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ESP32 Athlete Tracker Kit - Build a PPG Glucose Trend Monitor

SKU: CDN-KIT-3246-SLD Brand: Compoden Category: Electronics > Wearables & Health > Project Kits
Rs. 4,310.00
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ESP32 Athlete Performance Tracker Kit - Build a Wearable PPG Glucose Trend Monitor

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Difficulty: Advanced Build Time: 15-20 hrs Age: 25+ Skill: Biosignal Processing & Wearable Design

For an athlete pushing endurance limits, subtle shifts in blood glucose can make or break a performance. This advanced ESP32 kit lets you build a custom wearable that uses dual near-infrared LEDs and photodiodes to extract photoplethysmography (PPG) waveform features those shifts leave behind-without a traditional continuous glucose monitor. You'll design a multi-wavelength optical front-end, sample weak biosignals, and train your own algorithms to correlate PPG dynamics with metabolic trends seen during training, recovery, and competition.

What You'll Build

A fully functional finger-worn optical sensor module streaming raw PPG data to the ESP32. Waveforms are timestamped via the DS3231 real-time clock, logged to a MicroSD card, and summarised on a 0.96-inch OLED display showing real-time trend indicators. The prototype becomes your personal biosignal capture rig, ready to feed long-term glucose-correlation analysis or later be packaged in the included enclosure as a field-ready wearable.

What You'll Learn

  • Designing dual-wavelength PPG front-ends with transimpedance amplification using the LM358 op-amp to extract minute light-induced currents from the BPW34 photodiodes.
  • Sampling and digital filtering of physiological signals on the ESP32, rejecting motion artefacts and ambient light interference to isolate clean waveform morphology.
  • Extracting waveform features such as AC/DC ratio, rise time, and dicrotic notch characteristics that research has linked to glucose-related metabolic changes.
  • Logging time-stamped sensor data to a MicroSD module for offline analysis, allowing you to build statistical models or train machine learning pipelines for trend prediction.

Kit Contents

Component Quantity
ESP32 Dev Board x1
MAX30102 x1
850nm LED x2
BPW34 Photodiode x2
LM358 Op-Amp x2
DS3231 RTC x1
MicroSD Module x1
0.96in OLED x1
10k? Resistors x10
100nF Caps x10
3.7V LiPo 1000mAh x1
TP4056 Module x1
PCB Prototype Board x3
Enclosure Box x1
Soldering Iron x1
Solder Wire x1

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

Designed for B.Tech and M.Tech students in ECE and EEE final-year projects, researchers at IITs and NITs exploring non-invasive biosensing, and Smart India Hackathon teams building athlete performance monitors. It also suits sports scientists and hardware engineers who want a reproducible platform for capturing PPG-glucose correlation data before moving to clinical validation.

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 on the enclosure to launch the AI companion, which holds step-by-step diagnostics for this exact circuit. If you still need a human, message us on WhatsApp; we answer within hours.

Does this kit measure blood glucose levels directly?

No, it extracts PPG waveform features that research correlates with glucose trends. The output is not a medical-grade reading and should not be used for clinical decisions-it's a development platform for sports performance research.

What software skills are required?

You'll program the ESP32 in Arduino IDE (C++) for data acquisition and real-time display. For offline feature extraction and trend modelling, you can use Python with libraries like NumPy and scikit-learn-the companion AI includes starter code.

Can I train a machine learning model with the logged data?

Absolutely. The timestamped CSV files on the MicroSD card are ready for feature extraction. With enough controlled trials, you can build a custom predictor, though clinical-grade accuracy requires rigorous calibration and validation beyond the kit's scope.

Sports Performance - Near-IR LED + photodiode on finger measures photoplethysmography waveform features correlated with glucose trends.

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

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