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.

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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. For people living with diabetes (PWD), a full control of blood glucose level is a challenge of wide clinical, social, and economic importance 1. The Artificial Pancreas (AP) represents a promising response to this need 2. AP, known as closed-loop control of blood glucose in diabetes, combines a glucose sensor, a control system, and an insulin infusion device (Insulin Pump). Glucose control is aimed at measuring the patient's need to adjust insulin dosage, or even to shut down insulin delivery in case of hypoglycemia (e.g. caused by exercise, miscalculated insulin bolus). The loop is closed in case of basal insulin administration. In case of meals, even the most recent automated systems cannot react to the quick variations of glucose due to food ingestion. Both for commercially available and do-it-yourself artificial pancreas systems mealtime boluses need to be manually delivered by the user-this is why the systems are still called "hybrid-closed loop systems" 3,4. A generic bolus calculation algorithm needs personalized control inputs as: Total Daily Dose of insulin (TDD), carbintake, basal/bolus ratio, basal segments, carbohydrate-to-insulin ratio (CIR), target range, insulin sensitivity factor (ISF), and insulin duration of action. In particular, the levels of ISF and insulin duration are fixed during the initial calibration of the algorithm. However, they can be subject to relevant variations depending on the kinetics of the insulin absorption. Intra-and inter-individual variability in insulin absorption are well-known drawbacks for therapy in PWD. Several factors contribute to such a variability, including insulin type, bolus size, injection site, and the presence of skin alterations, such as lipo-hypertrophic nodules 5. A more accurate administration of insulin bolus can be guaranteed by a real-time monitoring of the amount of dru...

A systematic review on electroencephalographic (EEG)-based feature extraction strategies to diagnosis and therapy of attention deficit hyperactivity disorder (ADHD) in children is presented. The analysis is realized at an executive function level to improve the research of neurocorrelates of heterogeneous disorders such as ADHD. The Quality Assessment Tool for Quantitative Studies (QATQS) and field-weighted citation impact metric (Scopus) were used to assess the methodological rigor of the studies and their impact on the scientific community, respectively. One hundred and one articles, concerning the diagnostics and therapy of ADHD children aged from 8 to 14, were collected. Event-related potential components were mainly exploited for executive functions related to the cluster inhibition,whereas band power spectral density is the most considered EEG feature for executive functions related to the cluster working memory. This review identifies the most used (also by rigorous and relevant articles) EEG signal processing strategies for executive function assessment in ADHD.

A wearable system for the personalized EEG-based detection of engagement in learning 4.0 is proposed. The system can be used to make an automated teaching platform adaptable to the cognitive and emotional conditions of the user. For example, the teaching strategy could be personalized by an automatic modulation of the proposed contents. The system is validated by an experimental case study on twenty-one students. The experimental task consisted in learning how a specific human-machine interface works. Both the cognitive and motor skills of participants were involved. De facto standard stimuli based on cognitive task and music background were employed to guarantee a metrologically founded reference. The proposed signal processing pipeline (Filter bank, Common Spatial Pattern, and Support Vector Machine), in within-subject approach, reaches almost 77 % average accuracy by a 3 s time window, in detecting both cognitive and emotional engagement.

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A personalized model of the human knee for enhancing the inter-individual reproducibility of a measurement method for monitoring Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) after transdermal delivery is proposed. The model is based on the solution of Maxwell Equations in the electric-quasi-stationary limit via Finite Element Analysis. The dimensions of the custom geometry are estimated on the basis of knee circumference at the patella, body mass index, and sex of each individual. An optimization algorithm allows to find out the electrical parameters of each subject by experimental impedance spectroscopy data. Muscular tissues were characterized anisotropically, by extracting Cole-Cole equation parameters from experimental data acquired with twofold excitation, both transversal and parallel to tissue fibers. A sensitivity and optimization analysis aiming at reducing computational burden in model customization achieved a worst-case reconstruction error lower than 5%. The personalized knee model and the optimization algorithm were validated in vivo by an experimental campaign on thirty volunteers, 67% healthy and 33% affected by knee osteoarthritis (Kellgren-Lawrence grade [1,4]), with an average error of 3%.

A personalized model of the human knee for enhancing the inter-individual reproducibility of a measurement method for monitoring Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) after transdermal delivery is proposed. The model is based on the solution of Maxwell Equations in the electric-quasi-stationary limit via Finite Element Analysis. The dimensions of the custom geometry are estimated on the basis of knee circumference at the patella, body mass index, and sex of each individual. An optimization algorithm allows to find out the electrical parameters of each subject by experimental impedance spectroscopy data. Muscular tissues were characterized anisotropically, by extracting Cole–Cole equation parameters from experimental data acquired with twofold excitation, both transversal and parallel to tissue fibers. A sensitivity and optimization analysis aiming at reducing computational burden in model customization achieved a worst-case reconstruction error lower than 5%. The personalized knee model and the optimization algorithm were validated in vivo by an experimental campaign on thirty volunteers, 67% healthy and 33% affected by knee osteoarthritis (Kellgren–Lawrence grade ranging in [1,4]), with an average error of 3%.