Advanced International Journal for Research

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Emotiglow: A Hidden AI Based Wellness Guardian

Author(s) Dr. M. Sangeetha, Mr. K.Senthil Kumar, Thanseera S., Sujitha S., Ranithaa K. R., Subhaharini P.
Country India
Abstract This research EmotiGlow is a novel AI-powered wellness companion designed to gently track and support a user’s mental health by observing subtle patterns in mobile device interactions. Using the Adaptive Contextual Emotional Modulation (ACEM) algorithm, it interprets core behaviors such as touch micro-rhythms, interaction sequences, and content engagement, along with additional signals including scroll momentum, gesture timing, swipe paths, interaction randomness, attention shifts, micro-emotion triggers, and brief idle pauses. These insights are combined into a dynamic emotional score, which drives seven distinctive outputs: a glowing Mind Aura Ring, energy trails reflecting interaction patterns, an adaptive emotional orbit, context-aware Remedy Orbs, a live mood timeline, a Mind Mirror dashboard, and a self-regeneration mode for mood balancing. All feedback is delivered in real time, directly on the device, without requiring cameras, microphones, or wearable sensors. EmotiGlow transforms minute behavioral cues into actionable emotional insights, offering predictive guidance and fostering self-regulation. By keeping all computations on-device, it safeguards privacy while helping users understand and manage their emotions. This approach blends adaptive emotional inference with immersive visualization, offering a new, interactive paradigm in AI-assisted mental wellness.
Keywords Adaptive Contextual Emotional Modulation, Emotional Inference, Mobile Mental Wellness, Micro-Interaction Analysis, Real-Time Emotion Visualization, Privacy-Preserving AI, Interactive Well-Being Feedback
Field Computer Applications
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-03-31
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E-ISSN 3048-7641
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