How It Works
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    Download MUA
    in your VR & Mobile

    Create and login into the same account on both VR and mobile to sync across devices.

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    Measure and
    Collect
    Biometric Data in
    Real-time

    Real-time biometric data is collected through the mobile app and transmitted to the server.

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    Build
    User Dashboard and
    Recommend
    Meditation Content

    Check your biometric analysis results through dashboard in MUA XR. Based on your emotional state, MUA suggests the meditation that best suits for you.

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    Enhance
    User Dashboard
    through
    data Learning

    MIND-C AI learns from accumulated biometric data and pre and post changes through repeated use, allowing it to infer emotional states with greater precision.

Key Technology
Because your physical & emotional states are constantly changing,
The most suitable meditation experience must evolve.

  • Bio Data
    Collected via External Devices

    User Profile / HR / HRV / Blood Oxygen / SpO2 / Activity

  • Mind Data
    Assessed via User Questionnaire

    Emoji-based mood meter

Convert into basis for recommending meditation

  • Valence
  • Arousal

  • Tea Ceremony Meditation

    For highly stressed users

  • Space Meditation

    For users suffering from burnout

  • Body Scan Meditation

    For users who need inner stability

Backgrounds
XR meditation surpasses traditional methods in depth and stability,
significantly reducing the learning curve for beginners
89% of XR meditation participants felt disconnected from the outside world
XR meditation helps reduce distractions and enhances meditation effectiveness
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    • 90% said XR helped them reach deep meditation
    • Improved heart rate and HRV
    • Reduced time to enter deep meditation
    • Longer and better sleep than with traditional meditation

    From Dr. Gou Ge of Utrecht University in the Netherlands
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Backgrounds
Psychological Background of Emotion Measurement :
Arousal-Valence Model from James Russel
Based on James Russell’s two-dimensional circumplex model of emotion,
human emotions are viewed along continuous dimensions
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Valence : the degree of positive or negative emotion
Positive emotion (pleasant) ↔ Negative emotion (unpleasant)

Arousal : the level of excitement or activation
High arousal (excited, tense) ↔ Low arousal (calm)

Algorithm
Phase 1 : Biometric data + User response
Using smartwatch biometrics and user feedback, the algorithm study emotions and deliver personalized meditation.
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Phase 2 : Biometric data + AI learning
Leveraging the user’s basic model, MUA’s MIND-C AI estimates emotions from biometrics alone to deliver personalized meditation.
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Solution for individual user

Contact-Based Solutions

MUA works seamlessly with your existing smart watch.
Biometric signals measured by the watch are sent in real time to our servers, where they are transformed into indicators for emotional state estimation.
With repeated use, your own foundation model is built, enhancing meditation recommendations and enabling increasingly precise emotion estimation.

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Solution for group & business user

Contact-Free Solutions

MUA helps organizations maintain well-being and boost productivity.
Using a smart mirror, biometric signals are measured in a contactless manner and sent to MUA’s servers for emotional state analysis. Once the analysis is complete, MUA recommends the meditation most needed by the individual at that moment.
By analyzing data patterns across industries and groups, we provide optimized, specialized content and allow managers to track meditation outcomes through a dedicated dashboard—enabling effective mental health management.

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What More Can We Do?
we will expand the application of our technology to a wide range of genres,
including psychological therapy and marketing.

Powered by

MIND-C AI

AI-powered algorithm that interprets human bio signals
to accurately infer emotional states—a breakthrough technology redefining
how we understand and support mental well-being.

References
Psychological Background of Emotion Measurement :
Arousal-Valence Model from James Russel

Emotion recognition based on physiological signals using valence-arousal model(Basu, 2015)

Wearable-Based Affect Recognition—A Review(Schmidt, 2019)

WESAD, a multimodal dataset for wearable stress and affect detection (Schmidt, 2018)

Autonomic Nervous System Activity Distinguishes Among Emotions (Ekman, 1983)

Basic emotions are associated with distinct patterns of cardiorespiratory activity (Rainville, 2006)

Improving Real-Life Estimates of Emotion Based on Heart Rate: A Perspective on Taking Metabolic Heart Rate Into Account (Brouwer, 2018)

Physiological Pattern of Human Emotion State based on SPO2 sensor (Wibawa, 2016)

Emotion Recognition in Elderly Based on SpO2 and Pulse Rate Signals Using Support Vector Machine (Hakim, 2018)

Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors (Siirtola, 2023)

Heart rate as a measure of emotional arousal in evolutionary biology (Wascher, 2021)