Translational Neural Engineering Neuro-X Institute Ecole Polytechnique Federale de Lausanne Switzerland.

来源：评论

学校读者我要写书评

暂无评论

Structuring Clinical Notes of Italian ST-elevation Myocardial Infarction Patients 1

Structuring Clinical Notes of Italian ST-elevation Myocardia...

引用

1st Workshop on Patient-Oriented Language Processing, CL4Health 2024

作者： Torri, Vittorio Mazzucato, Sara Dalmiani, Stefano Paradossi, Umberto Passino, Claudio Moccia, Sara Micera, Silvestro Ieva, Francesca MOX - Modelling and Scientific Computing Lab Department of Mathematics Politecnico di Milano Italy Biorobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Italy Fondazione Toscana Gabriele Monasterio Italy Health Science Interdisciplinary Center Scuola Superiore Sant’Anna Italy Bertarelli Foundation Chair in Translational Neural Engineering Center for Neuroprosthetics Institute of Bioengineering Ecole Polytechnique Federale de Lausanne Switzerland HDS - Health Data Science Centre Human Technopole Italy

ISBN: (纸本)9782493814258

In recent years, it has become common for patients to get full access to their Electronic Health Records (EHRs), thanks to the advancements in the EHRs systems of many healthcare providers. While this access empowers patients and doctors with comprehensive and real-time health information, it also introduces new challenges, in particular due to the unstructured nature of much of the information within EHRs. To address this, we propose a pipeline to structure anamneses, providing patients with a clear and concise overview of their health data and its longitudinal evolution, also allowing clinicians to focus more on patient care during consultations. In this paper, we present preliminary results on extracting structured information from EHRs of patients diagnosed with ST-Elevation Myocardial Infarction from an Italian hospital. Our pipeline exploits text classification models to extract relevant clinical variables, comparing rule-based, recurrent neural network and BERT-based models. While various approaches utilized ontologies or knowledge graphs for Italian data, our work represents the first attempt to develop this type of pipeline. The results for the extraction of most variables are satisfactory (f1-score > 0.80), with the exception of the most rare values of certain variables, for which we propose future research directions to investigate. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

A Hardware-Efficient EMG Decoder with an Attractor-based neural Network for Next-Generation Hand Prostheses

A Hardware-Efficient EMG Decoder with an Attractor-based Neu...

引用

IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Mohammad Kalbasi MohammadAli Shaeri Vincent Alexandre Mendez Solaiman Shokur Silvestro Micera Mahsa Shoaran Institute of Electrical and Micro Engineering and Neuro-X Institute EPFL Geneva Switzerland Department of Electrical Engineering Sharif University of Technology Tehran Iran Bertarelli Foundation Chair in Translational Neuroengineering Neuro-X Institute EPFL Geneva Switzerland

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

Advancements in neural engineering have enabled the development of Robotic Prosthetic Hands (RPHs) aimed at restoring hand functionality. Current commercial RPHs offer limited control through basic on/off commands. Recent progresses in machine learning enable finger movement decoding with higher degrees of freedom, yet the high computational complexity of such models limits their application in portable devices. Future RPH designs must balance portability, low power consumption, and high decoding accuracy to be practical for individuals with disabilities. To this end, we introduce a novel attractor-based neural network to realize on-chip movement decoding for next-generation portable RPHs. The proposed architecture comprises an encoder, an attention layer, an attractor network, and a refinement regressor. We tested our model on four healthy subjects and achieved a decoding accuracy of 80.6±3.3%. Our proposed model is over 120 and 50 times more compact compared to state-of-the-art LSTM and CNN models, respectively, with comparable (or superior) decoding accuracy. Therefore, it exhibits minimal hardware complexity and can be effectively integrated as a System-on-Chip.

关键词： Accuracy Computational modeling neural networks Estimation Predictive models Electromyography Decoding

来源：评论

学校读者我要写书评

暂无评论

Neuroethology of natural actions in freely moving monkeys

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

EEG-based self-paced decoding of upper limb movement intention in healthy subjects

EEG-based self-paced decoding of upper limb movement intenti...

引用

Metrology for Extended Reality, Artificial Intelligence and neural engineering (MetroXRAINE), IEEE International Conference on

作者： Matteo Ceradini Stefano Tortora Luca Tonin Silvestro Micera The BioRobotics Institute and Department of Excellence in Robotics and AI Scuola Superiore Sant 'Anna Pisa Italy Department of Information Engineering University of Padova Padova Italy Padua Neuroscience Center University of Padova Padova Italy Bertarelli Foundation Chair in Translational Neuroengineering EPFL Geneva Switzerland

EEG-based brain-machine interfaces (BMIs) offer an intuitive approach for individuals with motor impairments to control prosthetic or rehabilitation devices. Decoding movement intentions plays a vital role in accurately translating the motor execution plans of subjects, such as identifying the desired grasp type or target position. In this study, EEG signals were recorded from seven healthy subjects during self-paced reaching and grasping tasks. Power Spectral Density (PSD) and Entropy were extracted as features to assess their efficacy in discrimination between different brain states related to movement planning. Classification between anticipation of movement and resting-state periods were evaluated using machine-learning methods (Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Support Vector Machines). The achieved results provide strong evidence for the feasibility of decoding movement intention, laying the foundation for future applications and advancements in the field. Attaining high accuracy in decoding movement intention holds significant potential for the translational applications of BMIs in the fields of biomedical engineering and rehabilitation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automatic Prediction of Upper Limb Motor Recovery in Stroke Patients Undergoing Robotic Therapy

Automatic Prediction of Upper Limb Motor Recovery in Stroke ...

引用

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

作者： Luigi Privitera Michael Lassi Stefania Dalise Valentina Azzolini Fulvio Vercillo Luca Maggiani Alberto Mazzoni Carmelo Chisari Silvestro Micera Andrea Bandini School of Advanced Studies Università di Camerino Italy Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna The BioRobotics Institute Scuola Superiore Sant’Anna Italy Neurorehabilitation Unit Pisa University Hospital Pisa Italy Interdisciplinary Research Center “Health Science” Scuola Superiore Sant’Anna Pisa Italy Bertarelli Foundation Chair in Translational Neural Engineering Center for Neuroprosthetics and Institute of Bioengineering Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

The recovery of upper extremity motor functions is crucial after a stroke. Robotic technologies have been employed for over two decades as part of rehabilitation strategies. However, the varying positive responses to robotic treatments emphasize the complexity of each clinical case. Addressing this diversity is a challenge in personalizing rehabilitation paths, underscoring the need for tailored recovery strategies. This study aims to develop a predictive method based on machine learning algorithms to anticipate upper limb motor recovery following therapy with the ALEx exoskeleton. The proposed approach automatically identifies patients more likely to benefit from robotic therapy. Through extensive feature extraction and validation (based on EMG and kinematics features of the upper limbs), we identified the contraction median of the brachioradialis muscle as the most predictive variable, resulting in an F1-Score of 1.0 in the majority of the classifiers. Our findings confirm that integrating EMG data during assessment can provide valuable information, enhancing overall evaluation and predicting motor recovery in stroke survivors undergoing robotic therapy.

关键词： Machine learning algorithms Exoskeletons Medical treatment Kinematics Stroke (medical condition) Muscles Motors Feature extraction Electromyography Robots

来源：评论

学校读者我要写书评

暂无评论

Classification of patients with cardiac amyloidosis using machine learning models on Italian electronic clinical health records*

Classification of patients with cardiac amyloidosis using ma...

引用

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

作者： Sara Mazzucato Andrea Bandini Silvestro Micera Giuseppe Vergaro Stefano Dalmiani Michele Emdin Claudio Passino Sara Moccia The BioRobotics Institute and the Department of Excellence in Robotics and Artificial Intelligence Scuola Superiore Sant’Anna Pisa Italy KITE Research Institute - Toronto Rehabilitation Institute University Health Network Toronto ON Canada Health Science Interdisciplinary Center Scuola Superiore Sant’Anna Pisa Italy Bertarelli Foundation Chair in Translational Neural Engineering Center for Neuroprosthetics and Institute of Bioengineering Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Fondazione Toscana Gabriele Monasterio Pisa Italy

Amyloidosis refers to a range of medical conditions in which misshapen proteins accumulate in various organs and tissues, forming insoluble fibrils. Cardiac amyloidosis is frequently linked to the buildup of misfolded transthyretin (TTR) or immunoglobulin light chains (AL). Delayed diagnosis, due to lack of disease awareness, results in a poor prognosis, especially in patients with AL amyloidosis. Early identification is therefore a key factor to improve patient outcomes. This study investigates the use of supervised machine-learning algorithms to support clinicians in classifying amyloidosis and control subjects. The aim of this work is to foster model interpretability reporting the most important risk factors in predicting the presence of cardiac amyloidosis. We analyzed electronic health records (EHRs) of 418 participants acquired in a time window of 12 years as part of a case-control study conducted in Fondazione Toscana Gabriele Monasterio (Italy) clinical practice. This work paves the way for the creation of digital health solutions that can aid in amyloidosis screening. The effective handling, analysis, and interpretation of these solutions can have a transformative effect on modern healthcare, offering new opportunities for improved patient care.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Learning with Convolutional neural Network for Proportional Control of Finger Movements from surface EMG Recordings

Deep Learning with Convolutional Neural Network for Proporti...

引用

International IEEE/EMBS Conference on neural engineering, CNE

作者： V. Mendez L. Pollina F. Artoni S. Micera Bertarelli Foundation Chair in Translational Neuroengineering Centre for Neuroprosthetics and Institute of Bioengineering School of Engineering Ecole Polytechnique Fèdèrale de Lausanne Lausanne Switzerland Functional Brain Mapping Laboratory Faculty of Medicine University of Geneva Geneva Switzerland

The control of robotic prosthetic hands (RPHs) for upper limb amputees is far from optimal. Simultaneous and proportional finger control of a RPH based on EMG signals is still challenging. Based on EMG and kinematics recordings of subjects following a pre-defined sequence of single and multi-fingers movements, we aimed at predicting finger flexion and thumb opposition angles. We compared two deep learning (DL) based approaches, the first one using the raw EMG signals and the second one using the spectrogram of the signal as input, with the standard state of the art decoding technique (STD) for finger angle regression. Using a genetic algorithm for hyper-parameter optimization, we obtained an optimized model architecture (and set of features in the case of STD) for each condition on one recording session. Then, we evaluated the best model of each condition on the eleven EMG and finger kinematics recordings available from four subjects. The two DL approaches based on convolutional neural networks predicted finger angles with a similar mean squared error loss but both of them outperformed the standard approach for the regression of simultaneous single-finger angles. This proposed decoding strategy and hyperparameter optimization framework provides a basis to further improve single finger proportional control for RPHs.

关键词： Deep learning Proportional control Thumb Kinematics Electromyography Decoding Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Facial Asymmetry Classification in Neurological Disorders: Integrating Computer Vision and Machine Learning for Improved Patient Care

Facial Asymmetry Classification in Neurological Disorders: I...

引用

IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： Pratik Ranjan Angelo Lasala Anna Lina Ruscelli Sujit Kumar Sahu Diego L. Guarín Sara Moccia Piero Castoldi Silvestro Micera Andrea Bandini School of Advanced Studies University of Camerino Italy The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy TeCIP Institute Health Science Interdisciplinary Center Scuola Superiore Sant'Anna Pisa Italy TeCIP Institute Scuola Superiore Sant'Anna Pisa Italy Department of Applied Physiology and Kinesiology Department of Biomedical Engineering University of Florida Gainesville FL US The BioRobotics Institute Health Science Interdisciplinary Center Scuola Superiore Sant'Anna Pisa Italy Bertarelli Foundation Chair in Translational Neuroengineering EPFL Geneva Switzerland Health Science Interdisciplinary Center The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy

ISBN: (数字)9798350362138

ISBN: (纸本)9798350362145

Facial gestures and movements play a critical role in communicating, eating, and expressing emotions, making the assessment of oro-facial functions vital in clinical practice. Neurological conditions such as stroke and facial palsy significantly impact these movements, necessitating accurate differentiation for appropriate diagnosis. This paper proposes an automated approach to classifying facial asymmetry in stroke and peripheral facial paralysis, leveraging computer vision and machine learning algorithms. Using public datasets like the Toronto NeuroFace and Massachusetts Eye and Ear Infirmary (MEEI), we employed facial landmark localization, comprehensive feature extraction, and classification techniques to discern subtle variations in facial movements and expressions across different conditions. Our approach achieved promising results, with accuracy up to 88% to distinguish facial impairments due to stroke and facial palsy. Tasks such as lip spreading, blinking, and eyebrow-raising demonstrated high accuracy, aligning with previous findings. Our study lays the groundwork for improving the diagnosis and treatment of oro-facial impairments using machine learning, with potential applications in clinical and emergency settings to enhance patient care and diagnostic accuracy.

关键词： Neurological diseases Location awareness Industries Computer vision Technological innovation Accuracy Machine learning algorithms Lips Machine learning Paralysis

来源：评论

学校读者我要写书评

暂无评论

False perspectives on human language: why statistics needs linguistics

arXiv

引用

arXiv 2023年

作者： Greco, Matteo Cometa, Andrea Artoni, Fiorenzo Frank, Robert Moro, Andrea University School for Advanced Studies IUSS Pavia Pavia27100 Italy The Biorobotics Institute Department of Excellence in AI and Robotics Scuola Superiore Sant’Anna Pisa Italy Ecole Polytechnique Federale de Lausanne Bertarelli Foundation Chair in Translational NeuroEngineering Center for Neuroprosthetics School of Engineering Chemin des Mines 9 GenevaGE CH 1202 Switzerland Functional Brain Mapping Laboratory Department of Basic Neurosciences University of Geneva Chemin des Mines 9 GenevaGE CH 1202 Switzerland Department of Linguistics Yale University New Haven United States

A sharp tension exists about the nature of human language between two opposite parties: those who believe that statistical surface distributions, in particular using measures like surprisal, provide a better understanding of language processing, vs. those who believe that discrete hierarchical structures implementing linguistic information such as syntactic ones are a better tool. In this paper, we show that this dichotomy is a false one. Relying on the fact that statistical measures can be defined on the basis of either structural or non-structural models, we provide empirical evidence that only models of surprisal that reflect syntactic structure are able to account for language regularities. © 2023, CC BY-NC-ND.

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：