Development of a Universal Validation Protocol and an Open-Source Database for Multi-Contextual Facial Expression Recognition.

Facial expression recognition (FER) poses a complex challenge due to diverse factors such as facial morphology variations, lighting conditions, and cultural nuances in emotion representation. To address these hurdles, specific FER algorithms leverage advanced data analysis for inferring emotional states from facial expressions. In this study, we introduce a universal validation methodology assessing any FER algorithm's performance through a web application where subjects respond to emotive images.

A Practical Approach to the Analysis and Optimization of Neural Networks on Embedded Systems.

The exponential increase in internet data poses several challenges to cloud systems and data centers, such as scalability, power overheads, network load, and data security. To overcome these limitations, research is focusing on the development of edge computing systems, i.e.

Natural language processing in low back pain and spine diseases: A systematic review.

Natural Language Processing (NLP) is a discipline at the intersection between Computer Science (CS), Artificial Intelligence (AI), and Linguistics that leverages unstructured human-interpretable (natural) language text. In recent years, it gained momentum also in health-related applications and research. Although preliminary, studies concerning Low Back Pain (LBP) and other related spine disorders with relevant applications of NLP methodologies have been reported in the literature over the last few years.

Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.

Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP.

Prediction of Glucose Concentration in Children with Type 1 Diabetes Using Neural Networks: An Edge Computing Application.

Background: Type 1 Diabetes Mellitus (T1D) is an autoimmune disease that can cause serious complications that can be avoided by preventing the glycemic levels from exceeding the physiological range. Straightforwardly, many data-driven models were developed to forecast future glycemic levels and to allow patients to avoid adverse events. Most models are tuned on data of adult patients, whereas the prediction of glycemic levels of pediatric patients has been rarely investigated, as they represent the most challenging T1D population.

Essential elements in determining patient's complexity of nursing care.

Background: Nurses dedicate majority of working time to satisfy patients' needs, which are oftentimes complex. Although the concept of patient's complexity of care (PCC) has been extensively studied, it remains undefined in its essential characteristics. Various tools for assessing PCC have been developed, yet, there is no gold standard of reference.

TMS-EEG Biomarkers of Amnestic Mild Cognitive Impairment Due to Alzheimer's Disease: A Proof-of-Concept Six Years Prospective Study.

Early and affordable identification of subjects with amnestic mild cognitive impairment (aMCI) who will convert to Alzheimer's disease (AD) is a major scientific challenge. To investigate the neurophysiological hallmarks of sensorimotor cortex function in aMCI under the hypothesis that some may represent the plastic rearrangements induced by neurodegeneration, hence predictors of future conversion to AD. We sought to determine (1) whether the sensorimotor network shows peculiar alterations in patients with aMCI and (2) if sensorimotor network alterations predict long-term disease progression at the individual level.

Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.

Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP.

A Multi-Sensor System for Sea Water Iodide Monitoring and Seafood Quality Assurance: Proof-of-Concept Study.

Iodine is a trace chemical element fundamental for a healthy human organism. Iodine deficiency affects about 2 billion people worldwide causing from mild to severe neurological impairment, especially in children. Nevertheless, an adequate nutritional intake is considered the best approach to prevent such disorders.

Measuring the effects of first antiepileptic medication in Temporal Lobe Epilepsy: Predictive value of quantitative-EEG analysis.

Objective: To determine the quantitative EEG responses in a population of drug-naïve patients with Temporal Lobe Epilepsy (TLE) after Levetiracetam (LEV) initiation as first antiepileptic drug (AED). We hypothesized that the outcome of AED treatment can be predicted from EEG data in patients with TLE.

Methods: Twenty-three patients with TLE and twenty-five healthy controls were examined.

CO and O Detection by Electric Field Sensors.

In this work an array of chemical sensors for gas detection has been developed, starting with a commercial sensor platform developed by Microchip (GestIC), which is normally used to detect, trace, and classify hand movements in space. The system is based on electric field changes, and in this work, it has been used as mechanism revealing the adsorption of chemical species CO and O. The system is composed of five electrodes, and their responses were obtained by interfacing the sensors with an acquisition board based on an ATMEGA 328 microprocessor (Atmel MEGA AVR microcontroller).

Different level of virtualization of sight and touch produces the uncanny valley of avatar's hand embodiment.

Humans increasingly often act through virtual and robotic avatars, which can feed back to their user only virtual sensory information. Since avatar is user's embodiment and body image is mostly based on senses, how virtualization of sensory inputs affects avatar self-attribution is a key question for understanding nowadays human behavior. By manipulating visual and tactile inputs in a series of experiments fashioned after the rubber hand illusion, we assessed the relative weight of the virtualization of sight (Real, Robotic, Virtual) and of touch (Real, Virtual) on artificial hand embodiment.

VENUS BY BOTTICELLI AND HER PITUITARY ADENOMA.

Simonetta Vespucci, considered the most beautiful woman of the Renaissance, is the inspiration and face of one of the most famous paintings of all times, "The Birth of Venus," by Botticelli. She died in 1476 at the age of 23 years. We postulate she suffered from a pituitary-secreting tumor progressing to pituitary apoplexy.

A Mobile App for the Remote Monitoring and Assistance of Patients with Parkinson's Disease and their Caregivers.

The remote monitoring of patients is based on digital systems that enable the remote collection, usually at home, of health data and its transmission to health centers. The telemedicine paradigm is of particular interest in chronic diseases, fragile population and elderly monitoring. Parkinson's disease (PD) is a neurodegenerative disorder having high impact on the lives of patients and their families.

Advances in the Electronics for Cyclic Voltammetry: the Case of Gas Detection by Using Microfabricated Electrodes.

This paper presents an advanced voltammetric system to be used as electronic tongue for liquid and gas analysis. It has been designed to be more flexible and accurate with respect to other existing and similar systems. It features improved electronics and additional operative conditions.

An integrated system for the monitoring of therapy and drug's side effects in Lymphoproliferative disorders.

The wide diffusion of telecommunication systems and the availability of cheap computational devices, such as smart wearable, PDA and smartphones, is multiplying the number of collaborative and remote-monitored applications accessible to a large mass of people. In particular, this scenario makes it possible the implementation of specific systems that improve the control of patients with minimal impact on the quality of their lives. This paper moves in this context and presents a general system for the continuous monitoring at home of therapy and disease symptoms.

Age-related changes of cortical excitability and connectivity in healthy humans: non-invasive evaluation of sensorimotor network by means of TMS-EEG.

The sensorimotor cortical system undergoes structural and functional changes across its lifespan. Some of these changes are physiological and parallel the normal aging process, while others might represent pathophysiological mechanisms underlying neurodegenerative disorders. In the last years, the study of possible age-related modifications in brain sensorimotor functional characteristics has been the focus of several research projects.

Age related differences in functional synchronization of EEG activity as evaluated by means of TMS-EEG coregistrations.

It was recently demonstrated that the characteristics of EEG rhythms preceding a transcranial magnetic stimulation (TMS) of the motor cortex (M1) influence the motor-evoked potential (MEP) amplitude with a peculiar pattern, thus reflecting the M1 functional state. As physiological aging is related to a decrease in motor performance and changes in excitability and connectivity strength within cerebral sensorimotor circuits, we aimed to explore whether aging affects EEG-MEP interactions. Using MRI-navigated TMS and multichannel EEG, we compared the EEG-MEP interactions observed in healthy aged subjects with those observed in young volunteers.

Optimal weighted averaging of event related activity from acquisitions with artifacts.

In several biomedical applications that require the signal processing of biological data, the starting procedure for noise reduction is the ensemble averaging of multiple repeated acquisitions (trials). This method is based on the assumption that each trial is composed of two additive components: (i) a time-locked activity related to some sensitive/stimulation phenomenon (ERA, Event Related Activity in the following) and (ii) a sum of several other non time-locked background activities. The averaging aims at estimating the ERA activity under very low Signal to Noise and Interference Ratio (SNIR).

Sensorimotor cortex excitability and connectivity in Alzheimer's disease: A TMS-EEG Co-registration study.

Several studies have shown that, in spite of the fact that motor symptoms manifest late in the course of Alzheimer's disease (AD), neuropathological progression in the motor cortex parallels that in other brain areas generally considered more specific targets of the neurodegenerative process. It has been suggested that motor cortex excitability is enhanced in AD from the early stages, and that this is related to disease's severity and progression. To investigate the neurophysiological hallmarks of motor cortex functionality in early AD we combined transcranial magnetic stimulation (TMS) with electroencephalography (EEG).

Neurophysiological features of motor cortex excitability and plasticity in Subcortical Ischemic Vascular Dementia: a TMS mapping study.

Objective: To evaluate neurophysiological features of M1 excitability and plasticity in Subcortical Ischemic Vascular Dementia (SIVD), by means of a TMS mapping study.

Methods: Seven SIVD and nine AD patients, along with nine control subjects were tested. The M1 excitability was studied by resting thresholds, area and volume of active cortical sites for forearm and hand's examined muscles.

Does an intraneural interface short-term implant for robotic hand control modulate sensorimotor cortical integration? An EEG-TMS co-registration study on a human amputee.

Purpose: Following limb amputation, central and peripheral nervous system relays partially maintain their functions and can be exploited for interfacing prostheses. The aim of this study is to investigate, for the first time by means of an EEG-TMS co-registration study, whether and how direct bidirectional connection between brain and hand prosthesis impacts on sensorimotor cortical topography.

Methods: Within an experimental protocol for robotic hand control, a 26 years-old, left-hand amputated male was selected to have implanted four intrafascicular electrodes (tf-LIFEs-4) in the median and ulnar nerves of the stump for 4 weeks.

Complex epileptic palilalia: a case report.

Palilalia is a kind of motor perseveration involving speech, consisting in the continuous repetition of words or phrases, or both. Rarely it occurs in seizure disorders as ictal or post-ictal manifestation. We report a case of epileptic palilalia in a 71-year-old patient with a previous history of intracerebral haemorrhage in the left frontal region, characterized by the continuous repetition of the same speech sequence for few minutes.

Denoising and harmonic artifacts rejection for ECG P-waves by quadratic variation reduction.

Atrial fibrillation (AF) is a common cardiac arrhythmia related to irregular atrial contractions. Several studies have shown that the analysis of P-waves extracted from ECG signals is helpful in understanding the predisposing factors to AF. However, P-waves are usually highly corrupted by noise and harmonic artifacts and this makes quite difficult their analysis.

Baseline wander estimation and removal by quadratic variation reduction.

The baseline wander is a low frequency additive noise partially overlapping the band of ECG signal. This makes its removal difficult without affecting the ECG. In this work we propose a novel approach to baseline wander estimation and removal based on the notion of quadratic variation.