Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring - Sheikh, Mahsa; Qassem, M.; Kyriacou, Panicos A. (Frontiers in Digital Health, 2021)
Collecting and analysing data from sensors embedded in the context of daily life has been widely employed for monitoring of mental health. Variations in parameters such as movement, sleep duration, heart rate, electrocardiogram (ECG), skin temperature etc., are often associated with psychiatric disorders. Namely, accelerometer data, microphone and call logs can be utilized to identify voice features and social activities indicative of depressive symptoms, whilst, physiological factors such as heart rate and skin conductance can be used to detect stress and anxiety disorders. Therefore, a wide range of devices comprising a variety of sensors have been developed to capture these physiological and behavioural data and translate them into phenotypes, and states related to mental health. Such systems aim to identify behaviours that are the consequent of an underlying physiological alteration, and hence the raw sensor data are captured and converted into features that are used to define behavioural markers, often through machine learning. However, due to the complexity of passive data, these relationships are not simple and need to be well established. Furthermore, parameters such as intra- and inter-personal differences need to be considered while interpreting the data. Altogether, combining practical mobile and wearable systems with the right data analysis algorithms can provide a useful tool for monitoring and management of mental disorders. The current review aims to comprehensively present and critically discuss all available smartphone-based, wearable, and environmental sensors for detecting such parameters in relation to the treatment and/or management of the most common mental health conditions.