Multi-Sensor Fusion Fall Detection Wearable Kit with ESP32 + MPU6050
ESP32-S3 Multi-Sensor Fall Detection Wearable Kit – AI-Powered Safety on Your Wrist
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
What if a wearable could distinguish between bending down and a dangerous fall—and automatically call for help? This kit lets you build exactly that. Using the ESP32-S3’s neural network accelerator, a TensorFlow Lite model classifies motion data from the MPU6050 fused with SpO2 pulse signals from the MAX30102. When a fall is detected, the SIM800L GSM module sends an SMS with GPS coordinates, creating a vital safety net for elderly care, lone-worker monitoring, or remote patient observation.
What You'll Build
A wrist-worn device that continuously monitors motion and vitals. An AI model running on the ESP32-S3 distinguishes falls from everyday activities like sitting or walking. On detection, the firmware triggers an SMS alert with the wearer's real-time GPS location to a pre-programmed number—providing autonomy and peace of mind without requiring a smartphone or constant cloud connectivity.
What You'll Learn
- Deploy a TensorFlow Lite model on ESP32-S3 for real-time motion classification
- Fuse MPU6050 inertial data with MAX30102 SpO2 readings to improve fall recognition accuracy
- Integrate NEO-6M GPS and SIM800L GSM for location-aware emergency alerts
- Design wearable‑grade power management with LiPo battery, buck converter, and OLED status display
Kit Contents
| Component | Quantity |
|---|---|
| ESP32-S3 Dev Board | 1 |
| MPU6050 | 1 |
| MAX30102 | 1 |
| NEO-6M GPS | 1 |
| SIM800L GSM | 1 |
| LM2596 Buck Converter | 1 |
| 3.7V LiPo 1000mAh | 1 |
| TP4056 Module | 1 |
| 0.96in OLED | 1 |
| 1000µF 25V Caps | 2 |
| 100nF Caps | 10 |
| PCB Prototype Board | 2 |
| Velcro Wristband | 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 | Pre-tested as a system |
| Build support | Forums and scattered tutorials | AI companion trained on this exact project |
| Time to first working build | Days of debugging | Hours, with step-by-step guidance |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
B.Tech ECE/EEE students developing IoT‑healthcare final‑year projects, Smart India Hackathon teams tackling wearable safety solutions, and embedded AI enthusiasts who want to run TensorFlow Lite Micro on a real, low‑power wearable. M.Tech researchers and NIT/VIT project groups will find the sensor‑fusion dataset and pre‑trained model equally valuable as a starting point for further innovation.
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?
The AI companion provides instant, step‑by‑step debugging specific to this circuit. You can also reach us on WhatsApp for a human review of your setup.
Does the kit include the AI model?
Yes, you receive a pre‑trained TensorFlow Lite fall‑detection model optimized for the ESP32‑S3, along with the training pipeline code so you can retrain with your own motion data.
Can I change the SOS message or fall sensitivity?
Absolutely. The Arduino‑compatible firmware is fully open and commented; you can edit the SMS template, add multiple emergency contacts, and fine‑tune the classification threshold to balance false positives and latency.
What programming background is required?
Familiarity with C/C++ and the Arduino IDE or PlatformIO is recommended. The guide walks you through every upload and calibration step, and the AI companion answers code‑level questions specific to this board and sensor stack.
MPU6050 + MAX30102 SpO2 + GPS fused by ESP32-S3 TFLite model. Classifies fall vs normal activity. GSM SOS.
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