############################# 🧠 Neuronest

AI-Powered Biofeedback & Cognitive Assistance Platform
Wearable hardware meets real-time intelligence.

Changelog | Architecture | Roadmap | Report Issue | Join Discord

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🚀 Introduction

Neuronest is a full-stack intelligent wearable system designed to monitor cognitive focus and physiological stability in real time.

It integrates embedded hardware, Bluetooth Low Energy communication, advanced signal filtering, and AI-driven cognitive coaching into a scalable architecture.

This is not just an app — it is a modular intelligence platform.

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🎯 Problem Statement

Most wearable devices collect physiological data — but do not interpret it intelligently.

Neuronest bridges the gap between:

📡 Sensor Data → 🧠 Signal Processing → 🤖 AI Interpretation → 🔔 Adaptive Feedback

Instead of passive tracking, Neuronest delivers actionable cognitive assistance.

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🧠 Core System Layers

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1️⃣ Hardware Layer

Components

• ESP32 WROOM-32
• MAX30105 IR Pulse Sensor
• MPU6050 Motion Sensor
• PWM Vibration Motor

Responsibilities

• Real-time sampling
• Signal filtering (noise reduction & smoothing)
• Focus state classification
• BLE packet transmission

Focus States:

• NO_FINGER
• FOCUSED
• NOT_FOCUSED

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2️⃣ Bluetooth Communication Layer

Built with CoreBluetooth.

Features:

• Auto BLE scanning
• Peripheral discovery
• Characteristic subscription
• Motor control write characteristic
• Real-time payload streaming

Designed for low-latency communication (<50ms typical).

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3️⃣ Application Layer (SwiftUI + MVVM)

Core Components:

• BLEFocusManager
• FocusPayload
• NeuronestAIViewModel
• ThemeStore
• HomeView
• FocusDashView

Responsibilities:

• State management
• Focus visualization
• Live telemetry
• Motor triggering interface
• AI coaching display

Architecture: MVVM for clear separation between logic and UI.

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4️⃣ AI Intelligence Layer

AI engine generates structured cognitive guidance.

Responsibilities:

• Interpret focus states
• Generate contextual coaching
• Maintain safety boundaries
• Produce structured responses

Designed for future expansion into:

• Personalized models
• Session memory
• Longitudinal pattern detection

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🏗 System Architecture

ESP32 Sensors
⬇
Signal Filtering & Classification
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Bluetooth Low Energy
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SwiftUI App
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AI Coaching Engine
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User Feedback (UI + Motor)

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🛠 Installation

iOS App

git clone https://github.com/HashtagPro-MC/Project-Neuronest