Om: Optimized Meditation

TL;DR

High-stress professions, such as military service, contribute to cognitive impairment, increased impulsivity, and long-term health risks, yet existing stress management techniques like self-guided meditation lack structure and real-time adaptability. Guided meditation is effective, but not always practical through a human.

Our AI-driven system personalizes meditation questioning sequences in real-time by integrating biometric wearables and visual analysis to assess physiological and cognitive responses. By dynamically adjusting meditation sessions and providing personalized wellness recommendations, our solution offers a scalable, effective, and data-driven approach to stress mitigation.

Inspiration / Problem Statement / What We Learned

We have seen firsthand the long-term consequences of high-stress environments. Veterans, first responders, and individuals in high-stakes professions often experience mental health disorders, PTSD, and substance use disorder (SUD) at disproportionately high rates. We wanted to explore an effective, preventative measure to mitigate the long-term impact of chronic stress before it manifests into severe mental health diagnoses.

The Effects of High-Stress Environments

High-stress professions lead to significant cognitive, emotional, and physiological challenges:

Cognitive Disruption – Stress reduces attention span by 30%, depleting working memory and impairing decision-making.
Risky Coping Mechanisms – Weakened prefrontal cortex (PFC) function increases impulsivity and SUD rates.
Physiological Impact – Heightened amygdala reactivity causes muscle tension, anxiety, and long-term health risks.

The Limitations of Self-Guided Meditation

While meditation is a proven tool for stress reduction, self-guided meditation is often ineffective in high-stress situations because it:

Lacks external structure, making engagement difficult.
Relies on personal discipline, leading to inconsistent practice.
Fails to override cognitive overload, rendering it ineffective for immediate relief.

As a result, self-guided meditation struggles to:

Actively regulate stress responses.
Enhance cognitive resilience.
Optimize decision-making in real-time.

The Power of Personalized Meditation

The U.S. Army’s STRONG Project found that personalized meditation can:

Reduce acute stress markers by 33% during survival training.
Improve working memory capacity in 72% of participants after four weeks.
Cut burnout-related medical discharges by 19% in high-deployment units.

The Problem with Human-Guided Meditation

Despite its benefits, human-guided meditation has scalability issues:

Limited Availability – Trained instructors are scarce.
High Cost – Widespread adoption is impractical.
Lack of Real-Time Adaptation – Traditional methods do not integrate biofeedback.
Inability to Personalize at Scale – Generic meditation techniques fail to address individual needs.

What it does

We developed an AI-driven system that personalizes meditation in real-time by integrating biometric data and visual analysis:

Biometric Wearable Data: Tracks heart rate variability, respiratory rate, electrodermal activity, blood volume pulse, and body temperature to assess physiological responses.

AI-Powered Visual Analysis: Uses video to monitor facial muscle tension, eye movement, head positioning, postural sway, and micro-expressions, providing insights into cognitive engagement and emotional states.

AI-Powered Personalization & Feedback:

Real-Time Effectiveness Analysis – AI evaluates biometric and visual markers to determine meditation efficacy. If the user remains at baseline levels, the questioning series will continue under our meditation framework. If an adverse signal is detected (ie increased HRV, increased body temperature, and increased eye tracking), the question series will further explore that area.
Personalized Guidance – Sessions adapt dynamically to individual stress levels and cognitive patterns.
Customized Wellness Recommendations – AI suggests relaxation strategies and wellness products like Ekkomi® teas based on user needs.

How we built it

To enhance accuracy and effectiveness, we:

Integrated biometric wearables and AI-powered visual analysis to assess user stress levels.
Used Perplexity AI to research and refine meditation personalization based on real-time physiological and cognitive patterns.
Applied machine learning for dynamic session adjustments and wellness recommendations based on meditation response patterns.

Challenges we ran into

Developing Mathematical Models for Facial Recognition – We wrote custom mathematical formulas to analyze facial expressions and movements:

Blink Detection – Used the eye aspect ratio (EAR) formula to determine eye closures.
Facial Tension Analysis – Normalized the eyebrow-eye distance to quantify muscle tension.
Head Movement Tracking – Measured facial center shifts to detect posture adjustments.
Micro-Expression Recognition – Applied differential eyebrow position analysis to identify subtle emotional changes.

Optimizing Real-Time Processing – Running API calls concurrently with audio playback in Streamlit required efficient request handling for Eleven Labs, Gemini, and Amazon S3 to minimize latency.

This integration of mathematical modeling and real-time processing ensures accurate, adaptive meditation guidance.

Accomplishments that we're proud of

Developed a multi-modal platform that leverages multiple data mediums to iterate in real time.
Reasoning and developing context over multiple prompts worked effectively.
Slept an average of 4 hours over 36 hours per team member.
Consumed 500 mg of caffeine and too many calories.

What's next for Om: Optimized Meditation

Deploy and test the platform on a team member's app, Novus link. Have access to 250+ users. Wedge into the market going B2B with companies that have existing meditation apps, and eventually deploy a B2C application.