Intelligent AI Smart Helmet with Alcohol Detection, Accident Prediction and Emergency Response Automation

Intelligent AI Smart Helmet with Alcohol Detection, Accident Prediction & Emergency Response Automation ESP32 + IoT + AI Agent + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak

Intelligent AI Smart Helmet

Alcohol Detection | Accident Prediction | Emergency Response Automation

1. Project Overview

This project develops an AI-powered smart helmet using ESP32, MQ3 alcohol sensor, MPU6050, GPS, ThingSpeak cloud, n8n automation, Google Sheets and Telegram alerts.

2. Objectives

• Alcohol Detection
• Helmet Wear Detection
• Accident Detection
• Accident Prediction
• GPS Tracking
• Telegram Alerts
• Voice Notifications
• Google Sheets Logging
• ThingSpeak Dashboard
• AI Agent Safety Analysis

3. Components List

Component	Quantity
ESP32	1
MQ3 Alcohol Sensor	1
MPU6050 Sensor	1
NEO-6M GPS Module	1
IR Helmet Sensor	1
Buzzer	1
Battery Pack	1
TP4056 Charger	1

4. System Architecture

Helmet Sensors
      |
      V
    ESP32
      |
      V
 ThingSpeak Cloud
      |
      V
   n8n Workflow
      |
      V
 AI Agent Analysis
      |
 --------------------
 |         |        |
 V         V        V
Telegram  Sheets  Voice Alerts

5. Circuit Connections

MQ3 AO      -> GPIO34
MPU6050 SDA -> GPIO21
MPU6050 SCL -> GPIO22
GPS TX      -> GPIO16
GPS RX      -> GPIO17
IR Sensor   -> GPIO25
Buzzer      -> GPIO27

6. Accident Detection Formula

a = sqrt(x² + y² + z²)

IF a > 3g
THEN Accident Detected

7. AI Risk Prediction

Parameter	Weight
Hard Braking	20
High Tilt	25
High Speed	20
Sudden Turns	20
Alcohol	15

Risk Score = Σ(Wi × Fi)

If Risk > 70
Send Warning Alert

8. Flowchart Logic

START

Initialize Sensors

Read Helmet Sensor

Helmet Worn?

NO --> Alert

YES

Read MQ3

Alcohol?

YES --> Alert

NO

Read MPU6050

Calculate Risk

Risk > 70 ?

YES --> Warning

NO

Accident?

YES --> Emergency Alert

NO

Upload Data

Repeat

9. ESP32 Sample Code

float alcoholValue;
float acceleration;
float riskScore;

void loop()
{
  alcoholValue = analogRead(34);

  readMPU();

  riskScore = calculateRisk();

  if(alcoholValue > 2000)
  {
      sendAlert();
  }

  if(acceleration > 3.0)
  {
      sendEmergency();
  }

  uploadThingSpeak();

  delay(1000);
}

10. ThingSpeak Fields

Field	Description
Field1	Alcohol Level
Field2	Acceleration
Field3	Risk Score
Field4	Latitude
Field5	Longitude
Field6	Helmet Status

11. n8n Workflow

Webhook
   |
   V
Receive ESP32 Data
   |
   V
AI Agent
   |
  / \
 /   \
V     V

Google Sheets
Telegram Alerts

12. Telegram Bot Setup

1. Open Telegram
2. Search BotFather
3. Create New Bot
4. Get Bot Token
5. Add Token to n8n Telegram Node

13. Voice Notification Automation

ESP32
  |
  V
n8n
  |
  V
AI Text
  |
  V
Text To Speech
  |
  V
Telegram Voice Alert

14. Emergency Response Workflow

1. Detect Accident
2. Get GPS Coordinates
3. Generate AI Message
4. Send Telegram Alert
5. Send Voice Message
6. Update Google Sheet
7. Update ThingSpeak Dashboard

15. AI Power Prediction

Power = Voltage × Current

P = V × I

Battery Life =
Battery Capacity / Current Draw

AI predicts remaining battery life using previous sensor, WiFi and GPS usage patterns.

16. Future Enhancements

• TinyML Accident Prediction
• Driver Fatigue Detection
• Heart Rate Monitoring
• Voice Assistant
• AWS IoT Integration
• Firebase Dashboard
• Camera Based Safety Monitoring

17. Expected Outcomes

• Alcohol Detection
• Accident Detection
• Accident Prediction
• GPS Tracking
• AI Safety Analysis
• Telegram Voice Alerts
• Google Sheets Logging
• ThingSpeak Dashboard
• Emergency Response Automation

Smart Helmet Project Documentation | ESP32 + AI + IoT + n8n + Telegram

For a complete academic project, I can also generate: index.php (dashboard homepage) config.php (database configuration) api.php (ESP32 data receiver API) save_data.php (MySQL storage) dashboard.php (live charts) telegram_alert.php (Telegram notifications) predict_ai.php (AI risk prediction module) database.sql (MySQL tables) Full project folder structure ready for XAMPP deployment.