1,721,025 research outputs found
Study and Development of a Novel Radio Frequency Electromedical Device for the Treatment of Peri-Implantitis: Experimental Performance Analysis, Modelling of the Electromagnetic Interaction with Tissues and In Vitro and In Vivo Evaluation
Full text linkLa peri-implantite (PI) è una grave patologia che interessa tessuti peri-implantari molli e duri. Ad oggi, la prevenzione è l’unico mezzo per contrastarla.
Recentemente, è stata sperimentata una terapia basata sulla somministrazione di corrente elettrica a radio frequenza (successo: 81%). Il trattamento è stato simulato numericamente, fornendo le distribuzioni di corrente (EC) e campo elettrico (EF) nei tessuti: l’effetto anti-infiammatorio è attribuibile alla EC, quello di rigenerazione ossea al EF.
Sono state considerate le misure di bioimpedenza (BM) per individuare le infiammazioni; numericamente si sono osservati cambiamenti nel modulo di impedenza del 4-20% (secondo diversi parametri), anche più alti sperimentalmente (35% infiammazione, 56% PI). Le BM permettono quindi di identificare il tessuto da trattare.
Per la ripetibilità, sono state considerate radici di denti naturali, numericamente e sperimentalmente; l’ordine di grandezza è lo stesso (qualche kΩ), anche se ci sono differenze legate alle condizioni di misura. La variabilità intra-soggetto è il 10% in uno stesso giorno, fino al 26% in giorni diversi; quella inter-soggetto è più alta.
La sicurezza elettrica è stata attentamente esaminata e si sono individuate le direttive applicabili (IEC 60601-1, 60601-1-2 and 60601-2-2).
Sono stati fatti test in vitro per valutare l’effetto della terapia sulla vitalità cellulare: non c’è un significativo aumento della necrosi (vitalità: 85% test, 94% controlli), l’effetto principale è l’apoptosi.
Sono stati numericamente indagati possibili effetti termici: non sono stati individuati riscaldamenti nocivi dei tessuti.
Si è progettato un nuovo dispositivo (PeriCare®) per trattare la PI, con parti diagnostica (BM) e terapeutica. Si stanno progettando elettrodi specifici e realizzando il prototipo. Si sta compilando il fascicolo tecnico e pianificando i test di conformità, in vista della certificazione. Il dispositivo medico dovrebbe entrare nel mercato il prossimo anno
Editorial: Wearable sensors for the measurement of physiological signals: what about their measurement uncertainty?
Full text linkWrist-worn and chest-strap wearable devices: Systematic review on accuracy and metrological characteristics
Full text linkThis paper analyses the state of the art on accuracy and metrological characteristics of wrist-worn and chest-strap wearable devices, in comparison with reference instruments. Basing on literature available results, neither a standard protocol for validation nor fixed metrological characteristics can be identified. Wearable devices are validated without standard procedures (test protocol, population characteristics and metrological parameters), which turns into irregular results, barely comparable each other. Therefore, it would be extremely interesting to conduct a pilot study to identify standard characteristics to evaluate accuracy, compliant to the guidelines for the expression of uncertainty in measurement and recognized by organizations promoting public health (e.g. the Food and Drug Administration in the United States). This way, it would be possible to start establishing a database of wearable devices’ metrological properties, useful not only for research, but also for caregivers and sportsmen, in different application fields (e.g. sport, medicine, Active and Assisted Living, etc.)
Combined use of wearable devices and Machine Learning for the measurement of thermal sensation in indoor environments
No full textMetrological Evaluation of Wearable ECG Systems: Heart Rate Estimation and PQRST Waveform Analysis
No full textWearable devices with electrocardiographic (ECG) sensors offer a strong and practical alternative to clinical systems monitoring physiological parameters. Identifying key ECG waveform points is essential for extracting cardiac features (e.g., PR intervals, QRS duration, QT intervals) and understanding cardiac function. This study evaluates heart rate (HR) estimation accuracy and precision using the Zephyr BioHarness 3.0 (reference device) and a 12-lead wireless ECG (test device) during rest and treadmill walking. It also develops an algorithm to detect PQRST wave points for extracting ECG features during various conditions (rest, walking, inclined walking, recovery). Compared to the BioHarness, the test device demonstrated high agreement in HR estimation (mean ± standard deviation = 0.11 bpm ± 2.04 bpm, p = 0.99), with minimal error in resting conditions. However, motion artifacts introduced variability, particularly during walking and inclined walking (walking MAE: 1.47 bpm ± 2.07 bpm; inclined walking MAE: 2.09 bpm ± 5.78 bpm; mean ± standard). Feature extraction analysis revealed increased errors in QRS and QT interval detection under dynamic conditions. In contrast, P-wave and PR interval related residuals were lower under dynamic conditions (inclined walking P-wave MAE: 18.22 ms ± 14.16 ms; PR interval MAE: 28.58 ms ± 22.66 ms; mean ± standard)
The Indirect Estimation of Breathing Rate through Wearables: Experimental Study and Uncertainty Analysis through Monte Carlo Simulation
Full text linkBreathing Rate (BR) is a fundamental physiological parameter and wearable sensors can indirectly estimate it through the measurement of electrocardiogram (ECG). Indeed, they are widely employed in several application fields thanks to their multiple advantages, such as user- friendliness, availability in different quality and cost segments, and capability to acquire multidomain physiological signals. This study aims at applying an approach based on respiratory sinus arrhythmia to the ECG signals acquired by a cardiac belt (Zephyr BioHarness 3.0) and a smartwatch (Samsung Galaxy Watch3), evaluating the measurement accuracy as well as performing a Monte Carlo simulation to analyze the uncertainty propagation along the measurement chain, from the wearable sensors to the estimated BR value. The results show that both the wearable sensors provide an accurate estimation of BR (almost null bias), with good precision (standard deviation of residuals: 3 bpm for both sensors), and moderate-high correlation with reference values (Pearson's correlation coefficient: 0.77 for Zephyr BioHarness 3.0 and 0.63 for Samsung Galaxy Watch3). Considering an uncertainty of ±1 bpm and ±2 bpm on heart rate for Zephyr BioHarness 3.0 and Samsung Galaxy Watch3, respectively, the Monte Carlo simulation provided expanded uncertainty values on the estimated BR of ±6 bpm and ±8 bpm, respectively, evidencing a relevant impact of physiological variability
Indirect Estimation of Breathing Rate Using Wearable Devices
Full text linkWearable sensors can be exploited for the indirect estimation of physiological parameters, such as breathing rate (BR). Indeed, BR is a significative quantity for both general health status monitoring and diagnostic purposes; however, standard methods for its assessment are often uncomfortable and mainly used for punctual (or brief, anyway) measurements. This article aims to perform an uncertainty analysis of BR indirect estimation made starting from electrocardiographic signals gathered through wearable sensors, namely, a cardiac belt (Zephyr BioHarness 3.0) and a smartwatch (Samsung Galaxy Watch3). Three different estimation methods were employed, considering respiratory sinus arrhythmia (RSA), signal amplitude modulation (AM), and machine learning (ML)-based techniques. Finally, the Monte Carlo simulation method was exploited for the measurement uncertainty estimation, including both sensors (hardware) and algorithms (software) contributions in the measurement chain. The results show that both the considered sensors are quite accurate (almost null bias) and precise (±[3, 5] bpm, depending on the estimation method) in the estimation of BR with the three different estimation algorithms. A slightly higher precision is obtained for the cardiac belt (a reduced 95% confidence interval is reported, with a maximum reduction of 4 bpm depending on the estimation algorithm), whose results are also more strongly correlated to the reference ones (Pearson’s correlation coefficient ≥0.75 in all the three methods). The Monte Carlo simulation evidenced that the ML-based method is the most robust with respect to the sensors’ uncertainty (with no differences in the output uncertainty with respect to the sensors’ uncertainty in input); moreover, the higher precision of the cardiac belt with respect to the smartwatch was confirmed (−1 bpm in the output uncertainty) if RSA- and AM-based methods are considered
A Method for Detecting Key Fiducial Points in Electrocardiographic Signals for Wave Characterization and HRV Analysis
No full textThe analysis of physiological signals is fundamental in fields such as healthcare and sports science, while cardiovascular disease remains a significant global health challenge. This study presents a method for detecting key fiducial points in electrocardiographic (ECG) signals. ECG signals were acquired using the Zephyr BioHarness 3.0 (reference device) and a new wireless ECG device (test device) to conduct the study. Measurements, including wave amplitude and duration, were obtained by identifying these points in the averaged waveform of each ECG signal. Hence, features such as P-wave, QRS complex, T-wave and their relative intervals were extracted from ECG signals provided by both devices. In addition, a heart rate variability (HRV) analysis was conducted, which provides additional information about cardiac health. HRV was analyzed in both time and frequency domains. The results demonstrate the reliability of both devices in identifying significant ECG features, with only minor variations in specific parameters. Notably, the QRS complex shows biases between 0 to 20 ms with percentage differences up to 30%, while the PR interval exhibits biases from 2 to 22 ms and percentage differences up to 33%. The HRV analysis shows strong agreement between the two devices. The study also highlights that both devices consistently measure heart rate (HR) (Pearson’s correlation coefficient: 0.88), further validating their accuracy and reliability for clinical and remote monitoring applications. These findings suggest that both devices are suitable for clinical and remote monitoring. Integrating these advanced ECG analysis methods could significantly improve patient monitoring and outcomes in both clinical and non-clinical environments
Wireless ECG and cardiac monitoring systems: State of the art, available commercial devices and useful electronic components
No full textWireless ElectroCardioGram (ECG) systems are employed in manifold application fields: tele-monitoring, sport applications, support to ageing people at home, fetal ECG, wearable devices and ambulatory monitoring. The presence of cables often hinders user’s free movements, alongside clinicians’ routine operations. Therefore, wireless ECG systems are desirable. This paper aims at reviewing the solutions described in the literature, besides commercially available devices and electronic components useful to setup laboratory prototypes. Several systems have been developed, different in terms of the adopted technology; when approaching the development of a wireless ECG system, some important aspects should be considered: electrodes (disposable, wet/dry, without contact, insulated), analog front-end, data acquisition systems (including amplifiers, multiplexer), wireless transmission technology (e.g. WiFi, Bluetooth) and power consumption (battery lifetime, miniaturization purposes). Technological advancements and continuous research have already brought to miniaturized and comfortable devices, but there is still room for improvement on multiple sides
Wearable devices and Machine Learning algorithms to assess indoor thermal sensation: metrological analysis
Full text linkPersonal comfort modeling is considered the most promising solution for indoor thermal comfort management in buildings. The use of wearable sensors is investigated for the real-time measurement of physiological signals to train comfort models for buildings monitoring and control. To achieve the required reliability, different uncertainty sources should be considered and weighted in the measurement results evaluation. This study presents an example of personal comfort model (PCM) development based on wearable sensors (i.e., Empatica E4 smartband and MUSE headband) acquiring multimodal signals (i.e., photoplethysmographic – PPG, electrodermal activity – EDA, skin temperature – SKT, and electroencephalographic – EEG ones), together with a metrological characterization of the modeling procedure. Starting from the data collected within an experimental campaign on 76 subjects, different Machine Learning (ML) algorithms were exploited to create comfort models capable of predicting the human thermal sensation (TS). The most accurate model was considered to investigate the impact of sensors uncertainty through a Monte Carlo simulation. Results showed that the Random Forest model is the best performing one (accuracy: 0.86). Monte Carlo simulation method proved that the model is very robust towards measurement uncertainties of input features (expanded uncertainty of the model accuracy: ± 0.04, k = 2). This confirms the possibility to derive the subject’s TS exploiting only physiological signals; measurement uncertainty is influenced mostly by PPG and EDA signals. This kind of investigation could lead to the development of PCMs, exploitable within control systems to optimize subjects’ well-being and building energy efficiency
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