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.

Author Biography

Gloria Cosoli, Università Politecnica delle Marche

G. Cosoli was born in Chiaravalle (AN) in 1989. She received the M.S. degree in electronic engineering (cum laude) from the Università Politecnica delle Marche (UNIVPM) in 2013 and the Ph.D. degree in mechanical engineering from the same university in 2017.
From 2016 to date, she has been a Postdoctoral Research Fellow with the Department of Industrial Engineering and Mathematical Sciences (DIISM) of UNIVPM. 






Research Papers