A review of wearable devices application for mental health monitoring: A machine learning perspective

Mental health disorders represent a growing global health burden, contributing significantly to reduced quality of life, decreased productivity, and escalating healthcare costs. Recent advancements in wearable electronic devices (WEDs), and sensor technologies have created new opportunities for continuous, unobtrusive monitoring of physiological and behavioral indicators associated with mental health conditions. Concurrently, machine learning (ML) techniques have emerged as powerful tools for transforming raw multimodal wearable data into meaningful clinical insights. This review presents a comprehensive analysis of the integration of wearable technologies and ML for mental health monitoring. It provides an overview of wearable sensing technologies, and bio signal analysis that are commonly used for mental health assessment and discusses their relevance to autonomic nervous system responses linked to psychological states. The review further analyzes traditional and neural network based learning approaches, highlighting their roles in feature extraction, classification, and prediction of mental health conditions such as stress, depression, emotion dysregulation, bipolar disorder, and schizophrenia. The review concludes by identifying key research gaps and outlining future directions toward scalable, clinically reliable, and ethically responsible ML-enabled wearable systems for mental healthcare. Supplementary Material File (review on mental health monitoring_submit_final.docx) - Download - 3.04 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 146views 60downloads Citations Download citation Emmanuel Okere, Sudesh Sivarasu, Lisa-Dionne Morris. A review of wearable devices application for mental health monitoring: A machine learning perspective. Authorea. 30 March 2026. DOI: https://doi.org/10.22541/au.177485977.78677913/v1 DOI: https://doi.org/10.22541/au.177485977.78677913/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.