Healmind Ai: An Artificial Intelligence-based Stress Detection And Support Platform

• Author(s):

  Dr. S.S. Patil | Prof. S.S. Shinde | Sanika Ekshette | Jayashree Devkar | Payal Dhasade | Kalyani Sul

• Keywords:

  Multimodal Stress Detection, Neuro-Symbolic Hybrid Logic, Feature Fusion, Holistic Intervention, Mental Wellness.

• Abstract:

  The Modern Mental Health Management Tools Need Something More Sensitive To The Actual Condition Of A Person, Beyond What They Are Willing To Disclose Via Their Communication With The System. The Currently Existing Solutions, Like Wysa And Woebot, Are Exclusively Based On The Text Input. Therefore, A User Typing “I Am Fine” While Actually Being In Serious Trouble, Cannot Be Identified. To Improve That Situation, HealMind AI Was Created To Analyze Three Parallel Streams Of Information: The Facial Expression Analyzed By A Webcam, The Tone Of Voice Analyzed By A Microphone, And Text Generated Via Journaling Or During Chat. There Is A Neuro-Symbolic Hybrid Logic Engine Serving As A Backbone Of This Technology, Combining Machine Learning Algorithms With A Rule-Based System And Providing All Results Within The Scope Of Clinical Safety. With A Help Of A Feature Fusion Layer, It Is Possible To Resolve Contradictions Between Different Input Channels. Thus, When A User's Facial Expression Seems Troubled, But His/her Voice Seems Calm, The Level Of His/her Stress Is Classified As Moderate Instead Of Reaching The Extremes. After The Stress Is Detected, A Stress Dashboard Triggers A Search For Nearby Clinics Using The Google Maps API, Allowing Users To Find A Suitable Help In Allopathy, Homeopathy, Or Ayurveda. The Overall Efficiency Of The Combination Equals 88%, Which Is Much Better Than Any Of The Single Channel Solutions.

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Other Details

• Paper id:

  IJSARTV12I5105396

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-18

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