🏋️‍♂️ Kobe Wearable Fitness Tracker Integration

⚠️ Disclaimer: This is a simulation project intended for demonstrating embedded system design and data integration. It is not a production-level implementation of the actual Kobe Robot or its proprietary systems.

This project simulates an embedded bridge system that connects third-party wearable fitness devices (like Fitbit or Garmin) to Kobe's B2C ecosystem. It uses an ESP32 microcontroller to handle Bluetooth Low Energy (BLE) communications with wearables, buffer the fitness data (heart rate, step count), and sync it to Kobe’s main system over Wi-Fi through a local network API.

📌 Features

🔗 BLE Pairing with secure two-way validation and reconnection logic.

📶 Wi-Fi Connectivity with auto-reconnect and local API push.

📊 Real-Time Buffering for heart rate and step count.

🚨 Threshold Alerts when heart rate exceeds safe range.

📡 Seamless Sync to Kobe's system when buffer is full.

🧠 System Architecture

🛠️ Technologies Used

• ESP32

• Arduino Framework (PlatformIO/Arduino IDE)

• BLE (GATT Profile)

• Wi-Fi + HTTPClient

• FreeRTOS (Threading BLE & Wi-Fi)

📁 Project Structure

. ├── main.ino # Main logic
├── blue.h / blue.c # Bluetooth setup, notify, reconnect
├── buffer.h / buffer.c # Data buffering logic
├── api_handler.h / .c # API POST functions
├── secretes.h # Wi-Fi credentials
├── README.md
├── imgs # assets

🔄 Data Flow

BLE Scan & Pair → Wearable Device

Register Notification → Heart Rate & Step Count

Buffer Values → Local Storage on Device

Buffer Full → Trigger HTTP POST to API

Kobe Server Receives → Data Sync + Device Metadata

🚀 Getting Started

1. Clone the repo and open in Arduino IDE or PlatformIO.

2. Add your secretes.h:

   const char* ssid = "your_wifi_ssid";

   const char* password = "your_wifi_password";

3. Flash to ESP32.

4. Monitor logs using Serial Monitor at 115200 baud.

📡 API Integration

• Endpoint 1: /api/heart_rate

• Endpoint 2: /api/step_count

• Endpoint 3: /api/register_device

• Endpoint 4: /api/is_device_secured

• Format: JSON { heart_rate: [...], steps: [...], device_name: "", mac: "" }

✅ Future Enhancements

Multi-user wearable support

Onboard analytics for alerting

MQTT instead of HTTP for real-time feedback