Loudness measurement by robust magnitude estimation

Published in: Proceedings of the 14th Joint International IMEKO TC1 + TC7 + TC 13 Symposium : "Intelligent quality measurements - theory, education and training" ; in conjunction with the 56th IWK, Ilmenau University of Technology and the 11th SpectroNet Collaboration Forum ; 31. August - 2. September 2011, JenTower Jena, Germany. - Ilmenau : Univ.-Bibliothek, ilmedia, 2011. URN: urn:nbn:de:gbv:ilm1-2011imeko:

Chest Wearable Apparatus for Cuffless Continuous Blood Pressure Measurements Based on PPG and PCG Signals

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Standardized nanomechanical atomic force microscopy procedure (SNAP) for measuring soft and biological samples

We present a procedure that allows a reliable determination of the elastic (Young's) modulus of soft samples, including living cells, by atomic force microscopy (AFM). The standardized nanomechanical AFM procedure (SNAP) ensures the precise adjustment of the AFM optical lever system, a prerequisite for all kinds of force spectroscopy methods, to obtain reliable values independent of the instrument, laboratory and operator. Measurements of soft hydrogel samples with a well-defined elastic modulus using different AFMs revealed that the uncertainties in the determination of the deflection sensitivity and subsequently cantilever's spring constant were the main sources of error. SNAP eliminates those errors by calculating the correct deflection sensitivity based on spring constants determined with a vibrometer. The procedure was validated within a large network of European laboratories by measuring the elastic properties of gels and living cells, showing that its application reduces the variability in elastic moduli of hydrogels down to 1%, and increased the consistency of living cells elasticity measurements by a factor of two. The high reproducibility of elasticity measurements provided by SNAP could improve significantly the applicability of cell mechanics as a quantitative marker to discriminate between cell types and conditions

Adherence to antibiotic treatment guidelines and outcomes in the hospitalized elderly with different types of pneumonia

Background: Few studies evaluated the clinical outcomes of Community Acquired Pneumonia (CAP), Hospital-Acquired Pneumonia (HAP) and Health Care-Associated Pneumonia (HCAP) in relation to the adherence of antibiotic treatment to the guidelines of the Infectious Diseases Society of America (IDSA) and the American Thoracic Society (ATS) in hospitalized elderly people (65 years or older). Methods: Data were obtained from REPOSI, a prospective registry held in 87 Italian internal medicine and geriatric wards. Patients with a diagnosis of pneumonia (ICD-9 480-487) or prescribed with an antibiotic for pneumonia as indication were selected. The empirical antibiotic regimen was defined to be adherent to guidelines if concordant with the treatment regimens recommended by IDSA/ATS for CAP, HAP, and HCAP. Outcomes were assessed by logistic regression models. Results: A diagnosis of pneumonia was made in 317 patients. Only 38.8% of them received an empirical antibiotic regimen that was adherent to guidelines. However, no significant association was found between adherence to guidelines and outcomes. Having HAP, older age, and higher CIRS severity index were the main factors associated with in-hospital mortality. Conclusions: The adherence to antibiotic treatment guidelines was poor, particularly for HAP and HCAP, suggesting the need for more adherence to the optimal management of antibiotics in the elderly with pneumonia

MEASUREMENT SET UP FOR THE EXPERIMENTAL STUDY OF THE DYNAMICS OF HOPPING

Abstract − Hopping is a gesture that, though simple, is apt to providing precious information on human-body dynamics, including dynamic stability. Currently available studies are normally based on a limited set of sensors and scarcely provide metrological details. This paper presents a new multi-sensors measurement set-up, including acceleration, force, angles and image-based positions. The metrological characteristics of the system are discussed, including systematic and random uncertainty contributions, measurement conditions and procedure. The same experimental apparatus may be used for studying other motion gestures, such as stepping down, forced hopping or cycling, thus providing a flexible experimental tool

LOUDNESS MEASUREMENT BY ROBUST MAGNITUDE ESTIMATION

none3Crenna F;Rossi Giovanni Battista; Bovio LucaCrenna, Francesco; Rossi, GIOVANNI BATTISTA; Bovio, Luc

Analisi ed interpretazione di misure di beta effettivo per la riduzione dell'incertezza sui principali dati nucleari relativi ai neutroni ritardati

Dottorato di ricerca in energetica. 7. ciclo. Tutore S. E. Corno e P. Ravetto. Coordinatori M. Cali'Consiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome; Biblioteca Nazionale Centrale - P.za Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Hybrid Convolutional Networks for End-to-End Event Detection in Concurrent PPG and PCG Signals Affected by Motion Artifacts

The accurate detection of physiologically-related events in photopletismographic (PPG) and phonocardiographic (PCG) signals, recorded by wearable sensors, is mandatory to perform the estimation of relevant cardiovascular parameters like the heart rate and the blood pressure. However, the measurement performed in uncontrolled conditions without clinical supervision leaves the detection quality particularly susceptible to noise and motion artifacts. This work proposes a new fully-automatic computational framework, based on convolutional networks, to identify and localize fiducial points in time as the foot, maximum slope and peak in PPG signal and the S1 sound in the PCG signal, both acquired by a custom chest sensor, described recently in the literature by our group. The event detection problem was reframed as a single hybrid regression-classification problem entailing a custom neural architecture to process sequentially the PPG and PCG signals. Tests were performed analysing four different acquisition conditions (rest, cycling, rest recovery and walking). Cross-validation results for the three PPG fiducial points showed identification accuracy greater than 93 % and localization error (RMSE) less than 10 ms. As expected, cycling and walking conditions provided worse results than rest and recovery, however reaching an accuracy greater than 90 % and a localization error less than 15 ms. Likewise, the identification and localization error for S1 sound were greater than 90 % and less than 25 ms. Overall, this study showcased the ability of the proposed technique to detect events with high accuracy not only for steady acquisitions but also during subject movements. We also showed that the proposed network outperformed traditional Shannon-energy-envelope method in the detection of S1 sound, reaching detection performance comparable to state of the art algorithms. Therefore, we argue that coupling chest sensors and deep learning processing techniques may disclose wearable devices to unobtrusively acquire health information, being less affected by noise and motion artifacts

Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks

Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task). Preliminary results in overnight monitoring showcased the ability to detect characteristic points in the four signals with a recall index of about 1.00, 0.90, 0.90, and 0.80, respectively. The accuracy of the signal quality classification was about 0.90, on average over four different classes. The average confidence of the correctly classified signals, against the misclassifications, was 0.93 vs. 0.52, proving the worthiness of the confidence index, which may better qualify the point identification. From the achieved outcomes, we point out that high-quality segmentation and classification are both ensured, which brings the use of a multi-modal framework, composed of wearable sensors and artificial intelligence, incrementally closer to clinical translation

Inadequate Macronutrient and Micronutrient Intakes in Hemodialysis and Peritoneal Dialysis Patients: Data from a Seven-Day Weighed Dietary Record

Background/aims: It is very important to assess the nutritional intake in patients on dialysis given the high prevalence of poor nutritional status of those in this population. The aim of this study was to assess nutrient intakes in hemodialysis (HD) and peritoneal dialysis (PD) patients. METHODS: A clinical cross-sectional study was conducted over 7 days on 14 dialysis patients (98 days) who were trained to keep a weighed food record and a 7-day food diary. Nutrient intake adequacy was compared with specific guidelines for Italians and dialysis patients. RESULTS: The mean daily protein intake (0.92 ± 0.36 g/kg) and energy intake (EI; 25.3 ± 7.4 kcal/kg) were inadequate according to the European best practice guidelines (EBPG). The ratio of EI to resting energy expenditure was 1.22. Inadequate intakes, compared to the EBPG, were found for calcium (525 ± 162 mg/day) and iron (8.7 ± 2.1 mg/day). Dietary fiber (14.7 ± 8.7 g/day), niacin (14.4 ± 5.2 mg/day), thiamine (0.8 ± 0.3 mg/day) and riboflavin (1.1 ± 0.4 mg/day) were also inadequate according to the Italian recommended dietary allowances (LARN). HD patients did not display different nutrient intakes between the dialysis days and the interdialytic period. Overall, the percentage of days during which nutrient recommendations were not satisfied ranged from 16 to 100% depending on the nutrient. CONCLUSION: Macronutrient and micronutrient intakes in HD and PD patients are largely inadequate compared to the EBPG. The weighed dietary record appears to be a useful and accurate tool for individual assessment of food intake in motivated patients. No nutrient intake differences were found between dialytic and interdialytic days in patients on HD