ESP32 Baby Monitor Kit with MPU6050 - Edge AI Anomaly Detection
Build an AI-Powered Baby Room Comfort Monitor with ESP32, MPU6050 & On-Device Anomaly Detection
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
For parents and makers who refuse to trade privacy for peace of mind, this kit lets you build a local AI system that continually monitors crib motion, room temperature, and humidity - and only alerts you when it detects a true anomaly. Running entirely on the ESP32, a TensorFlow Lite model learns typical sensor patterns and flags deviations instantly on a crisp OLED display. No cameras, no cloud subscriptions, no data ever leaving the nursery.
What You'll Build
A compact, enclosure-housed device that sits beside the crib and observes movement via the MPU6050 accelerometer-gyroscope while tracking ambient comfort with the DHT22. Over the first few hours, the onboard neural network establishes normal behaviour for your baby's movements and environment; after that, sustained departures - like sudden stillness, unusual shaking, or a sharp temperature drop - trigger a visual alert on the 0.96-inch OLED. The ACS712 current sensor can also monitor a connected appliance (such as a bottle warmer or heating pad) for unexpected power draw, adding an extra layer of safety awareness.
What You'll Learn
- Deploy a quantized TensorFlow Lite anomaly detection model on an ESP32 microcontroller - no cloud or mobile phone needed
- Fuse real-world sensor data from the MPU6050 (acceleration + gyroscope) and the DHT22 (temperature + humidity) into a unified time-series pipeline
- Train and compress a time-series model specifically for on-device inference, understanding pruning, quantization, and validation on embedded hardware
- Design a self-contained alerting system using an I2C OLED display, local buzzers, and condition-based logic that responds to model outputs
Kit Contents
| Component | Quantity |
|---|---|
| ESP32 Dev Board | 1 |
| MPU6050 Accelerometer-Gyroscope | 1 |
| DHT22 Temperature & Humidity Sensor | 1 |
| ACS712 5A Current Sensor | 1 |
| 0.96-inch OLED Display (I2C) | 1 |
| LM2596 Buck Converter | 1 |
| 100nF Capacitors | 10 |
| 4.7k? Resistors | 5 |
| PCB Prototype Board | 2 |
| Enclosure Box | 1 |
| 5V 2A Power Supply Unit | 1 |
| Soldering Iron | 1 |
| Solder Wire | 1 |
Why Buy This Kit Instead of Sourcing Parts Separately
| Factor | Sourcing Separately | Compoden Kit |
|---|---|---|
| Compatibility checks | You verify every part, wiring, and voltage tolerance | Pre-tested as a system - ESP32, sensors, display, and power supply work together out of the box |
| Build support | Forums and scattered tutorials | AI companion trained on this exact project, plus WhatsApp backup from our team |
| Time to first working build | Days of debugging sensor drivers and model integration | Hours, with step-by-step guidance on wiring, model flashing, and calibration |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
B.Tech ECE/EEE students tackling capstone projects that demand edge AI, Smart India Hackathon teams building privacy-first healthcare solutions, and maker-parents who want a baby monitor that never streams data to a server. It also fits IIT, NIT, VIT, and BITS Pilani students looking for a robust embedded ML project - and any engineer who believes a nursery's intelligence should live inside the room, not on a distant cloud.
Built and Backed by Compoden
Every Compoden kit ships with an AI build companion trained on this exact project - accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.
What if I get stuck during the build?
Scan the QR code inside the box to start a session with the AI companion, which knows every component and wiring detail of this kit. If you need a human, reach out via WhatsApp - our team responds within hours and can even video-call for tricky soldering or model-deployment steps.
Do I need prior machine learning experience to complete this kit?
No. The TensorFlow Lite model comes pre-trained and ready to flash onto the ESP32. The AI companion guides you through the process, and for those who want to go deeper, we include a Jupyter notebook that lets you re-train with your own sensor data and see how the model adapts.
Can this monitor detect if my baby stops moving or breathing?
The MPU6050-anchored model learns typical crib movement patterns and flags sustained absence of motion or erratic shaking - but it is not a medical device. It can surface unusual patterns that warrant a check; always follow pediatric safe-sleep guidelines and never rely solely on electronics for infant safety.
Does the monitor send any data to the internet?
No. All sensor processing, model inference, and alert logic run locally on the ESP32. The OLED display shows status without any cloud connection. The device generates zero network traffic, ensuring the nursery's data stays completely private.
Infant Care - ESP32 runs TensorFlow Lite anomaly detection model trained on sensor time series. Flags anomalies locally without cloud.
What's in this kit
Choose your assembly option:
- Soldering Kit - 25W soldering iron, 60/40 solder wire, flux, and small perfboard for permanent assembly.
- Breadboard Combo - 800-point full-size breadboard with 65-piece jumper wire pack for solderless prototyping.
Shipping Information
- Prepaid Orders: ₹75 for orders up to ₹999, FREE shipping above ₹999
- COD Orders: ₹125 shipping + ₹50 COD fee = ₹175 total
- Delivery Timeline: Dispatch in 1-2 days, delivery in 2-7 days depending on location
Returns & Warranty
- 7-Day Return: Manufacturing defects only (approval required)
- Warranty: 7 days from delivery
- Non-Returnable: Batteries, consumables, cut wires, clearance items