AI Smart Helmet for Accident Detection and Rider Safety

AI Smart Helmet for Accident Detection and Rider Safety ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Dashboard

AI Smart Helmet for Accident Detection and Rider Safety ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Dashboard 1. Project Overview This project is an AI-powered Smart Helmet System designed to improve rider safety using: ESP32 Crash detection sensors Helmet wearing detection Alcohol detection GPS tracking Cloud IoT dashboard n8n AI automation Telegram voice alerts Google Sheets logging ThingSpeak monitoring The helmet continuously monitors rider conditions and accident events. If an accident occurs: ESP32 detects crash/fall GPS location captured Data uploaded to ThingSpeak n8n automation triggered Telegram voice + text alerts sent Emergency contact notified Data stored in Google Sheets AI predicts battery/power consumption patterns 2. Features Core Features ✅ Accident detection ✅ Helmet wearing detection ✅ Alcohol detection ✅ Rider motion monitoring ✅ GPS live location ✅ Emergency SOS alerts ✅ Telegram voice notifications ✅ Google Sheets logging ✅ ThingSpeak cloud dashboard ✅ AI-based power usage prediction ✅ Real-time IoT monitoring 3. System Architecture Helmet Sensors ↓ ESP32 Controller ↓ WiFi / Internet ↓ ThingSpeak Cloud ↓ n8n Automation Server ↓ ├── Telegram Bot Alerts ├── Telegram Voice Alerts ├── Google Sheets Logging └── AI Agent Processing 4. Components List Component Quantity Purpose ESP32 Dev Board 1 Main controller MPU6050 Accelerometer + Gyroscope 1 Accident/fall detection MQ3 Alcohol Sensor 1 Alcohol detection GPS Module NEO-6M 1 Live location IR Sensor 1 Helmet wear detection Buzzer 1 Local alarm LED Indicators 2 Status indication Push Button 1 Emergency SOS 18650 Battery 1 Portable power TP4056 Charging Module 1 Battery charging Jumper Wires — Connections Helmet 1 Mounting platform 5. Working Principle Accident Detection The MPU6050 detects: Sudden impact High acceleration Abnormal tilt angle If threshold exceeds: Impact > 2.5g OR Tilt angle > 60° then accident event triggered. Helmet Detection IR sensor checks whether helmet is worn. If not worn: Buzzer activates Engine relay can remain OFF Alcohol Detection MQ3 detects alcohol concentration. If alcohol level exceeds threshold: Warning alert generated Vehicle ignition can be disabled GPS Tracking GPS module continuously updates: Latitude Longitude Used in emergency alerts. 6. Circuit Connections ESP32 Pin Mapping Module ESP32 Pin MPU6050 SDA GPIO21 MPU6050 SCL GPIO22 MQ3 Analog GPIO34 IR Sensor GPIO27 GPS TX GPIO16 GPS RX GPIO17 Buzzer GPIO25 LED GPIO26 SOS Button GPIO14 7. Circuit Schematic Diagram +------------------+ | ESP32 | | | MPU6050 SDA | GPIO21 | MPU6050 SCL | GPIO22 | MQ3 OUT ----| GPIO34 | IR Sensor --| GPIO27 | GPS TX -----| GPIO16 | GPS RX -----| GPIO17 | Buzzer -----| GPIO25 | LED --------| GPIO26 | SOS Button -| GPIO14 | +------------------+ 8. Flowchart START ↓ Initialize Sensors ↓ Connect WiFi ↓ Read Sensor Data ↓ Helmet Worn? ┌───────┴────────┐ NO YES ↓ ↓ Alert Check Alcohol ↓ Alcohol Detected? ┌─────┴─────┐ YES NO ↓ ↓ Warning Monitor MPU6050 ↓ Accident Detected? ┌────┴────┐ YES NO ↓ ↓ Send Cloud Data Loop ↓ Trigger n8n Workflow ↓ Telegram + Voice + Sheets ↓ END 9. ESP32 Source Code (Arduino IDE) #include #include #include #include #include #include MPU6050 mpu; TinyGPSPlus gps; HardwareSerial gpsSerial(1); const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String apiKey = "THINGSPEAK_API_KEY"; #define MQ3_PIN 34 #define IR_PIN 27 #define BUZZER 25 #define LED 26 float ax, ay, az; void setup() { Serial.begin(115200); pinMode(IR_PIN, INPUT); pinMode(BUZZER, OUTPUT); pinMode(LED, OUTPUT); Wire.begin(); mpu.initialize(); gpsSerial.begin(9600, SERIAL_8N1, 16, 17); WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(500); Serial.print("."); } Serial.println("WiFi Connected"); } void loop() { mpu.getAcceleration(&ax, &ay, &az); float impact = sqrt(ax * ax + ay * ay + az * az) / 16384.0; int alcohol = analogRead(MQ3_PIN); int helmet = digitalRead(IR_PIN); while (gpsSerial.available()) { gps.encode(gpsSerial.read()); } double lat = gps.location.lat(); double lng = gps.location.lng(); if (helmet == LOW) { digitalWrite(BUZZER, HIGH); } if (alcohol > 2500) { Serial.println("Alcohol Detected"); } if (impact > 2.5) { digitalWrite(BUZZER, HIGH); digitalWrite(LED, HIGH); if (WiFi.status() == WL_CONNECTED) { HTTPClient http; String url = "http://api.thingspeak.com/update?api_key=" + apiKey + "&field1=" + String(impact) + "&field2=" + String(lat, 6) + "&field3=" + String(lng, 6); http.begin(url); int httpCode = http.GET(); Serial.println(httpCode); http.end(); } } delay(2000); } 10. ThingSpeak Cloud Setup Create Channel Go to: ThingSpeak Create fields: Field Data Field 1 Impact Force Field 2 Latitude Field 3 Longitude Field 4 Alcohol Level Field 5 Helmet Status Copy: Write API Key Channel ID 11. n8n Automation Workflow Install n8n Use: n8n Official Website Workflow Logic ThingSpeak Webhook ↓ IF Accident Detected ↓ Generate AI Summary ↓ Telegram Message ↓ Telegram Voice Alert ↓ Google Sheets Logging 12. n8n Workflow JSON { "nodes": [ { "parameters": {}, "name": "Webhook", "type": "n8n-nodes-base.webhook", "typeVersion": 1, "position": [250, 300] }, { "parameters": { "chatId": "YOUR_CHAT_ID", "text": "🚨 Accident Detected!" }, "name": "Telegram", "type": "n8n-nodes-base.telegram", "typeVersion": 1, "position": [500, 300] } ], "connections": { "Webhook": { "main": [ [ { "node": "Telegram", "type": "main", "index": 0 } ] ] } } } 13. Telegram Bot Setup Step 1 — Create Bot Open: BotFather Telegram Bot Setup Commands: /newbot Copy: BOT TOKEN Step 2 — Get Chat ID Open: https://api.telegram.org/bot/getUpdates Copy chat ID. 14. Telegram Voice Notification Automation Method n8n converts alert text to speech using: Google TTS ElevenLabs API gTTS Python API Voice Message Example: Emergency Alert. Accident detected. Location shared to emergency contacts. 15. Google Sheets Integration Create spreadsheet columns: Timestamp Impact Latitude Longitude Alcohol Helmet In n8n: Google Sheets Node ↓ Append Row Useful for: Analytics Accident history AI training dataset 16. AI Power Consumption Prediction Logic Objective Predict remaining battery life. Inputs WiFi usage GPS activity Sensor sampling rate Alert frequency AI Formula Simple linear prediction: Battery Remaining=Battery Capacity−(WiFi+GPS+Sensor+Alert Power)×Time Advanced AI Future model: TinyML Edge AI LSTM battery forecasting 17. ThingSpeak Dashboard Widgets Add widgets: GPS location map Impact graph Helmet status Alcohol level Battery level 18. AI Agentic IoT Features AI Agent Responsibilities The AI agent can: ✅ Analyze accidents ✅ Predict dangerous driving ✅ Detect battery anomalies ✅ Send smart alerts ✅ Recommend charging times ✅ Generate rider safety reports 19. Future Enhancements Hardware GSM module Camera module Air quality sensor Heartbeat sensor Voice assistant AI Enhancements TinyML crash classification Rider fatigue detection Computer vision Edge AI processing Predictive maintenance 20. Deployment Guide Helmet Assembly Mount: MPU6050 at helmet center GPS on top side ESP32 rear compartment Battery in protected enclosure Power Management Use: 5V regulated supply Deep sleep mode Auto power shutdown Waterproofing Recommended: ABS enclosure Silicone seal Shockproof foam 21. Testing Procedure Test Cases Test Expected Result Helmet removed Buzzer ON Alcohol detected Warning Sudden fall Alert triggered GPS unavailable Retry Internet OFF Store locally 22. Estimated Cost Component Approx Cost ESP32 ₹500 MPU6050 ₹150 GPS Module ₹450 MQ3 Sensor ₹120 Battery ₹300 Miscellaneous ₹500 Total Estimated Cost ₹2000–₹3000 23. Applications Smart transportation Rider safety Fleet management Delivery services Emergency response systems Insurance telematics 24. Conclusion This project combines: IoT AI Cloud automation ESP32 embedded systems n8n workflows Telegram alerts Real-time monitoring to build a next-generation AI Smart Helmet Safety System capable of reducing accident response time and improving rider safety using intelligent automation. Useful Resources ESP32 Official Documentation Arduino IDE ThingSpeak Platform n8n Documentation Telegram Bot API Google Sheets API