Real-time emotion-aware AI Counseling System with memory retention polymer composites

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 13 | 06 | Page :
    By

    M.Sowmiya,

  • K. Aakash,

  • G.A. Hariharan,

  • V. Chithambaram,

  • A.B Hajira Be,

  1. , Information Technology, Easwari Engineering College, Chennai, Tamil Nadu, India
  2. , Information Technology, Easwari Engineering College, Chennai, Tamil Nadu, India
  3. , Information Technology, Easwari Engineering College, Chennai, Tamil Nadu, India
  4. , Department of Physics, Dhanalakshmi College of Engineering, Tamil Nadu, India
  5. , Department of Computer Applications, Karpaga Vinayaga College of Engineering of Technology, Madhuranthagam, Chengalpattu, Tamil Nadu, India

Abstract

The availability of mental health services is still a major barrier, with many individuals constrained by financial limitations, social stigma, and a shortage of accessible counselors. This work introduces an emotion-aware AI counselor designed to provide empathetic and personalized emotional support via voice-based interfaces. The system leverages Natural Language Processing (NLP) and sentiment analysis to detect emotional cues from speech and generate contextually appropriate, comforting responses. A key innovation is the memory retention module, which enables the AI to recall previous conversations for a more individualized interaction. Additionally, Reinforcement Learning with Human Feedback (RLHF) helps refine responses over time. Speech-to-Text (STT) and Text-to-Speech (TTS) technologies ensure seamless communication and user-friendliness.To further broaden the application scope, this intelligent system is proposed as a supportive tool in high-stress environments such as composite material manufacturing plants and polymer-processing units, where workers are exposed to physically demanding and psychologically stressful conditions. The AI counselor can be embedded into safety helmets or smart workstations made from advanced polymer composites, offering on-the-job emotional support. The system also integrates deep learning models like BERT and RNNs, multimodal emotion detection from speech and text, and Retrieval-Augmented Generation (RAG) for contextual replies. Secure backend processing with Flask, personalized voice interaction through Tacotron2 or WaveNet, and features like dialogue continuity, feedback-driven learning, and cultural sensitivity highlight its potential. This positions the framework as a scalable, tech-enabled therapeutic aid to complement workplace wellness, particularly in the composites and advanced materials sectors.

Keywords: Emotion-aware AI, Sentiment Analysis, Mental Health Support, Memory Retention, Reinforcement Learning, Speech Recognition.

How to cite this article:
M.Sowmiya, K. Aakash, G.A. Hariharan, V. Chithambaram, A.B Hajira Be. Real-time emotion-aware AI Counseling System with memory retention polymer composites. Journal of Polymer and Composites. 2025; 13(06):-.
How to cite this URL:
M.Sowmiya, K. Aakash, G.A. Hariharan, V. Chithambaram, A.B Hajira Be. Real-time emotion-aware AI Counseling System with memory retention polymer composites. Journal of Polymer and Composites. 2025; 13(06):-. Available from: https://journals.stmjournals.com/jopc/article=2025/view=229497


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Ahead of Print Subscription Original Research
Volume 13
06
Received 21/07/2025
Accepted 26/08/2025
Published 24/10/2025
Publication Time 95 Days
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