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RideReport/ActivityPredictor: Low-power activity recognition library using accelerometer data and random forests. · GitHub

This repository was archived by the owner on Jun 9, 2025. It is now read-only.

Name		Name	Last commit message	Last commit date
json		json
models		models
.gitattributes		.gitattributes
.gitignore		.gitignore
FFTManager.cpp		FFTManager.cpp
FFTManager.h		FFTManager.h
FFTManager_opencv.cpp		FFTManager_opencv.cpp
LICENSE		LICENSE
README.md		README.md
RandomForestManager.cpp		RandomForestManager.cpp
RandomForestManager.h		RandomForestManager.h
RandomForestManager.swift		RandomForestManager.swift
Utility.cpp		Utility.cpp
Utility.h		Utility.h

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ActivityPredictor

Low-power activity recognition library using accelerometer data and random forests.

For example usage, see: https://github.com/RideReport/RideRecorderApp

To train your own model, see: https://github.com/RideReport/ActivityPredictionPipeline

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Low-power activity recognition library using accelerometer data and random forests.

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