mlsanityData to 14 Oct 2026

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

arXiv 2401.13722 ↗First posted 24 Jan 2024cs.HC cs.AIcited by 1

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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.

Key facts. Cited by 1 paper 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

2 of 11 references.

References · 2

  1. 2023Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signalcited by 3
  2. 2024Deep Fuzzy Framework for Emotion Recognition using EEG Signals and Emotion Representation in Type-2 Fuzzy VAD Spacecited by 2

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Citing papers per year: 2026: 1.

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