Tutorialtinymlwearablesaccelerometerarduino nano 33 ble sense
Arduino Nano Detects Squats With TinyML
7.1
A DIY project demonstrates counting squats using an Arduino Nano 33 BLE Sense and a TinyML model trained with Google's Tiny Motion Trainer. The device uses on-board accelerometer data, pairs over Bluetooth, and runs inference on-device after uploading TF4Micro-generated firmware. The guide provides step-by-step capture, training, code links, and placement instructions for thigh-mounted wearable testing.
Key Points
- 1Uses Arduino Nano 33 BLE Sense accelerometer and TinyML model to count squats reliably
- 2Enables on-device inference without cloud dependency, improving latency and preserving personal data privacy
- 3Provides a full training-to-deployment workflow using TF4Micro, Tiny Motion Trainer, and Arduino code
Scoring Rationale
High actionability and practical TinyML workflow drive score, limited by single-source DIY tutorial and limited novelty.
Sources
Public references used for this report.
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