Human Activity Recognition using Extremely Fast Decision Tree Classifier
Keywords:
Decision Tree, HAR, K Nearest Neighbour, Logistic regression, Naïve Baye’s, PAMAP2Abstract
Human activity recognition (HAR) is a subfield of artificial intelligence that focuses on identifying and understanding human actions and movements using various sensors and data analysis techniques. It's like deciphering the language of our physical movements. These systems are so demanding these days as they have a wide range of applications in various fields including: healthcare, fitness and sports, smart homes & buildings, security and surveillance and human-computer interaction. As HAR technology continues to evolve, it's becoming increasingly accurate and sophisticated, opening up even more possibilities for the future. In this paper, we have explored diverse methods to assess the influence of chosen classifiers on training and testing procedures. To evaluate the model, we have used the data from the popularly known PAMAP2 dataset and five different classification techniques have been used. The Extremely Fast Decision Tree (EFDT) emerged as the fastest performing algorithm in attaining 99.6% accuracy in minimum execution time i.e. 20 minutes.
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