It has been largely demonstrated how human be-haviour can have a great impact on the quality of life. Some habits, such as those related to sleeping, exercising and working, directly affect people's psycho-physical health, either positively or negatively. The research has been increasingly focusing on better understanding how some behaviours, or changes in someone's usual habits, can be triggers to early recognising and predicting bad health conditions that might even be the warning signal of pathological conditions such as depressive disorders or neurodegenerative diseases. Non-invasive wearable health monitoring systems have the potential of being a key technology to this purpose, because they are easy-to-use and are not perceived as intrusive by users. In this paper, a system that makes use of popular commercial wrist-wearable devices to find the correlation between the monitored users' activities and their stress and well-being conditions, as subjectively self-assessed by them, is proposed. The paper aims to present a methodology to automatically learn which users' activities can be associated with positive and negative health conditions so that they can be later predicted as soon as the first signals are detected by wearable devices. The paper further presents the implementation and preliminary results of a first prototype of the proposed system, which monitors users' sleep and activity and assesses the correlation with stress levels and illness conditions.

Daily Activities Monitoring of Users for Well-Being and Stress Correlation Using Wearable Devices

Francesca Marcello
Primo
;
Virginia Pilloni
Secondo
2021

Abstract

It has been largely demonstrated how human be-haviour can have a great impact on the quality of life. Some habits, such as those related to sleeping, exercising and working, directly affect people's psycho-physical health, either positively or negatively. The research has been increasingly focusing on better understanding how some behaviours, or changes in someone's usual habits, can be triggers to early recognising and predicting bad health conditions that might even be the warning signal of pathological conditions such as depressive disorders or neurodegenerative diseases. Non-invasive wearable health monitoring systems have the potential of being a key technology to this purpose, because they are easy-to-use and are not perceived as intrusive by users. In this paper, a system that makes use of popular commercial wrist-wearable devices to find the correlation between the monitored users' activities and their stress and well-being conditions, as subjectively self-assessed by them, is proposed. The paper aims to present a methodology to automatically learn which users' activities can be associated with positive and negative health conditions so that they can be later predicted as soon as the first signals are detected by wearable devices. The paper further presents the implementation and preliminary results of a first prototype of the proposed system, which monitors users' sleep and activity and assesses the correlation with stress levels and illness conditions.
Wearable devices, Activity recognition, Stress, Illness, E-Health
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/331400
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