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Hybrid classification algorithm improves recognition of human activity

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Research in the International Journal of Computer Applications in Technology introduces a hybrid classification algorithm aimed at improving the recognition of human activities using smartphone data. The work could have implications for various fields, including health care and personal support.

Ahmad Taher Azar of both the Prince Sultan University in Riyadh, Saudi Arabia and Benha University in Benha, Egypt, hoped to demonstrate a tool for accurately categorizing six distinct human activities: lying, sitting, standing, walking, walking upstairs, and walking downstairs. He used supervised machine learning techniques that merged Random Forest Decision Trees (RFDT) and Neural Networks (NN) to this end.

The hybrid approach was able to classify six human activities with an accuracy rate of 96%. This surpasses the performance of individual machine learning techniques like NN or RFDT and is comparable with the current state-of-the-art methods. However, what sets this algorithm apart is its ...

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