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This paper presents the Human-Centric Companion System (HCCS), an intelligent framework designed to enhance personal wellbeing through real time emotion recognition and stress analysis using Machine Learning. The system combines facial emotion detection via a CNN Bi-LSTM model and stress estimation through physiological sensor data (PPG and accelerometer signals). It integrates multimodal data using a Fast API-based backend and a React + Vite frontend interface. The model classifies emotional states such as happiness, sadness, fear, and anger, while computing a stress score derived from wearable signals. The solution provides a personalized dash- board for monitoring mental wellness, generating insights, and promoting emotional awareness. The proposed system achieves 85% accuracy in emotion recognition and 82% correlation between facial and physiological stress metrics, demonstrating its effectiveness in human centered wellbeing applications.
Keywords:
Emotion Recognition, Stress Detection, CNN- Bi LSTM, Machine Learning, Fast API, React, Physiological Signals, Human Wellbeing
Cite Article:
"Human-Centric Companion System for Enhancing Personal Wellbeing Using Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b990-b993, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604273.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator