Towards an IoHT Platform to Monitor QoL Indicators

Abstract

The Quality of Life has been studied for a long time, and the World Health Organization defines it as the individual perception about life regarding four major domains: physical, psychological, social, and environmental. The relevance to study QoL lies in the search for strategies able to measure a patient’s well-being. Without these strategies, treatments, and technological solutions that aim to improve people’s QoL would be restricted to physicians’ implicit and subjective perceptions. Thus, there are many instruments for formal QoL assessment (usually questionnaires). However, the use of these instruments is time-consuming, non-transparent, and error-prone. Considering this problem, in this work, we discuss the proposal to use the Internet of Health Things (IoHT) to collect data from smart environments and apply machine learning techniques to infer QoL measures. To achieve this goal, we designed an IoHT platform inspired by the MAPE-K loop. Our literature review has shown that this idea is promising and that there are many open challenges to be addressed.