Sheikh Mahsa, Qassem M, Kyriacou Panicos A
Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom.
Front Digit Health. 2021 Apr 7;3:662811. doi: 10.3389/fdgth.2021.662811. eCollection 2021.
Collecting and analyzing data from sensors embedded in the context of daily life has been widely employed for the monitoring of mental health. Variations in parameters such as movement, sleep duration, heart rate, electrocardiogram, 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, and 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 behavioral data and translate them into phenotypes and states related to mental health. Such systems aim to identify behaviors that are the consequence of an underlying physiological alteration, and hence, the raw sensor data are captured and converted into features that are used to define behavioral 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 intrapersonal and interpersonal differences need to be considered when interpreting the data. Altogether, combining practical mobile and wearable systems with the right data analysis algorithms can provide a useful tool for the 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.
收集和分析来自日常生活环境中嵌入的传感器的数据已被广泛用于心理健康监测。运动、睡眠时间、心率、心电图、皮肤温度等参数的变化通常与精神疾病有关。具体而言，加速度计数据、麦克风和通话记录可用于识别表明抑郁症状的语音特征和社交活动，心率和皮肤电导率等生理因素可用于检测压力和焦虑症。因此，已经开发了包括各种传感器的广泛设备来捕获这些生理和行为数据，并将它们转化为与心理健康相关的表型和状态。此类系统旨在识别作为潜在生理改变结果的行为，因此，原始传感器数据被捕获并转换为用于定义行为标记的特征，通常是通过机器学习。然而，由于被动数据的复杂性，这些关系并不简单，需要充分确立。此外，在解释数据时需要考虑诸如人际和人际差异等参数。总之，将实用的移动和可穿戴系统与正确的数据分析算法相结合，可以为精神障碍的监测和管理提供有用的工具。本综述旨在全面介绍并批判性地讨论所有可用的基于智能手机、可穿戴设备和环境传感器，以检测与最常见心理健康状况的治疗和/或管理相关的此类参数。
