# Proactive Emotion Tracker: AI-Driven Continuous Mood and Emotion Monitoring

arXiv: https://arxiv.org/abs/2401.13722 · HTML: https://mlsanity.com/paper/2401.13722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal](https://mlsanity.com/paper/2305.19379/) (2023)
- [Deep Fuzzy Framework for Emotion Recognition using EEG Signals and Emotion Representation in Type-2 Fuzzy VAD Space](https://mlsanity.com/paper/2401.07892/) (2024)

## Built on by (most cited first, 1 of 1)

- [Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2607.28687/) (2026)

## Abstract (arXiv)

This research project aims to tackle the growing mental health challenges in today's digital age. It employs a modified pre-trained BERT model to detect depressive text within social media and users' web browsing data, achieving an impressive 93% test accuracy. Simultaneously, the project aims to incorporate physiological signals from wearable devices, such as smartwatches and EEG sensors, to provide long-term tracking and prognosis of mood disorders and emotional states. This comprehensive approach holds promise for enhancing early detection of depression and advancing overall mental health outcomes.