Noninvasive measurement of transdermal drug delivery by impedance spectroscopy

The effectiveness in transdermal delivery of skin permeation strategies (e.g., chemical enhancers, vesicular carrier systems, sonophoresis, iontophoresis, and electroporation) is poorly investigated outside of laboratory. In therapeutic application, the lack of recognized techniques for measuring the actually-released drug affects the scientific concept itself of dosage for topically- and transdermally-delivered drugs. Here we prove the suitability of impedance measurement for assessing the amount of drug penetrated into the skin after transdermal delivery. In particular, the measured amount of drug depends linearly on the impedance magnitude variation normalized to the pre-treated value. Three experimental campaigns, based on the electrical analysis of the biological tissue behavior due to the drug delivery, are reported: (i) laboratory emulation on eggplants, (ii) ex-vivo tests on pig ears, and finally (iii) in-vivo tests on human volunteers. Results point out that the amount of delivered drug can be assessed by reasonable metrological performance through a unique measurement of the impedance magnitude at one single frequency. In particular, in-vivo results point out sensitivity of 23 ml(-1), repeatability of 0.3%, non-linearity of 3.3%, and accuracy of 5.7%. Finally, the measurement resolution of 0.20 ml is compatible with clinical administration standards

A Wearable SSVEP BCI for AR-based, Real-time Monitoring Applications

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

Active and Passive Brain-Computer Interfaces Integrated with Extended Reality for Applications in Health 4.0

This paper presents the integration of extended reality (XR) with brain-computer interfaces (BCI) to open up new possibilities in the health 4.0 framework. Such integrated systems are here investigated with respect to an active and a passive BCI paradigm. Regarding the active BCI, the XR part consists of providing visual and vibrotactile feedbacks to help the user during motor imagery tasks. Therefore, XR aims to enhance the neurofeedback by enhancing the user engagement. Meanwhile, in the passive BCI, user engagement monitoring allows the adaptivity of a XR-based rehabilitation game for children. Preliminary results suggest that the XR neurofeedback helps the BCI users to carry on motor imagery tasks with up to 84% classification accuracy, and that the level of emotional and cognitive engagement can be detected with an accuracy greater than 75%

EEG-based measurement system for monitoring student engagement in learning 4.0

A wearable system for the personalized EEG-based detection of engagement in learning 4.0 is proposed. In particular, the effectiveness of the proposed solution is assessed by means of the classification accuracy in predicting engagement. The system can be used to make an automated teaching platform adaptable to the user, by managing eventual drops in the cognitive and emotional engagement. The effectiveness of the learning process mainly depends on the engagement level of the learner. In case of distraction, lack of interest or superficial participation, the teaching strategy could be personalized by an automatic modulation of contents and communication strategies. The system is validated by an experimental case study on twenty-one students. The experimental task was to learn how a specific human-machine interface works. Both the cognitive and motor skills of participants were involved. De facto standard stimuli, namely (1) cognitive task (Continuous Performance Test), (2) music background (Music Emotion Recognition-MER database), and (3) social feedback (Hermans and De Houwer database), were employed to guarantee a metrologically founded reference. In within-subject approach, the proposed signal processing pipeline (Filter bank, Common Spatial Pattern, and Support Vector Machine), reaches almost 77% average accuracy, in detecting both cognitive and emotional engagement

Preliminary experimental identification of a FEM human knee model

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

A finite element model of abdominal human tissue for improving the accuracy in insulin absorption assessment: A feasibility study

A Finite Element Model of the human abdomen biomechanics for patients undergoing diabetes therapies was developed. In particular, FEM was used to improve a previous insulin absorption measurement method based on bioimpedance spectroscopy (BIS). As a matter of facts, the noise introduced during the insulin injection phase significantly affects the BIS measurements. The noise, due to the pressure exerted on the abdomen tissue, arises sensibility issues on the signal correlated to the drug presence under the skin. In this study, the abdomen is modeled with three layers (skin, fat and muscle). A feasibility study about the decoupling of the mechanical deformation and the electrical dynamics is presented in order to model the effect of mechanical uncertainty sources (e.g., pressure exerted during the injection phase and/or breathing) on the impedance measurements. The proposed simplified model is realised by referring to the average values of skin, fat and muscle thickness, along with mechanical abdomen parameters al-ready presented and validated in scientific literature. The obtained results confirm the possibility to decouple me-chanical and electrical analyses when the excitation voltage is characterized by a frequency higher than 1 kHz. The results will be used to improve the accuracy of an exhaustive approach, already developed by the authors, for real-time insulin absorption measurement

Subjective Symptoms in Magnetic Resonance Imaging Personnel: A Multi-Center Study in Italy

Introduction: Magnetic Resonance Imaging (MRI) personnel have significant exposure to static and low-frequency time-varying magnetic fields. In these workers an increased prevalence of different subjective symptoms has been observed. The aim of our study was to investigate the prevalence of non-specific subjective symptoms and of “core symptoms” in a group of MRI personnel working in different centers in Italy, and of possible relationships with personal and occupational characteristics.Methods: The occurrence of 11 subjective symptoms was evaluated using a specific questionnaire with 240 subjects working in 6 different Italian hospitals and research centers, 177 MRI health care and research personnel and 63 unexposed subjects employed in the same departments. Exposure was subjectively investigated according to the type of MRI scanner (≤1.5 vs. ≥3 T) and to the number of MRI procedures attended and/or performed by the personnel, even if no information on how frequently the personnel entered the scanner room was collected. The possible associations among symptoms and estimated EMF exposure, the main characteristics of the population, and job stress perception were analyzed.Results: Eighty-six percent of the personnel reported at least one symptom; drowsiness, headache, and sleep disorders were the most frequent. The total number of symptoms did not differ between exposed persons and controls. Considering the total number of annual MRI procedures reported by the personnel, no significant associations were found nor with the total number of symptoms, nor with “core symptoms.” Only subjects complaining of drowsiness also reported a significantly higher mean annual number of MRI procedures with ≤ 1.5 T scanners when compared with exposed subjects without drowsiness. In a multivariate model, subjects with a high level of perceived stress complained of more symptoms (p = 0.0002).Conclusions: Our study did not show any association between the occurrence of reversible subjective symptoms, including the more specific “core symptoms,” and the occupational exposure of MRI personnel to static and low-frequency time-varying magnetic fields. On the other hand, the role played by occupational stress appears to be not negligible. In further research in this field, measurements of EMF exposure should be considered

Measurement instrumentation in Passive Brain-Computer Interfaces

EEG Passive Brain Computer Interfaces assess cognitive and emotional condition of the user by means of electric signal acquired from the scalp. In the framework of Industry 4.0, Passive BCI represents a promising monitoring channel to improve human-machine interaction and integration. In this thesis, the prototyping and characterization of BCI measurement instrumentation to detect basic and complex mental states are presented. Both off-the-shelf instrumentation and CE-marked devices for medical use are exploited to acquire brain signals. The proposed solutions address the challenge of maximizing hardware wearability (minimizing the number of channels and employing dry electrodes) without penalizing accuracy and latency. To this end, appropriate signal processing strategies based on data-driven approaches are developed. Semi-custom machine learning algorithms are implemented for feature extraction and classification. Emotional valence, rehabilitation distraction, learning engagement, and work-related stress are the case studies proposed to experimentally validate the measurement instrumentation. Databases of EEG signals available online were consulted and experimental campaigns were conducted for a total of more than 200 subjects. Crucial metrological issues in the measurement instrumentation of passive BCIs are explored: e.g., definition of the measurand and its compatibility with the quantitative approach, experimental reproducibility, as well as cross- and within-subject reproducibility. The within-subjects accuracy exceeded 92% and 95% for distraction and emotional valence, respectively. The cross-subject accuracy reached 99% in recognition of a stressful condition