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Wearable measurement systems for cardiorespiratory and biomechanical monitoring
No full textNegli ultimi dieci anni, le tecnologie indossabili sono diventate uno strumento fondamentale per il monitoraggio continuo delle funzioni fisiologiche e biomeccaniche umane. La loro crescente diffusione in ambito sanitario e sportivo riflette la necessità di sistemi non invasivi e discreti, in grado di fornire dati affidabili al di fuori di ambienti di laboratorio controllati.
Parametri cardiorespiratori quali la frequenza cardiaca (HR) e la frequenza respiratoria (BR) riflettono l’attività integrata dei sistemi cardiovascolare e respiratorio e i meccanismi autonomici che ne regolano l’adattamento alle richieste fisiologiche. I dispositivi wearable capaci di acquisire segnali elettrocardiografici (ECG) e respiratori consentono un monitoraggio continuo e in tempo reale nella vita quotidiana, supportando l’identificazione precoce di alterazioni fisiologiche e il follow-up a lungo termine.
Allo stesso modo, le unità di misura inerziali (IMU) rivestono un ruolo sempre più rilevante nell’analisi biomeccanica in ambito sportivo e quotidiano. La loro capacità di acquisire il movimento in condizioni reali permette di valutare le prestazioni, la qualità del movimento, l’affaticamento e il rischio di infortunio con elevata validità ecologica.
L’obiettivo di questa tesi è contribuire al progresso del monitoraggio wearable in due ambiti: fisiologia cardiorespiratoria e biomeccanica applicata allo sport.
Il primo filone di ricerca riguarda il monitoraggio fisiologico. Un dispositivo ECG wireless (WECG) è stato valutato in condizioni di riposo e dinamiche per caratterizzarne le prestazioni metrologiche e la capacità di estrarre HR e caratteristiche morfologiche clinicamente rilevanti. Successivamente, è stato progettato, realizzato e caratterizzato un sensore respiratorio basato su un composito stampato in 3D di poliuretano termoplastico e carbon black (CB-TPU), in grado di rilevare i movimenti toracici durante la respirazione. Sebbene studiati separatamente, questi dispositivi rappresentano passi fondamentali verso un sistema wearable integrato per la valutazione cardiopolmonare.
Il secondo filone di ricerca affronta il monitoraggio biomeccanico attraverso la caratterizzazione metrologica di un sistema wearable basato su IMU, integrato in un gilet sportivo e posizionato nella regione dorsale superiore (T1–T3). Due studi sperimentali hanno valutato la durata del passo e l’altezza del salto. Inoltre, sono stati sviluppati modelli di machine learning per classificare i livelli di attività e rilevare azioni specifiche dello sport, potenziando le capacità del sistema per il monitoraggio in campo.Over the past decade, wearable technology has become a key tool for the continuous monitoring of human physiological and biomechanical functions. Its growing adoption in healthcare and sports science reflects the demand for unobtrusive systems capable of providing reliable data outside controlled laboratory environments.
Cardiorespiratory parameters such as heart rate (HR) and breathing rate (BR) reflect the integrated activity of the cardiovascular and respiratory systems and their autonomic regulation. Wearable devices capable of acquiring electrocardiographic (ECG) and respiratory signals enable real-time and continuous monitoring in daily life, supporting early detection of physiological alterations and long-term health assessment.
Similarly, inertial measurement units (IMUs) play an increasingly important role in biomechanical analysis in both sports and daily contexts. Their ability to capture movement under real-world conditions allows the evaluation of performance, movement quality, fatigue, and injury risk with high ecological validity.
The aim of this thesis is to contribute to wearable monitoring in two domains: cardiorespiratory physiology and sports biomechanics.
The first research direction focuses on physiological monitoring. A wireless ECG device (WECG) was evaluated in resting and dynamic conditions to characterize its metrological performance and its ability to extract HR and clinically relevant morphological features. Subsequently, a respiratory sensor based on a 3D-printed composite of thermoplastic polyurethane and carbon black (CB-TPU) was designed, produced, and characterized for detecting chest movements during breathing. Although investigated separately, these devices represent foundational steps toward an integrated cardiopulmonary wearable system.
The second research direction addresses biomechanical monitoring through the metrological characterization of an IMU-based wearable system integrated into a sports vest and positioned on the upper back (T1–T3). Two experimental studies evaluated stride duration and jump height. Furthermore, machine learning models were developed to classify activity levels and detect sport-specific actions, enhancing the system’s capabilities for field-based sports monitoring
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
Metrological 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)
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
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
Metrological Characterization of a Jump Height Measurement Procedure Based on Inertial Sensors
No full textInertial measurement units (IMUs) have become increasingly valuable tools for monitoring human movements, offering compact and portable solutions for athletic performance analysis. This study aims to metrologically characterize the K-AI wearable device for measuring vertical jump height in professional athletes. The authors developed and validated an algorithm using the K-AI wearable IMU device (K-Sport, Fano, Italy) with data collected on non-athletes and professional basketball players. The jump height measurements obtained from the vertical acceleration of the IMU device were compared to a reference system using video analysis with the Nikon D7200 camera. While the study included primarily non-athletes, it also involved professional basketball players to evaluate the performance of the algorithm in real-world sports environments. The results indicate a mean residual difference of -0.055 m and a standard deviation of 0.068 m, with a strong positive correlation between the two devices (ρ=0.88). However, the Bland-Altman analysis highlights a linear systematic error that increases at greater jump height. This trend suggests that the IMU device estimates lower jump height values than the reference system. This limitation underscores the need for a calibration model to correct systematic deviations to improve the device applicability in the athletic performance assessment
Design and Experimental Validation of a Cardiac Simulator for Prosthetic Heart Valve Evaluation
No full textContinuous innovation in medical technologies is important to address the complexities of cardiovascular diseases. This research aimed to design and experimentally validate an innovative cardiac valve simulator specifically designed to evaluate prosthetic heart valve (PHV) behavior. The simulator incorporates 3D-printed left ventricle and aorta real models, providing a controlled environment to assess PHV s. A hydraulic circuit, connected to a linear actuator, simulates the dynamic environment of the systemic circulation. The cardiac simulator allows personalized testing based on individual anatomical and physiological characteristics. Lab VIEW-based control systems ensure controlled replication of cardiac parameters. Experimen-tal results, conducted at a 1: 1 scale under resting conditions (70 bpm, 70 ml stroke volume), demonstrate the simulator's ability to replicate physiological conditions, as evidenced by pressure and flow signals at varying heart rates. Frequency analysis confirms the consistency of experimental data with theoretical predictions. Moreover, a unique feature of the realized simulator is its compact design, reducing footprint and components for improved accessibilit
Metrological Characterization of a Wearable Device for the Assessment of Gait Parameters
No full textWearable devices are widely marketed for sports applications, particularly inertial measurement unit (IMU) sensors, which playa pivotal role in enhancing the athletes' performance. IMU sensors can capture gait dynamics, allowing real-time monitoring during training and competition. Positioning these sensors on the upper torso holds a distinctive advantage, as it allows researchers and coaches to attain accurate data regarding athletes' motion. This research focuses on the metrological analysis of the K-AI Wearable Tech device by K-Sport (Fano, Italy), with a specific emphasis on the stride duration. The study involves a comparison of the device measurement results with those obtained from single point pressure sensors (reference device, gold standard). The results demonstrate high measurement precision and accuracy. The distribution of measurement differences (i.e., residuals) shows a narrow spread with low mean value (approximately 0 s) and low standard deviation (0.01 s and 0.15 s for left and right feet, respectively). Bland-Altman plots confirm agreement with reference device and the Pearson's correlation coefficient shows a strong correlation (0.98 and 0.97 for left and right foot, respectively). Hence, the wearable device can be considered suitable for gait analysis in sport applications, where it results particularly comfortable for upper torso positioning, not interfering with the athlete's movements
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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