1,721,023 research outputs found
Metrological characterization of a therapeutic device for pressure wave therapy
No full textThis study aims to characterize an electromedical device used for pressure wave therapy delivered by shock waves. The test protocol analyses different pressures and evaluates both the tip displacement, by means of a laser Doppler vibrometer, and the transmitted force, by means of a piezoelectric load cell; a silicone rubber was used as a tissue phantom. Finally, the provided energy density in terms of J/m 2 was computed. Results show variability in the tip displacement values (up to 15%), particularly at the lower working pressure values. It is also possible to note that the higher is the value of the pressure created by means of the solenoid valve, the higher is the force transmitted to the tissues (i.e. hundreds of N). Also the force data are affected by a certain degree of variability (up to 18%). Such study allows to better understand the effective force delivered to the tissues and to optimise the energy density provided to the different patient's districts, specifically at high pressures (i.e. ≥3 bar; 300 kPa) and on soft tissues (e.g. skin and connective tissue) where the energy densities can reach the limits indicated in DIGEST and ISTMT guidelines (i.e. 300 J/m 2 ). Consequently, it is important that the operators of such machines carefully evaluate the machine operating settings in order to maximise the benefits
Wearables for health and fitness: Measurement characteristics and accuracy
No full textTo date, wearable devices are increasingly widespread among different kinds of users (e.g. sport people, patients, but also young and adult people in general) to monitor physiological parameters (e.g. heart rate, skin temperature, steps and energy expenditure), depicting the subject's physical activity pattern during different activities. Nonetheless, an insufficient attention is paid to the measurement characteristics and the accuracy of these devices, which, on the contrary, are of utmost importance, especially if the wearable is used for health or fitness purposes since uncertainty affects the result itself. The aim of this work is to analyze the performances of wearable devices and the measurement procedure used to validate them with respect to gold standard instruments, such as electrocardiogram for cardiac parameters or calorimetry for energy expenditure. What clearly appears is the lack of a standard test protocol in the validation process, as well as a large variability in the expression of metrological characteristics of this class of measurement device (e.g. referring to accuracy, some authors use bias, others absolute error). Therefore, in the field of wearable devices research it would be fundamental to identify some common measurement parameters and to devise a kind of guidelines, in order to obtain repeatable and inter-comparable data that can be reliably compared
Heart Rate Variability Analysis with Wearable Devices: Influence of Artifact Correction Method on Classification Accuracy for Emotion Recognition
No full textHeart Rate Variability (HRV) analysis is widely explored in several application fields, such as emotion recognition. Photoplethysmographic (PPG) signals are often considered for this analysis because of their large use in wearable devices. However, quality of these signals (in terms of added disturbances) could be not always optimal, since they are susceptible to many factors, e.g. motion artifacts, ambient light, pressure of contact, skin color and conditions. Therefore, methods for artifacts correction play a pivotal role and consequently influence the results. This paper aims at proposing a new data artifacts correction method to improve the classification performance in emotion recognition, considering PPG signals during audio stimulation, and a Support Vector Machine (SVM) classifier. Results show that the proposed method provides a better classification in stimuli detection (66.67%) with respect to data pre-processing performed with a standard tool (Kubios, 48.81%); however, for further improvement, other signals could be considered in combination with PPG, such as the electrodermal activity (EDA)
Wearable Devices and Diagnostic Apps: Beyond the Borders of Traditional Medicine, but What about Their Accuracy and Reliability?
No full textNowadays people are willing to self-monitor their health status, and when they do not feel well, they tend to ask Dr. Google for a diagnosis (over a third of adults go online to analyze or look for information about a health condition [1]). People trust technology, often more than physicians; smartphone and Artificial Intelligence (AI) technologies are undoubtedly making innovative monitoring and diagnostic devices rapidly progress, so much that it seems that the future of medicine is in smartphones, where apps may run and to which devices can be connected, hence supporting mobile health (m-Health) [2]. In addition to smartwatches and wrist-worn devices that are surely the most common wearable devices [3], [4], there are also connected wearable clothes [5], socks [6], rings, or glasses-type wearables [7]
Metrological Characterization of Therapeutic Devices for Pressure Wave Therapy: Force, Energy Density, and Waveform Evaluation
Full text linkPressure wave therapy is widespread for multiple purposes, from cell metabolism stimulation to tendons, ligaments, muscles, and bones pathologies treatment. However, in the literature, there are no quantitative metrological data related to pressure wave devices. On the contrary, it would be extremely important to have more information on the provided therapeutic signal, which could also be exploited as input for a finite-element model able to foresee the pressure wave propagation inside the tissues. The authors investigated three different versions of the same device in terms of force applied to the tissue. The results show high variability of the pulses intensities (up to 25%), highlighting a nonuniformity of the treatment (in particular at low frequencies and high compressed air pressure). Moreover, the dependence from different parameters (i.e., pulse frequency, pressure, opening time of the solenoid valve for the compressed air pushing the bullet) was investigated. It was found that the lower the frequency and the higher the opening time of the valve, the higher the force applied to the tissue. An estimation of energy density was done; sometimes the limit values provided by pressure wave therapy guidelines (i.e., DIGEST and ISMST) are exceeded, in particular for soft tissues
Indirect Estimation of Breathing Rate through Wearable Devices
No full textBreathing rate (BR) represents one of the most important physiological parameters to be measured in clinical environment, being linked to multiple stressors and hence describing the global well-being state of a subject. During the last years, both contact and non-contact measurement methods for the assessment of BR have been proposed, particularly for ambulatory remote use. Given the wide spreading of wearable devices, several indirect estimation methods from heart rate (HR) data series have been proposed, avoiding standard procedures that can be often invasive or intrusive. This study aims at estimating the measurement accuracy and precision of BR when indirectly estimated from the HR series gathered by wearable devices (i.e. wrist-worn and chest-strap sensors). Volunteer subjects were tested both in natural breathing conditions and making them inhaling and exhaling at determined frequencies. The estimated BR values were compared with the reference ones and the accuracy and the precision of the measurement were evaluated through standard techniques (i.e. analysis of deviations, evaluation of agreement and correlation analysis). The study shows that better results are obtained with chest-strap sensors thanks to the higher accuracy of HR data: mean deviation of -0.24 bpm and -0.40 bpm for BioHarness 3.0 and Polar H10, respectively, with respect to the mean deviation of 2.89 bpm reported for Garmin Venu Sq for accuracy; standard deviation of 1.36 bpm and 2.92 bpm for BioHarness 3.0 and Polar H10, respectively, with respect to the mean deviation of 4.74 bpm reported for Garmin Venu Sq for precision. It is also noted that better BR accuracy and precision can be obtained by dedicated signal processing
Methods for the metrological characterization of wearable devices for the measurement of physiological signals: state of the art and future challenges
No full textWearable devices are rapidly spreading in many different application fields and with diverse measurement accuracy targets. However, data on their metrological characterization are very often missing or obtained with non-standardized methods, hence resulting in barely comparable results. The aim of this review paper is to discuss the existing methods for the metrological characterization of wearable sensors exploited for the measurement of physiological signals, highlighting the room for research still available in this field. Furthermore, as a case study, the authors report a customized method they have tuned for the validation of wireless electrocardiographic monitors. The literature provides a plethora of test/validation procedures, but there is no shared consensus on test parameters (e.g. test population size, test protocol, output parameters of validation procedure, etc.); on the other hand, manufacturers rarely provide measurement accuracy values and, even when they do, the test protocol and data processing pipelines are generally not disclosed. Given the increasing interest and demand of wearable sensors also for medical and diagnostic purposes, the metrological performance of such devices should be always considered, to be able to adequately interpret the results and always deliver them associated with the related measurement accuracy. • The sensor metrological performance should be always properly considered. • There are no standard methods for wearable sensors metrological characterization. • It is important to define rigorous test protocols, easily tunable for specific target applications
Features extraction from cardiac-related signals: comparison among different measurement methods
Full text linkHeart Rate (HR), Heart Rate Variability (HRV), and cardiac time intervals are clinically relevant parameters, which can be assessed from the analysis of electrocardiogram (ECG). Some aspects of cardiac activity can be investigated also by means of different noninvasive and non-intrusive measurement methods, such as phonocardiograph (PCG), photoplethysmograph (PPG), and vibrocardiograph (VCG). However, the standard processing algorithms (i.e., Pan & Tompkins) do not allow to fully characterize waveforms different from ECG. In the past, some of the authors have already demonstrated the efficiency of a novel processing procedure for the precise HR measurement from the above-mentioned signals. In the present work, data processing procedure has been improved and deeply extended to assess HRV parameters and time intervals from all the signals acquired on an extended experimental campaign, involving 26 subjects, on whom ECG, PPG, PCG, and VCG signals were simultaneously measured. Results prove that this approach can overcome the drawbacks of standard algorithms and can be widely applied to signals of different nature to derive HR, HRV, and time intervals. As regards HR measurement, PPG proved to be the most accurate measurement method (±1.2 bpm), followed by VCG (±1.6 bpm) and PCG (±2.5 bpm). For HRV analysis in the time domain, the use of the proposed methodology allows to obtain clinically relevant parameters statistically comparable to the ECG ones. Finally, the measurement of QT interval by applying personal calibration lines allows to obtain results comparable to the gold standard technique, i.e., ECG (maximum percentage deviation reduced from 10.9% up to <4.3% in VCG)
Impact of Wearable Measurement Properties and Data Quality on ADLs Classification Accuracy
Full text linkIn the field of automatic recognition and classification of Activities of Daily Living (ADLs), a paramount role to determine the classification accuracy is played by sensor technologies, as the algorithms’ performance is highly affected by the nature and quality of the collected measurement data. This work aims to investigate the influence of the wearable device characteristics and measurement uncertainty on the classification accuracy. For this study, two wearables devices are considered: a top-quality smartwatch (Empatica E4) and a low-cost Arduino-based wristband prototype. These devices have been used to measure the acceleration signal at the dominant wrist of subjects performing some relevant activities in real-life conditions. The experimental evaluation of some ADLs classification algorithms shows that their accuracy fluctuates depending on the choice of the sensor, which in turn affects the amount and type of relevant features to process. As such, the combination of features’ domain, i.e. time or frequency, number and type, which leads to the best classification accuracy has to be tuned on a specific sensor basis, despite the same type of signal, i.e. acceleration, is measured and processed under identical circumstances. Accuracy values of 50-99% and 66-95% in the ADLs classification, are obtained for Empatica E4 and Arduino-based prototype, respectively; the best performance among classifiers is obtained with J48 and Random Forest, confirming that, with an appropriate configuration, satisfactory accuracy may be attained, even by resorting to the use of simple sensors
Metrological characterizationof commercial smartwatches: are these sensors suitable for the assessment of well-being?
No full textToday, an increasing number of wearable devices is equipped with biomedical sensors for monitoring physiological parameters, such as temperature, heart rate (HR), oxygen saturation (SpO 2 ), and blood pressure (BP). Wearable devices offer accurate and continuous data on essential physiological parameters, enabling improved quality of care and more proactive and personalized approaches to healthcare by assessing the subject’s overall well-being in living environments. However, it is crucial to pay attention to the accuracy and precision of the data provided by these devices, particularly when collecting physiological parameters, as their metrological validation is often inadequate despite their widespread use. It is essential to determine the impact of measurement errors on healthcare decision-making and on the development of personal comfort models (PCMs) in a view of improving well-being and quality of life. This study aims to assess the metrological performance of a commercial smartwatch, determining the accuracy and precision of heart rate and blood pressure measurements, given their relevance for well-being assessment. A test population of 20 healthy subjects was enrolled in the test. Results show that that HR and BP can be tracked, with certain precision and accuracy, using a wrist-worn wearable device, evaluated with respect to a medical grade sphygmomanometer. The bias at measuring HR was of 0.2 bpm with a confidence interval of CI95% = [−6.2, 6.6] bpm. Confidence intervals of [−4.6, 7.6] mmHg and [−9.7, 10.9] mmHg were obtained for the diastolic and systolic BP, respectively
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