{"product_id":"kit-nano-neural-network-on-hardware","title":"Nano Neural Network on Hardware Kit with Arduino Nano + MPU6050","description":"\u003ch1\u003eDeploy a Neural Network on Hardware: Arduino Nano + MPU6050 Real-Time Classifier Kit\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 6-8 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e TinyML model deployment\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eYou’ll train a compact neural network on your PC, quantize it for an 8-bit microcontroller, and flash it onto an Arduino Nano. Once deployed, the system reads real-time motion data from the MPU6050, runs inference locally, and classifies gestures or patterns with a validated 96% accuracy—no cloud, no latency, just edge AI in a matchbox-sized device.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eA battery-powered hardware neural network that captures accelerometer and gyroscope streams, processes them through a trained TinyML model, and displays the predicted class on a 0.96-inch OLED. The final assembly fits inside an ABS enclosure, making it a portable demo for smart sensor applications or a hackathon-ready proof-of-concept.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eTrain a feedforward neural network in Python and export it to TensorFlow Lite for microcontrollers\u003c\/li\u003e\n  \u003cli\u003eQuantize floating-point weights to 8-bit integers while preserving classification accuracy\u003c\/li\u003e\n  \u003cli\u003eInterface MPU6050 and DHT22 sensors with Arduino Nano and fuse data in real time\u003c\/li\u003e\n  \u003cli\u003eDebug on-device inference pipelines using serial output and OLED visualization\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eArduino Nano\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMPU6050\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDHT22\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e0.96in OLED\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e3.7V LiPo 500mAh\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTP4056 Module\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e4.7kΩ Resistors\u003c\/td\u003e\n\u003ctd\u003e5\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e100nF Capacitors\u003c\/td\u003e\n\u003ctd\u003e5\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePCB Prototype Board\u003c\/td\u003e\n\u003ctd\u003e2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eABS Enclosure Box\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eSoldering Iron\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eSolder Wire\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eB.Tech ECE and EEE students working on edge AI electives, Smart India Hackathon teams needing a proven sensor-AI base, and hobbyists from IIT, NIT, VIT, or BITS Pilani who want to move beyond Arduino blink sketches into real on-device machine learning. It’s designed for those comfortable with Arduino IDE and basic Python who are ready to bridge the PC-to-microcontroller gap.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery 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.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat PC software do I need to train the neural network?\u003c\/summary\u003e\u003cp\u003eYou’ll use Python with TensorFlow and the TensorFlow Lite Converter. The AI companion includes a ready-to-run Colab notebook that generates the model. If you hit a snag, WhatsApp support can guide you through the training flow.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow is 96% accuracy achieved on an 8-bit microcontroller?\u003c\/summary\u003e\u003cp\u003eThe provided model is pre-quantized and validated on real MPU6050 data collected from this exact sensor. The AI companion explains how quantization-aware training preserves accuracy; you can verify it on your own collected samples.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat if the deployment fails or the OLED shows garbage?\u003c\/summary\u003e\u003cp\u003eCheck I2C wiring and baud rate first—the AI companion’s diagnostic mode reads sensor registers live. If that doesn’t fix it, share a photo on WhatsApp and we’ll help isolate the issue, usually a cold joint or mismatched library version.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I retrain the model for different gestures?\u003c\/summary\u003e\u003cp\u003eYes. The kit includes a data collection script and the AI companion walks you through recording new motion classes, retraining, and re-quantizing. You’ll have a custom classifier by the end of the build.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eTiny ML model trained on PC deployed to Nano. Classifies sensor data in real time with 96% accuracy.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/nano-r3-ch340-development-board-arduino-compatible-no-cable\"\u003eArduino Nano\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/mpu6050-imu-module-6-axis-gyro-accelerometer-for-arduino\"\u003eMPU6050\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/dht22-temperature-humidity-sensor-module-accurate-readings\"\u003eDHT22\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/096in-oled-display-128x64-i2cspi-for-arduino-raspberry-pi\"\u003e0.96in OLED\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/37v-30c-1000mah-lipo-battery-yy802542-for-drones\"\u003e3.7V LiPo 500mAh\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/tp4056-1a-li-ion-charger-module-with-protection-micro-usb\"\u003eTP4056 Module\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/resistor-variety-pack-100-pcs-10-values-14w-carbon-film\"\u003e4.7kΩ Resistors\u003c\/a\u003e x5\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/capacitor-variety-pack-6-values-100nf-to-470uf-30-pieces\"\u003e100nF Caps\u003c\/a\u003e x5\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/esp-wroom-32-breakout-board-pcb-55x52mm\"\u003ePCB Prototype Board\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-4-abs-case-enclosure-redwhite\"\u003eABS Enclosure Box\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/soldering-kit-25w-with-solder-wire-flux-paste-compoden\"\u003eSoldering Iron\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/soldering-kit-25w-with-solder-wire-flux-paste-compoden\"\u003eSolder Wire\u003c\/a\u003e\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Nano Neural Network on Hardware Kit with Arduino Nano + MPU6050?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Nano Neural Network on Hardware Kit with Arduino Nano + MPU6050 includes all components needed: Arduino Nano, MPU6050, DHT22, 0.96in OLED, 3.7V LiPo 500mAh and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Nano Neural Network on Hardware Kit with Arduino Nano + MPU6050?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-21. Tiny ML model trained on PC deployed to Nano. Classifies sensor data in real time with 96% accuracy. Estimated build time is 6-8 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Nano Neural Network on Hardware Kit with Arduino Nano + MPU6050 online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Nano Neural Network on Hardware Kit with Arduino Nano + MPU6050 is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Nano Neural Network on Hardware Kit with Arduino Nano + MPU6050\",\n  \"description\": \"Tiny ML model trained on PC deployed to Nano. Classifies sensor data in real time with 96% accuracy.\",\n  \"sku\": \"CDN-KIT-2272\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-nano-neural-network-on-hardware\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"3830\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"Mini \u0026 Nano Form Factor\"\n}\n\u003c\/script\u003e\u003cp\u003e\u003cstrong\u003eChoose your assembly option:\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSoldering Kit\u003c\/strong\u003e — 25W soldering iron, 60\/40 solder wire, flux, and small perfboard for permanent assembly.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBreadboard Combo\u003c\/strong\u003e — 800-point full-size breadboard with 65-piece jumper wire pack for solderless prototyping.\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Compoden","offers":[{"title":"Clone \/ Soldering Kit","offer_id":53469344301421,"sku":"CDN-KIT-2272-CL-SLD","price":3140.0,"currency_code":"INR","in_stock":true},{"title":"Clone \/ Breadboard Combo","offer_id":53469344334189,"sku":"CDN-KIT-2272-CL-BB","price":2610.0,"currency_code":"INR","in_stock":true},{"title":"R3 Original \/ Soldering Kit","offer_id":53469344366957,"sku":"CDN-KIT-2272-R3-SLD","price":5580.0,"currency_code":"INR","in_stock":true},{"title":"R3 Original \/ Breadboard Combo","offer_id":53469344399725,"sku":"CDN-KIT-2272-R3-BB","price":5040.0,"currency_code":"INR","in_stock":true},{"title":"R4 WiFi \/ Soldering Kit","offer_id":53469344432493,"sku":"CDN-KIT-2272-R4-SLD","price":4900.0,"currency_code":"INR","in_stock":true},{"title":"R4 WiFi \/ Breadboard Combo","offer_id":53469344465261,"sku":"CDN-KIT-2272-R4-BB","price":4370.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-nano-neural-network-on-hardware.png?v=1781947962","url":"https:\/\/compoden.com\/products\/kit-nano-neural-network-on-hardware","provider":"Compoden","version":"1.0","type":"link"}