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A pilot study to investigate possible differences between a virtual reality-based neurofeedback and a traditional neurofeedback is presented. Neurofeedback training aimed to strengthen the emotional regulation capacity. The neurofeedback task is to down-regulate negative emotions by decreasing the beta band power measured in the midline areas of the scalp (i.e., Fcz-Cpz). Negative International Affective Picture System images were chosen as eliciting stimuli. Three healthy subjects participated in the experimental activities. Each of them underwent three VR-based neurofeedback sessions and three neurofeedback sessions delivered on a traditional 2D screen. The neurofeedback training session was preceded by a calibration phase allowing to record the rest and the baseline values to adapt the neurofeedback system to the user. For the majority of sessions, the average value of the high beta band power during the neurofeedback training remained below the baseline, as expected. In compliance with previous studies, future works should investigate the virtual reality-based neurofeedback efficacy in physiological responses and behavioral performance.

A wireless and wearable device with a low number of channels and dry electrodes is proposed for EEG-based attention assessment during motor-rehabilitation tasks. The system is a part of an instrument for real-time engagement assessment in rehabilitation 4.0. An experimental campaign on nine volunteers was realized for metrologically characterizing the system. Common Spatial Pattern (CSP) algorithm was used for features selection from the brain signal. The performance of three different supervised classifiers for distracted and non-distracted conditions were compared. The higher accuracy, 71.63±3.43 %, was obtained by the k-Nearest Neighbors classifier.

A method for high-wearable EEG-based assessment of pediatric emotional and cognitive engagement in neuro-motor rehabilitation is proposed. A specific easy calibration is provided in the perspective of a personalized medicine. Due to the lack of studies evaluating pediatric multidimensional engagement, an observational non-interventional protocol was adopted for collecting the EEG data related to the high/low levels of engagement. The experimental validation of the proposed method involved four children performing a rehabilitation exercise in five 8-min sessions. Due to the age and frailty of the subjects, no negative emotions were expressly induced and an unbalanced dataset was obtained. Different Synthetic Minority Oversampling Technique (SMOTE)-based strategies for unbalanced dataset management and classification methods were compared. The highest performances were achieved by combining Artificial Neural Network (ANN) models with the KMeansSMOTE oversampling method. Balanced accuracies of 71.2 % and 74.5 % for the emotional engagement and the cognitive engagement are obtained, respectively.

Objectives: Technology is increasingly being used to help practise mindfulness. Immersive virtual reality-enhanced mindfulness may prove especially effective for a wide range of clinical interventions where traditional mindfulness is currently proving valuable. The current paper provides a preliminary survey of research on this topic, aimed at verifying scientific evidence that VR technology improves the practice of mindfulness and its therapeutic effectiveness. A recognition on emerging technological solutions aimed at improving decentering and interoceptive awareness (IA) in mindfulness interventions is also proposed. Methods: A systematic search was performed in ACM, Science Direct, Web of Science, Scopus, IEEE Xplore, and PubMed, using the following keywords: “mindfulness” AND “virtual reality”. Quality Assessment Tool for Quantitative Studies (QATQS) was used to assess study quality. Results: Fifty-three papers were considered in the review involving 1652 subjects. Pain, stress, depression, anxiety, borderline personality disorder, and addictions are the addressed clinical cases. The quality analysis did not reveal any strong quality papers and over 90% were rated as weak. According to the majority of the studies, VR guarantees increasing relaxation self-efficacy, reducing mind wandering, and preserving attention resources. Interoceptive awareness and decentering are both overlooked in the literature. Conclusions: VR exhibits potential favourable features to support mindfulness practice, especially immersive and multisensory VR. The use of bio/neurofeedback sensors allows an adaptive experience in real time. A design proposal for upcoming trends in VR-supported mindfulness was presented and the need for more rigorous, randomised controlled studies in the future was highlighted.

An ultrasonic heading measurement method, working under magnetic interference prohibitive for magnetometers, was prototyped, validated, and metrologically characterized. Two capacitive ultrasonic transducers convert the mechanical rotation in two correspondingly time-delayed electrical sine waves. Then, the time delay is estimated using the standard tree parameter sine-fitting algorithm. The prototyped goniometer achieves the same repeatability level (

A high wearable instrument for brain-computer interface (BCI), based on steady-state visual evoked potentials, and conceived with low-cost, off-The-shelf components, is proposed. Peculiar features are: 1) a single-channel differential acquisition; 2) active transducers using dry electrodes with metallic pins; 3) real-Time computation based on Goertzel algorithm, lighter than fast Fourier transform; and 4) absence of training need before the first use. In this way, the proposed instrument overcomes the state-of-The art issues of comfort, wearability, signal quality, and feasibility on limited resources devices (e.g., tablets or smartphones) of BCI applications. The accuracy results of the instrument prototype, assessed in an experimental campaign on human subjects in laboratory, foster its application in wearable biomedical devices.

Therapeutic effects of Mindfulness meditation practices in clinical interventions, specifically in the treatment of stress, anxiety, depression, chronic and acute pain are scientifically well founded. Mindfulness is increasingly being supported by technology and among various interventions immersive VR seems rather peculiar due to its ability to improve decentering and interoceptive awareness. A systematic review on Virtual Reality supported Mindfulness is currently being published. In this paper, some preliminary results of this review are presented, also providing a brief discussion about a possible evolutionary technological trend, on the basis of the input and output perceptual domains exploited.

A real-time monitoring system based on Augmented Reality (AR) and highly wearable Brain-Computer Interface (BCI) for hands-free visualization of patient's health in Operating Room (OR) is proposed. The system is designed to allow the anesthetist to monitor hands-free and in real-time the patient's vital signs collected from the electromedical equipment available in OR. After the analysis of the requirements in a typical Health 4.0 scenario, the conceptual design, implementation and experimental validation of the proposed system are described in detail. The effectiveness of the proposed AR-BCI-based real-time monitoring system was demonstrated through an experimental activity was carried out at the University Hospital Federico II (Naples, Italy), using operating room equipment.

A methodological contribution to a reproducible Measurement of Emotions for an EEG-based system is proposed. Emotional Valence detection is the suggested use case. Valence detection occurs along the interval scale theorized by the Circumplex Model of emotions. The binary choice, positive valence vs negative valence, represents a first step towards the adoption of a metric scale with a finer resolution. EEG signals were acquired through a 8-channel dry electrode cap. An implicit-more controlled EEG paradigm was employed to elicit emotional valence through the passive view of standardized visual stimuli (i.e., Oasis dataset) in 25 volunteers without depressive disorders. Results from the Self Assessment Manikin questionnaire confirmed the compatibility of the experimental sample with that of Oasis. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e., Hemispheric Asymmetry Theories), and (ii) automated (i.e., a pipeline of a custom 12-band Filter Bank and Common Spatial Pattern). An average within-subject accuracy of 96.1 %, was obtained by a shallow Artificial Neural Network, while k-Nearest Neighbors allowed to obtain a cross-subject accuracy equal to 80.2%.

A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient’s attention for enhancing the therapy effectiveness.

An instrument based on the integration of Brain Computer Interface (BCI) and Augmented Reality (AR) is proposed for robotic autism rehabilitation. Flickering stimuli at fixed frequencies appear on the display of Augmented Reality (AR) glasses. When the user focuses on one of the stimuli a Steady State Visual Evoked Potentials (SSVEP) occurs on his occipital region. A single-channel electroencephalographic Brain Computer Interface detects the elicited SSVEP and sends the corresponding commands to a mobile robot. The device's high wearability (single channel and dry electrodes), and the trainingless usability are fundamental for the acceptance by Autism Spectrum Disorder (ASD) children. Effectively controlling the movements of a robot through a new channel enhances rehabilitation engagement and effectiveness. A case study at an accredited rehabilitation center on 10 healthy adult subjects highlighted an average accuracy higher than 83%. Preliminary further tests at the Department of Translational Medical Sciences of University of Naples Federico II on 3 ASD patients between 8 and 10 years old provided positive feedback on device acceptance and attentional performance.

A multidisciplinary instrumentation and measurement course, for students in Information Communications Technology engineering, was conceived for facing the challenges of the fourth industrial revolution. The course is based on a “learning small enterprise” integrated with an academic FabLab, by exploiting Active Learning methodologies, in particular Cooperative Project-Based and Scenario-Based. Education activity is based on the model of (i) “learning factories” focused on process innovation, and (ii) small enterprises of the fourth industrial revolution, exploiting Internet of Things (IoT) and Additive Manufacturing technologies. A case study at University of Naples Federico II on the course “Instrumentation and Measurement for Smart Industry”, as well as on the correspondingly germinated academic Fablab, is presented. The students proved to be involved in the proposal and consider as relevant the impact of this experience on their competences after 6–36 months.

An highly-wearable (wireless, few–channels and dry electrodes) device is proposed for EEG based valence emotion recognition. The component is a part of an instrument for real time engagement assessment in rehabilitation 4.0. The frontal, central, and occipital asymmetry were used as well known features related to emotional valence. The device was metrologically characterized on human subjects emotionally elicited through passive viewing of pictures taken from Oasis data set. As metrological references, a standardized test, the Self Assessment Manikin, was exploited. A 2nd order polynomial kernel-based Support Vector Machine reached 83.2 ± 0.3% accuracy in classifying emotional valence from each 2-s epoch of EEG acquired signals.

An on-chip transducer, for monitoring noninvasively the insulin bio-availability in real time after administration in clinical diabetology, is proposed. The bioavailability is assessed as insulin decrease in situ after administration by means of local impedance measurement. Inter-and-intra individual reproducibility is enhanced by a personalized model, specific for the subject, identified and validated during each insulin administration. Such a real-time noninvasive bioavailability measurement allows to increase the accuracy of insulin bolus administration, by attenuating drawbacks of glycemic swings significantly. In the first part of this paper, the concept, the architecture, and the operation of the transducer, as well as details about its prototype, are illustrated. Then, the metrological characterization and validation are reported in laboratory, in vitro on eggplants, ex vivo on pig abdominal non-perfused muscle, and in vivo on a human subject, using injection as a reference subcutaneous delivery of insulin. Results of significant intra-individual reproducibility in vitro and ex vivo point out noteworthy scenarios for assessing insulin bioavailability in clinical diabetology.

A customizable Finite Elements Model of human knee is proposed for improving inter-individual reproducibility in NSAIDs transdermal delivery measurement. The model simulates: (i) the measurement system, based on Bio-Impedance Spectroscopy, and (ii) the system under test, namely the knee by five parallel, homogeneous, and concentric layers: bone, muscle, adipose tissue, wet skin, and dry skin. In this paper, first the equations and the architecture of the model are described. Then, the results of the numerical characterization and the preliminary experimental validation are reported. A sensitivity analysis was realized for reducing computational burden during Model customization. Only five parameters out of the 64 used in the Cole-Cole equation were sufficient for fitting experimental data of different subjects.

The present work is a first step in building a wearable system to monitor the heart functionality of a patient and assess the cardiovascular risk by means of non-invasive measurements, such as electrocardiogram (ECG), heart rate, blood oxygenation, and body temperature. Also clinic data obtained by means of a patient interview are taken into account. In this feasibility study, measures from a pre-existing dataset are exploited. They are processed with a machine learning algorithm. Features are first extracted from the measures collected with the wearable sensors. Then, these features are employed together with clinic data to classify the patients health status. A Random Forest classifier was employed and the algorithm was characterized considering different setups. The best accuracy resulted equal to 78.6% in distinguishing three classes of patients, namely healthy, unhealthy non-critical, and unhealthy critical patients.

A method for monitoring the misalignment of the magnetic axis in axially-symmetric magnets is proposed. Conversely to the other methods for magnet axis determination, the proposed method is suitable also when the magnet has been deployed and the surrounding equipment has been installed, often making the axis region and almost the whole remaining magnet aperture not accessible. Requiring only a few measurements of the magnetic field at fixed positions inside the magnet aperture, it overcomes the main drawback of the other Hall sensor-based methods which is having to deal with sturdy mechanics of the moving stages. In this paper the analytical description of the method is presented, and the sensitivity concerning the measurement is studied. Then, the validation of the measurement method, based on analytical simulations of the magnetic field inside an ideal solenoid magnetic aperture, is presented.

An experimental procedure for measuring the drug absorbed by a biological tissue in laboratory emulation of dermatological topical treatments is proposed. Laboratory emulation is based on the analysis of the eggplant electrical reaction to the injection of drug. Eggplant and human tissue are both well modeled by a distributed circuit model described by the ColeCole empirical equation. An exploratory measurement campaign aimed at investigating the relationship between the injected drug amount and the measured impedance is reported. The basic ideas, the measurement system design, and the proposed measurement procedure are illustrated. Then, its feasibility is proved experimentally and the results of the metrological characterization are reported and discussed. Results point out that, by a “simple” measurement of the impedance module (and not a spectroscopy), the amount of injected drug can be assessed by acceptable uncertainty.