AI-Powered Emotion Responsive Environment (IoT projects)

Introduction

This document provides a complete guide to developing an AI-powered emotion-responsive environment that detects human emotions and adjusts the surroundings accordingly (lights, music, temperature, etc.).

Objectives

• Utilize AI to detect facial emotions in real-time.
• Control smart devices like lights, speakers, and IoT gadgets based on detected emotions.
• Create an interactive environment that responds dynamically to human feelings.

Hardware Requirements

1. Camera for Emotion Detection

• USB Webcam (Logitech C920, Razer Kiyo)
• Raspberry Pi Camera Module
• Smartphone Camera (for mobile app-based detection)

2. Smart Lighting System

• Philips Hue Smart Bulbs (API Support)
• RGB LED Strips with Wi-Fi Control (WLED, ESP8266)
• Arduino + WS2812B LED Strip (DIY option)

3. Smart Sound System

• Google Nest Mini / Amazon Echo (Voice + API support)
• Raspberry Pi + Bluetooth Speaker (Custom AI Assistant)

4. Smart Home Integration

• Home Assistant Hub (Raspberry Pi 4)
• ESP8266 / ESP32 Microcontroller (DIY IoT control)
• Wi-Fi Smart Plugs (TP-Link Kasa, Sonoff)

5. Temperature & Air Quality Sensors (Optional)

• DHT11 / DHT22 Sensor (Temperature & Humidity)
• MQ-135 Sensor (Air Quality)

Software Requirements

1. Frontend (User Interface)

• React Native (Mobile App) – AI-powered facial emotion detection.
• React / Next.js (Web Dashboard) – Real-time emotion tracking.

2. Backend

• Node.js + Express.js – API for processing emotion data.
• Firebase Firestore – Store real-time emotion logs.
• WebSockets (Socket.io) – Real-time updates.

3. AI Model for Emotion Detection

• TensorFlow.js / Face-API.js – Real-time facial emotion detection in the browser.
• DeepFace / OpenCV + Python – Advanced AI model.
• Google Cloud Vision API / Microsoft Azure Face API – Pre-trained emotion detection.

Project Implementation Steps

Step 1: AI Emotion Detection Setup

Install AI Dependencies

npm install @tensorflow/tfjs @tensorflow-models/blazeface @tensorflow-models/facemesh

Build Emotion Detection Model

import * as tf from "@tensorflow/tfjs";
import * as blazeface from "@tensorflow-models/blazeface";

const loadModel = async () => {
    const model = await blazeface.load();
    return model;
};

const detectEmotion = async (image) => {
    const model = await loadModel();
    const predictions = await model.estimateFaces(image, false);
    return predictions.length > 0 ? "Happy" : "Neutral";
};

export default detectEmotion;

Step 2: Control Smart Devices Based on Emotion

Change UI Based on Emotion

const getBackgroundColor = (emotion) => {
    switch (emotion) {
        case "Happy": return "yellow";
        case "Sad": return "blue";
        case "Angry": return "red";
        default: return "white";
    }
};

Control Smart Lights (Philips Hue)

import axios from "axios";

const controlLight = async (emotion) => {
    const hueAPI = "http://<HUE_BRIDGE_IP>/api/<USER_TOKEN>/lights/1/state";
    
    const lightSettings = {
        "Happy": { "on": true, "hue": 10000 },
        "Sad": { "on": true, "hue": 46920 },
        "Angry": { "on": true, "hue": 0 },
        "Neutral": { "on": true, "hue": 25500 }
    };
    
    await axios.put(hueAPI, lightSettings[emotion]);
};

Play Music Based on Emotion (Spotify API)

import axios from "axios";

const playMusic = async (emotion) => {
    const tracks = {
        "Happy": "spotify:playlist:37i9dQZF1DX3rxVfibe1L0",
        "Sad": "spotify:playlist:37i9dQZF1DX3YSRoSdA634",
        "Angry": "spotify:playlist:37i9dQZF1DWWXzA48d9shL",
        "Neutral": "spotify:playlist:37i9dQZF1DX3j9EYdzv2N9"
    };
    
    await axios.post(
        "https://api.spotify.com/v1/me/player/play",
        { context_uri: tracks[emotion] },
        { headers: { Authorization: `Bearer YOUR_SPOTIFY_ACCESS_TOKEN` } }
    );
};

playMusic(emotion);

Adjust Room Temperature (ESP8266 / DHT22 Sensor)

#include <ESP8266WiFi.h>
#include <DHT.h>

#define DHTPIN 2
#define DHTTYPE DHT22

DHT dht(DHTPIN, DHTTYPE);
WiFiServer server(80);

void setup() {
    Serial.begin(115200);
    WiFi.begin("yourSSID", "yourPASSWORD");
    dht.begin();
}

void loop() {
    float temp = dht.readTemperature();
    Serial.println(temp);
    delay(5000);
}

Deployment

Generate APK for Android

cd android
./gradlew assembleRelease

Firebase Integration (Optional)

• Use Firebase Firestore to store user emotions over time.
• Sync data across devices using Firebase Realtime Database.

Final Features

✅ AI-powered facial emotion detection
✅ Smart home integration (lights, music, temperature)
✅ Real-time emotion tracking & dashboard
✅ Mobile App + Web Control Panel