B-HAR-HumanActivityRecognition/B-HAR_Baseline-Human-Activity-Recognition
This package focuses on the definition, standardization, and development of workflow for human activity recognition in depth analysis.
This framework helps researchers and data scientists working with sensor data to understand and compare different human activity recognition (HAR) methods. You provide raw sensor data from human activities, along with a configuration file, and it outputs detailed statistics and benchmark results for various machine learning models. It's designed for those who need to evaluate the effectiveness of different HAR approaches on specific datasets.
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Use this if you are a researcher or data scientist evaluating and comparing different algorithms for human activity recognition using sensor data.
Not ideal if you are looking for a pre-trained model for immediate deployment in an application or if you don't work with human activity sensor data.
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Jupyter Notebook
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MIT
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Last pushed
May 20, 2021
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