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TinyML on Arduino: Building Interactive Voice and Gesture AI Projects
Paperback
Publisher: Independently Published
Published: May 6 2026
Pages: 166
Weight: 0.66
Height: 0.35 Width: 7.00 Depth: 10.00
Language: English
Artificial Intelligence is moving to the edge. TinyML on Arduino: Building Interactive Voice and Gesture AI Projects is your comprehensive guide to embedding complex machine learning models directly onto resource-constrained microcontrollers. Say goodbye to network latency, high bandwidth costs, and privacy concerns, and say hello to autonomous, low-power AI.
Authored by Willa Westwood, this definitive, hands-on handbook bridges the gap between data science and embedded engineering. Whether you are a maker, a hardware developer, or a seasoned C++ programmer looking to master edge computing, this book provides the exact blueprints you need to transform raw sensor data into actionable intelligence.
What You Will Discover Inside:- Hardware Mastery: Navigate the capabilities of the ARM Cortex-M architecture, evaluating the classic Arduino Nano 33 BLE Sense against the cutting-edge Arduino VENTUNO Q and its Neural Processing Unit (NPU).
- Audio & Speech Recognition: Interface with PDM microphones, apply Digital Signal Processing (DSP) to generate Mel-spectrograms, and build a custom Keyword Spotting (KWS) engine for wake-word detection.
- Motion & Gesture Tracking: Harness 6-axis IMUs to capture physical movement, apply low-pass filters for gravity compensation, and train Convolutional Neural Networks (CNNs) to recognize complex 3D alphanumeric gestures.
- Proximity & Touchless Interfaces: Develop sterile, infrared-driven smart switches using advanced IR photodiode logic, recognizing hover and hold states for high-noise environments.
- Multimodal Fusion: Combine audio and motion inference in parallel pipelines to create a context-aware AI assistant that responds to voice and gesture simultaneously.
- Model Optimization: Squeeze massive AI power into tiny silicon using Post-Training Int8 Quantization, memory profiling, and precise static buffer allocation.
- Power & Deployment: Implement Wake-on-Sound and Wake-on-Motion hardware interrupts to maximize battery life, estimate LiPo vs. coin cell runtimes, and transition your project from a breadboard to a custom PCB.
Stop relying on theory. This guide walks you through complete, deployable projects including an Air-Gesture Media Controller, an AI-Driven Security Glass Break Monitor, a Sterile Touchless Smart Switch, and a fully fused Multimodal Assistant.
Stop sending your data to the cloud. Equip your devices with the power to think for themselves. Scroll up and grab your copy today to start building the next generation of Edge AI technology!
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