AI Smart Wheelchair with Voice and Eye Control

AI Smart Wheelchair with Voice and Eye Control AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Dashboard

AI Smart Wheelchair with Voice and Eye Control AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Dashboard 1. Project Overview This project is an AI-enabled Smart Wheelchair designed for elderly and disabled individuals. The wheelchair can be controlled using: 👁️ Eye movement tracking 🎙️ Voice commands 📱 Mobile IoT dashboard 🤖 AI-based automation The system uses an ESP32 microcontroller integrated with: Sensors Motor drivers Cloud platforms AI analytics n8n workflow automation Telegram voice alert system The wheelchair also sends: Battery health alerts Emergency notifications Usage analytics Power consumption predictions 2. Key Features Smart Control Features Voice-controlled navigation Eye-controlled movement Obstacle detection Automatic braking AI-assisted movement prediction IoT Features Real-time monitoring Cloud dashboard Telegram alerts Google Sheets logging Remote tracking AI Features Battery prediction Usage pattern learning Intelligent alert generation Power optimization 3. System Architecture +----------------------+ | User Voice | +----------+-----------+ | v Voice Recognition | v +-------------+ +-------------+ +--------------+ | Eye Sensor | --> | ESP32 | --> | Motor Driver | +-------------+ +-------------+ +--------------+ | ---------------------------------------- | | | v v v ThingSpeak Google Sheets Telegram Bot | | | -------------------------------------- | v n8n AI Automation 4. Components List Component Quantity Purpose ESP32 Dev Board 1 Main controller L298N Motor Driver 1 Motor control DC Geared Motors 2 Wheelchair movement IR Eye Blink Sensor 1 Eye movement detection Ultrasonic Sensor HC-SR04 2 Obstacle detection Microphone Module 1 Voice command input Battery Pack 12V 1 Power supply Buck Converter 1 Voltage regulation Relay Module 1 Safety shutdown Buzzer 1 Alert system WiFi Router/Hotspot 1 Internet connectivity Jumper Wires Multiple Connections Wheelchair Chassis 1 Base frame 5. Circuit Schematic Diagram +----------------+ | ESP32 | | | | GPIO 18 -----> Motor IN1 | GPIO 19 -----> Motor IN2 | GPIO 21 -----> Motor IN3 | GPIO 22 -----> Motor IN4 | GPIO 5 <---- Eye Sensor | GPIO 13 <---- Echo | GPIO 12 ----> Trigger | GPIO 34 <---- Mic Module | GPIO 25 ----> Buzzer +----------------+ | v WiFi Connection | ------------------------ | | | v v v Telegram n8n ThingSpeak 6. Flowchart START | v Initialize ESP32 | v Connect to WiFi | v Read Sensors Data | ------------------- | | | v v v Voice Eye Obstacle Command Movement Detection | | | ---------- | | | v | Control Motors <--- | v Upload Data to Cloud | v Trigger n8n Workflow | v Send Telegram Alerts | v LOOP 7. ESP32 Source Code (Arduino IDE) #include #include const char* ssid = "YOUR_WIFI_NAME"; const char* password = "YOUR_WIFI_PASSWORD"; String apiKey = "THINGSPEAK_API_KEY"; #define IN1 18 #define IN2 19 #define IN3 21 #define IN4 22 #define trigPin 12 #define echoPin 13 #define eyeSensor 5 #define buzzer 25 long duration; int distance; void setup() { Serial.begin(115200); pinMode(IN1, OUTPUT); pinMode(IN2, OUTPUT); pinMode(IN3, OUTPUT); pinMode(IN4, OUTPUT); pinMode(trigPin, OUTPUT); pinMode(echoPin, INPUT); pinMode(eyeSensor, INPUT); pinMode(buzzer, OUTPUT); WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(500); Serial.print("."); } Serial.println("WiFi Connected"); } void loop() { // Ultrasonic Distance digitalWrite(trigPin, LOW); delayMicroseconds(2); digitalWrite(trigPin, HIGH); delayMicroseconds(10); digitalWrite(trigPin, LOW); duration = pulseIn(echoPin, HIGH); distance = duration * 0.034 / 2; // Eye Sensor int eyeState = digitalRead(eyeSensor); if(distance < 20) { stopWheelchair(); digitalWrite(buzzer, HIGH); } else { digitalWrite(buzzer, LOW); if(eyeState == HIGH) { moveForward(); } else { stopWheelchair(); } } uploadThingSpeak(distance, eyeState); delay(3000); } void moveForward() { digitalWrite(IN1, HIGH); digitalWrite(IN2, LOW); digitalWrite(IN3, HIGH); digitalWrite(IN4, LOW); } void stopWheelchair() { digitalWrite(IN1, LOW); digitalWrite(IN2, LOW); digitalWrite(IN3, LOW); digitalWrite(IN4, LOW); } void uploadThingSpeak(int distance, int eye) { if(WiFi.status()== WL_CONNECTED){ HTTPClient http; String url = "http://api.thingspeak.com/update?api_key=" + apiKey + "&field1=" + String(distance) + "&field2=" + String(eye); http.begin(url); int httpCode = http.GET(); Serial.println(httpCode); http.end(); } } 8. n8n Workflow Logic Workflow Functions Receive ESP32 webhook data Analyze sensor values Generate AI decisions Send Telegram alerts Store logs in Google Sheets Trigger voice notifications n8n Workflow Steps Webhook Trigger | v HTTP Request (ESP32 Data) | v IF Node (distance < 20?) | YES/NO | v Telegram Alert | v Google Sheets Logging | v AI Processing Node | v Voice Notification 9. Example n8n Workflow JSON { "nodes": [ { "name": "Webhook", "type": "n8n-nodes-base.webhook", "parameters": { "path": "wheelchair-data" } }, { "name": "Telegram", "type": "n8n-nodes-base.telegram", "parameters": { "chatId": "YOUR_CHAT_ID", "text": "Obstacle detected!" } } ] } 10. Telegram Bot Setup Step 1: Create Bot Open Telegram and search: Telegram Then message: @BotFather Commands: /newbot BotFather provides: Bot Token API access Step 2: Get Chat ID Send message to your bot. Open: https://api.telegram.org/bot/getUpdates Find: "chat":{"id":123456789} Step 3: Send Notifications Example API: https://api.telegram.org/bot/sendMessage?chat_id=&text=ObstacleDetected 11. Google Sheets Integration Create Sheet Columns Time Distance Eye State Battery Status n8n Google Sheets Node Connect Google account Select Spreadsheet Append Rows automatically Data stored: Sensor logs Alerts Battery prediction User activity 12. ThingSpeak Dashboard Setup Create Channel Use: ThingSpeak Create Fields: Distance Eye Sensor Battery Temperature Dashboard Widgets Live graph Gauge meter Alert chart Battery analytics 13. AI Power Consumption Prediction Logic Goal Predict battery drain and optimize wheelchair runtime. Inputs Motor usage time Obstacle frequency Distance traveled Battery voltage Speed AI Formula Battery Consumption: P=V×I Remaining Battery Estimate: Battery Remaining=Battery total ​ −Consumption Prediction Logic IF battery < 20% Send Alert Reduce Motor Speed Enable Power Saving 14. Voice Notification Automation Telegram Voice Alerts n8n converts text to speech: “Obstacle detected” “Battery low” “Emergency assistance required” Workflow ESP32 Event | v n8n Webhook | v AI Decision | v Text-to-Speech | v Telegram Voice Message 15. AI Agentic Features Intelligent Behaviors Learns user movement patterns Predicts battery usage Detects abnormal activity Sends autonomous alerts Example AI Actions Situation AI Response Low battery Reduce speed Obstacle nearby Stop wheelchair Emergency detected Notify caregiver Long inactivity Trigger wellness alert 16. Future Enhancements Advanced AI Features Face recognition Emotion detection Health monitoring Fall detection IoT Upgrades GPS tracking Mobile app Cloud AI dashboard Remote driving Hardware Upgrades Li-ion smart BMS Brushless motors Solar charging Autonomous navigation 17. Deployment Guide Hardware Assembly Mount motors Install ESP32 Connect sensors Attach battery Configure wiring Software Installation Arduino IDE Install: ESP32 board package WiFi library HTTPClient library Cloud Setup Configure ThingSpeak API Configure n8n workflow Setup Telegram bot Connect Google Sheets 18. Applications Disabled assistance Elderly mobility Smart hospitals Rehabilitation centers AI healthcare systems 19. Advantages Hands-free control Low-cost AI system Real-time monitoring Emergency automation Cloud analytics 20. Conclusion This project combines: ESP32 IoT AI automation Voice control Eye tracking Cloud analytics Agentic workflows to create a modern AI Smart Wheelchair System capable of improving mobility, safety, and independence for users with physical disabilities.