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https://github.com/arrhythmia-detection/optimizedmlpfloat32

Deploys a simple MLP to ESP32-S3 chip to do arrhythmia classification using Chapman ECG dataset
https://github.com/arrhythmia-detection/optimizedmlpfloat32

arrhythmia-classification chapman-ecg esp32-arduino esp32-s3 tensorflow tensorflow-lite tensorflow-lite-micro tflm-arduino

Last synced: 8 days ago
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Deploys a simple MLP to ESP32-S3 chip to do arrhythmia classification using Chapman ECG dataset

Host: GitHub
URL: https://github.com/arrhythmia-detection/optimizedmlpfloat32
Owner: arrhythmia-detection
Created: 2025-01-09T15:54:27.000Z (20 days ago)
Default Branch: main
Last Pushed: 2025-01-21T04:30:23.000Z (9 days ago)
Last Synced: 2025-01-21T05:24:23.052Z (9 days ago)
Topics: arrhythmia-classification, chapman-ecg, esp32-arduino, esp32-s3, tensorflow, tensorflow-lite, tensorflow-lite-micro, tflm-arduino
Language: C++
Homepage:
Size: 32.2 KB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

TF MLP Float 32 Model Deployment on ESP32-S3

Tools

This is a standard [platformio](https://platformio.org/) project for `esp32-s3-devkitc-1`
board which deploys a vanilla [MLP](include/optimized_mlp_float32.h) (*non quantized and utilizes author extracted features*) to ESP32-S3 chip
and collects necessary performance metrics (see [Collected Metrics](CollectedMetrics.md)).
For model training etc., please refer to
[this repository](https://github.com/arrhythmia-detection/ArrhythmiaDetectionModels).