Why the study?
Chronic anxiety and stress can cause physical and psychological illnesses, but identifying which Internet of Things approaches best monitor these emotions and assist people remains challenging.
This systematic review highlights that current IoT approaches for stress and anxiety monitoring predominantly rely on physiological data and decision trees, with a focus on detection rather than user assistance.
Emphasizes detection over assistance and stress over anxiety in IoT monitoring; extends taxonomies for future digital mental health studies.
Anxiety and stress are common emotional responses for human beings, but their chronic manifestation can lead to physical and psychological illnesses. The advancement of sensing technologies, such as Internet of Things, has contributed to the understanding and assisting events related to anxiety and stress. However, the main challenge is knowing which approaches can be used to better monitor these emotions and assist people. Based on a systematic literature review, this work analyzed studies both to determine how data is collected, and to monitor anxiety and stress levels. Two taxonomies synthesize the techniques mapped. The results indicated more emphasis on studying stress than anxiety and more focus on detecting anxiety and stress levels than on assisting the user. Among the main techniques to collect data, 62.5% of the studies used physiological data like heart data, and for data analysis techniques, 48% of the studies used Decision Trees.
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Paula et al. (2022) studied this question.
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