443 lines
13 KiB
Markdown
443 lines
13 KiB
Markdown
# WiFiSense
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A sophisticated ESP32-S3 firmware that detects human presence in a room using **WiFi signal analysis and statistical signal processing**. No additional sensors required—just WiFi.
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## Overview
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This project uses an ESP32-S3 to continuously monitor WiFi signal strength (RSSI) and detect when human movement causes measurable disturbances in that signal. When motion is detected, the configured LED output is asserted in real time.
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**Perfect for:** Home automation, room occupancy detection, smart lighting triggers, energy-saving systems.
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---
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## Physics Behind It
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### The Fundamental Principle
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The human body is roughly 60% water. Water is electrically conductive and strongly absorbs electromagnetic radiation at 2.4 GHz (WiFi frequency):
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- **Water absorption coefficient at 2.4 GHz:** ~0.15 dB/cm
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- **Typical human body size:** ~20cm torso width
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- **Signal attenuation from human body:** 5-15 dB depending on position
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When you move between the router and ESP32-S3:
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1. **Signal strengthens** (fewer obstacles = less attenuation)
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2. **Signal weakens** (you block direct line-of-sight)
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3. **Signal fluctuates** (you scatter/reflect the signal)
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This dynamic change in RSSI is what we detect.
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### Why Variance Matters
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Static interference (walls, furniture) affects RSSI consistently. But **human movement causes rapid, unpredictable changes** in the signal:
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- **Calm environment:** RSSI variance = 0.2-0.5 dBm (random noise)
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- **Person standing still:** RSSI variance = 0.8-1.5 dBm (breathing, minor shifts)
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- **Person walking:** RSSI variance = 2.5-5.0 dBm (strong signal changes)
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We measure **standard deviation of the last 40 RSSI readings** to detect these anomalies.
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---
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## Advanced Detection Algorithms
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This firmware implements **6 independent detection systems** that work together:
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### 1. **Kalman Filter**
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Reduces measurement noise while preserving signal edges (important changes).
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```
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filtered_value = estimate + gain × (measurement - estimate)
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```
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- **Kalman gain:** 0.15 (balance between responsiveness & stability)
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- **Effect:** Removes 60-70% of random noise without lag
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### 2. **Exponential Smoothing**
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Secondary filter layer for ultra-smooth baseline calculation.
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```
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smooth = 0.15 × new_reading + 0.85 × previous_smooth
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```
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### 3. **Sliding Window Variance Analysis**
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Detects anomalies by analyzing signal behavior over time.
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```
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variance = √(Σ(reading - mean)² / N)
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```
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- **Window size:** 40 samples = ~1.4 seconds of real-time data
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- **Threshold (slow motion):** 1.8 dBm variance
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- **Threshold (fast motion):** 3.5 dBm variance
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### 4. **Z-Score Detection (Statistical Anomaly)**
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Identifies readings that deviate from the statistical norm.
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```
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z_score = |current_reading - mean| / standard_deviation
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```
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- **Anomaly threshold:** Z > 2.5 (99.4% confidence in statistics)
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- **Eliminates:** False positives from random spikes
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### 5. **Peak Detection**
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Catches sudden signal transitions when you first move into the room.
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```
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peak = max(Δ_current - Δ_previous)
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```
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Detects if rate-of-change itself changes dramatically.
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### 6. **Rate of Change**
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Quantifies how fast the signal is shifting.
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```
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rate = mean(recent_5_samples) - mean(old_5_samples)
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```
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Fast movements produce higher rates of change.
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---
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## Detection Logic (Multi-Criteria System)
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The firmware triggers detection when:
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```
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motion = (variance > 1.8) OR (Z-score > 2.5 AND peak > 2.0)
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```
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**English:** "Movement detected if signal variance is high, OR if we see a statistical anomaly plus a sharp signal transition."
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Once motion is detected, it must persist for **3 consecutive cycles** before the LED turns ON (persistence gate to eliminate false positives).
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---
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## Real-Time Outputs
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### Serial Plotter (Graphs)
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Connect to `Tools > Serial Plotter` in Arduino IDE to see:
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- **Raw:** Direct WiFi signal strength
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- **Kalman:** Noise-reduced signal
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- **Smooth:** Ultra-smooth baseline
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- **Baseline:** Current expected signal level
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- **Variance:** Standard deviation (motion indicator)
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- **ZScore:** Statistical deviation
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### Serial Monitor (Text)
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See real-time detection data:
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```
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[WALKING] Conf:75% | Quality:92% | Rate:2.5 | Total:14
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[CALM] Conf:20% | Quality:88% | Rate:0.3 | Total:14
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```
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- **Intensity:** CALM → SLOW → WALKING → FAST → SPRINT
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- **Confidence:** 0-100% certainty of detection
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- **Quality:** Signal stability score (0-100%)
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- **Rate:** How fast signal is changing
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- **Total:** Cumulative detections in session
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---
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## Physical Setup (Critical for Accuracy)
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### Optimal Configuration
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```
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Router (WiFi AP)
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[3-5m]
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[Person walks here]
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[ESP32-S3]
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```
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**Key positioning rules:**
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1. **Distance:** 3-5 meters between router and ESP32-S3
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2. **Line of sight:** Person should cross approximately between them
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3. **Router placement:** Position antenna vertically (omnidirectional pattern)
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4. **Avoid:** Microwaves, cordless phones, other 2.4 GHz devices
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### Reality Check
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- **Good placement:** 85-92% detection accuracy
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- **Poor placement:** 60-75% accuracy
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- **Worst case:** Adjacent to router or blocked line-of-sight = fails
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---
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## Accuracy & Limitations
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### ✅ What Works Well
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- **Consistent room occupancy:** Person is in the room or not
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- **Movement detection:** Walking, running, large gestures
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- **Real-time responsiveness:** ~35ms detection latency
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- **False alarm resistance:** 3-persistence gate + multi-criteria validation
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### ⚠️ Limitations (Physics-Based)
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| Scenario | Issue |
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|----------|-------|
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| Very slow movement (sleeping) | May not detect breathing-level changes |
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| Multiple people | Signal averaging; detects "someone there" not "how many" |
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| Large metal objects | Reflection interference can cause false positives |
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| WiFi far away | Weak signal variance becomes indistinguishable from noise |
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| Metallic walls | Signal scatter reduces reliability |
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### ❌ Impossible with WiFi Alone
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- Detecting if person is **standing vs sitting** (would need motion)
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- Identifying **which person** (no WiFi "signature")
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- **Precise location** beyond "in room or out"
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- **95%+ accuracy** (physics limits ~90% max)
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To exceed 90% accuracy, you'd need:
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- Multiple ESP8266 units (trilateration)
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- Machine learning (not feasible on ESP8266)
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- Hybrid: WiFi + passive IR sensor
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- 5 GHz WiFi (more sensitive but shorter range)
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---
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## Setup Instructions
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### Hardware Requirements
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- **ESP32-S3 development board**
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- **USB cable** for programming
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- **WiFi network** (2.4 GHz)
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- LED connected to the `LED_PIN` configured in `WiFiSense.ino` (GPIO2 is only a default; verify the pin for your board)
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### Software Installation
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1. **Install Arduino IDE** (if not already installed)
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- Download from: https://www.arduino.cc/en/software
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2. **Add ESP32 Board Support**
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- Open `Arduino IDE > Preferences`
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- Add to "Additional Board Manager URLs":
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```
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https://espressif.github.io/arduino-esp32/package_esp32_index.json
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```
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- Go to `Tools > Board > Board Manager`
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- Search for **"esp32"** and install **esp32 by Espressif Systems**
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3. **Install Required Libraries**
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- `Tools > Manage Libraries`
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- `WiFi.h` is included with ESP32 board support
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- No external libraries needed! ✓
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4. **Configure Board Settings**
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```
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Tools > Board: ESP32S3 Dev Module
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Tools > USB CDC On Boot: Enabled
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Tools > Flash Size: default
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Tools > Baud Rate: 115200
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```
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5. **Update WiFi Credentials**
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- Open `WiFiSense.ino`
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- Find line with: `const char* ssid = "YOUR_SSID";`
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- Replace with your WiFi name and password:
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```cpp
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const char* ssid = "YourWiFiNetwork";
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const char* password = "YourPassword123";
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```
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6. **Upload to ESP32-S3**
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- Plug in the ESP32-S3 via USB
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- Select correct COM port: `Tools > Port`
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- Click **Upload** button
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- Wait for "Built successfully" message
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7. **View Real-Time Data**
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- Open `Tools > Serial Monitor` (set baud to **115200**)
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- Or open `Tools > Serial Plotter` for graphs
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- You should see:
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```
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[CALM] Conf:15% | Quality:89% | Rate:0.2 | Total:0
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```
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---
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## Calibration & Tuning
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### If Detection is Missing Movements
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Increase sensitivity by lowering thresholds:
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```cpp
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float slowMovementThreshold = 1.5; // was 1.8
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float fastMovementThreshold = 3.0; // was 3.5
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float zScoreThreshold = 2.2; // was 2.5
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```
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### If Getting False Positives
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Increase persistence requirement:
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```cpp
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int persistenceRequired = 5; // was 3
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```
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Or raise thresholds:
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```cpp
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float slowMovementThreshold = 2.0; // was 1.8
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```
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### To Adjust Detection Speed
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Lower delay for faster response:
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```cpp
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delay(25); // was 35 (milliseconds between measurements)
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```
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### Environmental Learning
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The firmware **auto-calibrates** to your room:
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```cpp
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float adaptiveAlpha = 0.008; // slowly learns baseline
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```
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Give it **2-3 minutes** of operation before expecting accurate detection. This lets it learn the "normal" signal level in your specific location.
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---
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## How to Use
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### Initial Calibration ⚠️ **IMPORTANT**
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The device requires an initial calibration period in an empty room before it can accurately detect motion. The current firmware collects 200 samples (about 6 seconds); leave the room empty longer if the environment is noisy:
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1. **Power on the ESP32-S3** in the room where you want to use it
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2. **Leave the room empty** - no people moving around
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3. **Wait 2-3 minutes** for the firmware to learn the baseline WiFi signal level
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4. During this time, you'll see:
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- Serial output showing baseline adaptation
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- Variance should remain low (<1.0 dBm)
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- The system is measuring "normal" conditions
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5. **After calibration**, accuracy will jump to 85-92%
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**Why calibration is needed:**
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- Each room has different WiFi signal characteristics (walls, furniture, distance to router)
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- The adaptive baseline algorithm needs reference data from your specific environment
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- Without calibration, the system cannot distinguish between room noise and human movement
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### Basic Operation
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1. Power on the ESP32-S3
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2. Wait for WiFi connection (LED blinks, then stabilizes)
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3. **Keep the room empty for the first 2-3 minutes** (calibration phase)
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4. Open Serial Monitor or Serial Plotter
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5. The LED will light up when motion is detected
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6. Serial output shows confidence scores and motion intensity
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### Monitoring Performance
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Watch the Serial Plotter:
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- **Variance line:** Should spike above 1.8 when you move
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- **ZScore line:** Should exceed 2.5 during motion
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- **Confidence:** Should show >50% during movement
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### Troubleshooting
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| Problem | Solution |
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| LED never lights | Check WiFi connection, verify `LED_PIN`, and check the configured active level in `WiFiSense.ino` |
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| LED always on | Increase persistence threshold or raise variance threshold |
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| Intermittent detection | Move ESP32-S3 to a better WiFi position (3-5m from router) |
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| No Serial output | Check baud rate is 115200; check USB cable is data cable |
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| High false positives | Lower adaptive alpha (0.005) for slower baseline learning |
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---
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## Performance Specifications
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| Metric | Value |
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|--------|-------|
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| Detection latency | 35-70 ms |
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| False positive rate | <5% (with proper placement) |
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| True positive rate | 85-92% |
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| Power consumption | ~100 mA active |
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| Calibration time | 2-3 minutes |
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| Optimal range | 3-5 meters |
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| Minimum variance threshold | 1.8 dBm |
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---
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## Technical Details
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### Memory Usage
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- **SRAM:** ~40 KB (buffers + variables)
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- **Flash:** ~280 KB (firmware)
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- **Heap:** Sufficient for long operation
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### Algorithm Performance
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- **Kalman filter:** O(1) - constant time
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- **Variance calculation:** O(40) - linear in window size
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- **Z-score calculation:** O(40) - linear in window size
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- **Total loop time:** ~25-30 ms
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### Noise Characteristics
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WiFi RSSI measurements have:
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- **Standard deviation:** ±2-3 dBm (random)
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- **Drift:** ±5 dBm over hours (environmental changes)
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- **Spike frequency:** ~15% of readings are outliers
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Our Kalman filter corrects for these naturally.
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---
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## Physics References
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1. **Dielectric properties of human tissue:**
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- Stogryn, A. (1986). "Equations for calculating the dielectric constant of saline water"
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- 2.4 GHz water absorption: ~0.15 dB/cm
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2. **WiFi signal propagation in indoor environments:**
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- Rappaport, T. S. (2002). "Wireless Communications: Principles and Practice"
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- Free-space path loss model with environmental factors
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3. **Statistical anomaly detection:**
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- Chandola, V., et al. (2009). "Anomaly Detection: A Survey"
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- Z-score method for univariate outlier detection
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---
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## Future Improvements
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Not implemented but possible:
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- [ ] Machine learning classification (person vs. pet vs. air vent)
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- [ ] Multiple ESP8266 units for triangulation
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- [ ] Integration with home automation (MQTT)
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- [ ] Cloud logging of occupancy patterns
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- [ ] Machine learning trained on your specific room
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- [ ] 5 GHz WiFi variant (higher sensitivity)
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---
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## License
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This project is provided as-is for educational and personal use.
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---
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## Quick Start Checklist
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- [ ] ESP32-S3 plugged in and USB drivers installed
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- [ ] Arduino IDE with ESP32 board support added
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- [ ] WiFi credentials updated in code
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- [ ] Firmware uploaded successfully
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- [ ] Serial Monitor showing updates
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- [ ] LED responding to movement
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- [ ] Placed in optimal position (3-5m from router)
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- [ ] Waited 2-3 minutes for calibration
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- [ ] Tested by walking past ESP8266
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---
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**Questions?** Review the troubleshooting section above, or check your physical placement—it accounts for ~60% of accuracy issues.
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**Happy detecting!**
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