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A Machine Learning Approach to Classify Driver Mental Workload as Assessed by Electroencephalography through Infrared Thermal Imaging

Mental workload (MW) describes the brain commitment needed to accomplish a task. The ecological monitoring of MW allows to potentially support individuals in several ergonomic applications, such as Advanced driver assistance systems (ADAS). In this field, the employment of noninvasive contactless techniques to measure the MW is advantageous to not restrain the driver. To this aim, Infrared Thermal Imaging (IRI) is highly suited thanks to its contactless and lightweight features. This study ai...

Deep Learning-Based Assessment of POMA Scores Through EMG Derived Correlation and Coherence Matrices

Stroke is a critical medical condition characterized by the sudden interruption of blood flow to the brain, often resulting in cellular damage or, in severe cases, tissue necrosis. The consequences of stroke vary significantly among individuals, ranging from mild functional impairments to profound disabilities. Hence, gait rehabilitation is a cornerstone of stroke recovery, aiming to restore mobility and improve functional outcomes. Moreover, the effectiveness of rehabilitation interventions ...

An Overview of Approaches and Methods for the Cognitive Workload Estimation in Human–Machine Interaction Scenarios through Wearables Sensors

Background: Human-Machine Interaction (HMI) has been an important field of research in recent years, since machines will continue to be embedded in many human actvities in several contexts, such as industry and healthcare. Monitoring in an ecological mannerthe cognitive workload (CW) of users, who interact with machines, is crucial to assess their level of engagement in activities and the required effort, with the goal of preventing stressful circumstances. This study provides a comprehensive...

Machine learning and deep learning applied to EEG and fNIRS for early autism spectrum disorder diagnosis: a systematic review

Introduction: Autism Spectrum Disorder (ASD) presents considerable diagnostic challenges due to its heterogeneous nature and early developmental onset. In recent years, the convergence of noninvasive neuroimaging modalities such as Electroencephalography (EEG) and Functional Near Infrared Spectroscopy (fNIRS) with machine learning (ML) and deep learning (DL) techniques has opened new avenues for uncovering objective biomarkers of ASD. EEG offers millisecond level resolution of brain electrica...

Data-Driven Identification of Stroke through Machine Learning Applied to Complexity Metrics in Multimodal Electromyography and Kinematics

A stroke represents a significant medical condition characterized by the sudden interruption of blood flow to the brain, leading to cellular damage or death. The impact of stroke on individuals can vary from mild impairments to severe disability. Treatment for stroke often focuses on gait rehabilitation. Notably, assessing muscle activation and kinematics patterns using electromyography (EMG) and stereophotogrammetry, respectively, during walking can provide information regarding pathological...

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Prodotti recenti

A Machine Learning Approach to Classify Driver Mental Workload as Assessed by Electroencephalography through Infrared Thermal Imaging

Deep Learning-Based Assessment of POMA Scores Through EMG Derived Correlation and Coherence Matrices

An Overview of Approaches and Methods for the Cognitive Workload Estimation in Human–Machine Interaction Scenarios through Wearables Sensors

Machine learning and deep learning applied to EEG and fNIRS for early autism spectrum disorder diagnosis: a systematic review

Data-Driven Identification of Stroke through Machine Learning Applied to Complexity Metrics in Multimodal Electromyography and Kinematics

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