The importance of physiological data variability in wearable devices for digital health applications

This paper aims at characterizing the variability of physiological data collected through a wearable device (Empatica E4), given that both intra- and inter-subject variability play a pivotal role in digital health applications, where Artificial Intelligence (AI) techniques have become popular. Inter-beat intervals (IBIs), ElectroDermal Activity (EDA) and Skin Temperature (SKT) signals have been considered and variability has been evaluated in terms of general statistics (mean and standard deviation) and coefficient of variation. Results show that both intra- and inter-subject variability values are significant, especially when considering those parameters describing how the signals vary over time. Moreover, EDA seems to be the signal characterized by the highest variability, followed by IBIs, contrary to SKT that results more stable. This variability could affect AI algorithms in classifying signals according to particular discriminants (e.g. emotions, daily activities, etc.), taking into account the dual role of variability: hindering a net distinction between classes, but also making algorithms more robust for deep learning purposes thanks to the consideration of a wide test population. Indeed, it is worthy to note that variability plays a fundamental role in the whole measurement chain, characterizing data reliability and impacting on the final results accuracy and consequently on decision-making processes

Assessment of Domestic Well-Being: From Perception to Measurement

Nowadays, there are plenty of sensing devices that enable the measurement of physiological, environmental, and behavioral parameters of people 24 hours a day, seven days a week and provide huge quantities of different data. Data and signals coming from sensing devices, installed in indoor or outdoor environments or often worn by the users, generate heterogeneous and complex structured datasets, most of the time not uniformly structured. The artificial intelligence (AI) algorithms applied to these sets of data have demonstrated capabilities to infer indices related to a subject's status and well-being [1]. Well-being is a key parameter in the World Health Organization (WHO) definition of health, considering its physical, mental, and social spheres. Quantitatively assessing a subject's well-being is of paramount importance if we want to assess the whole status of a person, which is particularly useful in the case of ageing people living alone. Assessment allows for continuous remote monitoring to improve people's quality of life (QoL) according to their perceptions, needs, and preferences. Technology undoubtedly plays a pivotal role in this regard, providing us new tools to support the objective evaluation of a subject's status, including her/his perception of the living environment. Its potential is huge, also in terms of support to the healthcare system and ageing people; however, there are several engineering challenges to consider, especially in terms of sensors integrability, connectivity, and metrological performance, in order to obtain reliable and accurate measurement systems

Metrological characterization of a therapeutic device for pressure wave therapy

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Electrical resistivity and electrical impedance measurement in mortar and concrete elements: A systematic review

This paper aims at analyzing the state-of-the-art techniques to measure electrical impedance (and, consequently, electrical resistivity) of mortar/concrete elements. Despite the validity of the concept being widely proven in the literature, a clear standard for this measurement is still missing. Different methods are described and discussed, highlighting pros and cons with respect to their performance, reliability, and degree of maturity. Both monitoring and inspection approaches are possible by using electrical resistivity measurements; since electrical resistivity is an important indicator of the health status of mortar/concrete, as it changes whenever phenomena modifying the conductivity of mortar/concrete (e.g., degradation or attacks by external agents) occur, this review aims to serve as a guide for those interested in this type of measurements

Methods for the metrological characterization of wearable devices for the measurement of physiological signals: state of the art and future challenges

Wearable 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

Metrological Characterization of Therapeutic Devices for Pressure Wave Therapy: Force, Energy Density, and Waveform Evaluation

Pressure 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

Wearable Devices and Diagnostic Apps: Beyond the Borders of Traditional Medicine, but What about Their Accuracy and Reliability?

Nowadays 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]