G. Crotti, D. Giordano, P. S. Letizia, A. Delle Femine, M. Luiso
A simplified procedure for the Accurate Frequency Response Identification of Voltage Transformers

The paper proposes a simplified and affordable procedure for instrument transformers frequency characterization for medium voltage applications. It consists of a two steps measurement procedure, where both steps involve the generation of sine waves only, so that the it can be easily performed in instrument transformers calibration laboratories without requiring additional generation features. In the first step, a 50 Hz measurement of the Voltage Instrument Transformer ratio error at rated voltage is performed. The second step consists of a frequency sweep performed at low voltage up to the first resonance. By these two data sets, an approximated but accurate frequency response can be easily obtained using a non-linear fitted model. In the paper, a commercial voltage instrument transformer for medium voltage grids is characterized by the proposed method; results are shown and validated by comparison with a reference frequency characterization performed at INRIM under rated voltage.

A. Mingotti, D. Cavaliere, L. Peretto, R. Tinarelli
THD Uncertainty Estimation for Inductive Current Transformers Using the Accuracy Class Information

The Total Harmonic Distortion (THD) measurement is among the key elements to assess the power quality of a network. However, the Standards does not provide sufficient information to evaluate the uncertainty related to THD, neither voltage nor current one. Therefore, this work tackles the uncertainty estimation of the current THD measured by off-the-shelf inductive current transformers. The uncertainty computation is performed by a consolidated expression presented by the authors. Finally, the actual measurements of THD and the computation of its uncertainty are compared with those estimates with the presented expression.

Giuseppina Affinito, Raffaele Palladino, Maria Triassi, Pasquale Arpaia
Derivation and Calibration of a Cardiovascular Risk Score for Occupational Medicine

Cardiovascular disease (CVD) is one of the most prevalent causes of long-term sickness absence from work, and the working environment can contribute to its impact on workers. health. The aim of this study is to develop, validate, and compare scores to measure the risk of diagnosis of unsuitability for work. A cohort of workers employed by the municipalities of Naples was examined, as part of their scheduled occupational health surveillance, at the occupational health outpatient clinic of the Department of Public Health of the University of Naples Federico II between January 2006 and December 2016. Cox proportional hazards models were used in the cohort to derive risk equations for the assessment of the 10-year risk of diagnosis of unsuitability for work. Two models were developed: (i) Model A, including all the variables of the Multivariable Cox Model, and (ii) Model B, with only the variables statistically significant at the .05. The Akaikes Information Criterion was used to compare fit and performance of the different models in the derivation cohort. The above analysis proved that the model B was the better predictor of diagnosis of unsuitability for work and should be used as a clinical tool for the assessment of fitness for work in health surveillance.

Andrea Apicella, Pasquale Arpaia, Mirco Frosolone, Giovanni Improta, Francesco Isgrò, Nicola Moccaldi, Angela Natalizio
EEG-Based Attention Assessment in Motor-Rehabilitation

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.

Jan Saliga, Pavol Dolinsky, Imrich Andras, Linus Michaeli
A new CS method for ECG signal

Compressed sensing (CS) is a modern method reducing amount of transferred and stored data which have been applied for many types of signals fulfilling sparsity requirements. Electrocardiogram signals (ECG) is one of such signals. In this paper a completely patient-agnostic compressed sensing and reconstruction technique for ECG signals is proposed. A modified sensing method incorporating a QRS detector is used to guarantee the exact R wave positions in the reconstructed signal for any level of compression. For the signal reconstruction a novel method using a dynamic ECG signal model is described, in which the model parameters are found using the Differential Evolution optimization algorithm. Reconstruction quality is evaluated using the MIT-BIH arrhythmia database, and compared against wavelet dictionary reconstruction methods showing better reconstruction quality for compression ratios above 5.