PowerPotato/PowerPotato-Gesture-10KB

🥔 PowerPotato: 10KB Air-Gesture Shorts Controller-2k prameters

"Trained in 2 minutes inside Termux on a mobile CPU. Fits on a floppy disk. Controls YouTube Shorts with wrist flicks so you can eat greasy pizza in peace." 🍕📱✨

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⚡ The Potato Specs

• 🪶 Model Size: Only ~10 Kilobytes!
• 🥔 Hardware Target: Runs 100% locally on any potato phone, browser, or microcontroller CPU at 60+ FPS.
• 🔋 Battery Drain: Practically 0% (uses lightweight 3-axis accelerometer data).
• 🎯 Accuracy: 95%+ with confidence filtering and cooldown debouncing.

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🕹️ Supported Gestures & Actions

📱 Movement	🎯 Mapped Action
Flick Down ⬇️	Next Video / Scroll Down
Flick Up ⬆️	Previous Video / Scroll Up
Wrist Twist 🔄	Double-Tap Like (Heart ❤️)
Back Knock ✊	Pause / Play ⏸️
Normal Idle 🚶‍♂️	Ignored (Zero false alarms!)

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🧠 Architecture

A custom, microscopic 1D-CNN (Convolutional Neural Network) trained on 30 resampled timesteps of 3-axis accelerometer data (Ax, Ay, Az).

• Input shape: [1, 3, 30] (Batch, 3 channels, 30 timesteps)
• Output shape: [1, 5] (Class probabilities)

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💻 Quick Web Inference (JavaScript / ONNX Runtime Web)

import * as ort from 'onnxruntime-web';

// 1. Load the tiny model
const session = await ort.InferenceSession.create('gesture_model.onnx');

// 2. Feed rolling 30-sample accelerometer buffer [1, 3, 30]
const inputTensor = new ort.Tensor('float32', flatFloat32Array90, [1, 3, 30]);
const results = await session.run({ accel_input: inputTensor });

console.log("Prediction output:", results.probabilities.data);