# Smart Devices and Multimodal Systems for Mental Health Monitoring: From Theory to Application

## TL;DR

Smart devices and AI are being used to monitor mental health, but more research is needed to make these tools reliable and widely applicable.

## Contribution

A systematic review of biosignal-based smart systems for mental health monitoring, highlighting methodological gaps and future directions.

## Key findings

- Multimodal biosignal systems show potential for mental health monitoring but are limited by small sample sizes and lack of validation.
- Common analytical methods include feature extraction and machine learning models like SVM and CNNs.
- Standardized reporting and ethical design are critical for translating these systems into clinical practice.

## Abstract

Smart devices and multimodal biosignal systems, including electroencephalography (EEG/MEG), ECG-derived heart rate variability (HRV), and electromyography (EMG), increasingly supported by artificial intelligence (AI), are being explored to improve the assessment and longitudinal monitoring of mental health conditions. Despite rapid growth, the available evidence remains heterogeneous, and clinical translation is limited by variability in acquisition protocols, analytical pipelines, and validation quality. This systematic review synthesizes current applications, signal-processing approaches, and methodological limitations of biosignal-based smart systems for mental health monitoring. Methods: A PRISMA 2020-guided systematic review was conducted across PubMed/MEDLINE, Scopus, the Web of Science Core Collection, IEEE Xplore, and the ACM Digital Library for studies published between 2013 and 2026. Eligible records reported human applications of wearable/smart devices or multimodal biosignals (e.g., EEG/MEG, ECG/HRV, EMG, EDA/GSR, and sleep/activity) for the detection, monitoring, or management of mental health outcomes. The reviewed literature after predefined inclusion/exclusion criteria clustered into six themes: depression detection and monitoring (37%), stress/anxiety management (18%), post-traumatic stress disorder (PTSD)/trauma (5%), technological innovations for monitoring (25%), brain-state-dependent stimulation/interventions (3%), and socioeconomic context (7%). Across modalities, common analytical pipelines included artifact suppression, feature extraction (time/frequency/nonlinear indices such as entropy and complexity), and machine learning/deep learning models (e.g., SVM, random forests, CNNs, and transformers) for classification or prediction. However, 67% of studies involved sample sizes below 100 participants, limited ecological validity, and lacked external validation; heterogeneity in protocols and outcomes constrained comparability. Conclusions: Overall, multimodal systems demonstrate strong potential to augment conventional mental health assessment, particularly via wearable cardiac metrics and passive sensing approaches, but current evidence is dominated by proof-of-concept studies. Future work should prioritize standardized reporting, rigorous validation in diverse real-world cohorts, transparent model evaluations, and ethics-by-design principles (privacy, fairness, and clinical governance) to support translation into practice.