Random Forest for Human Daily Activity Recognition

Machine learning classifiers are often used to evaluate the predicting accuracy of human activity recognition. This study aimed to evaluate the performance of random forest (RF) compared to other classifiers with considering the time taken to build the models. Human activity daily living data, namel...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-10, Vol.1655 (1), p.12087
Main Authors: Nurwulan, Nurul Retno, Selamaj, Gjergji
Format: Article
Language:English
Subjects:
Accelerometers
Artificial neural networks
Classifiers
Discriminant analysis
Human activity recognition
Learning theory
Machine learning
Moving object recognition
Performance evaluation
Physics
Support vector machines
Walking
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Summary:Machine learning classifiers are often used to evaluate the predicting accuracy of human activity recognition. This study aimed to evaluate the performance of random forest (RF) compared to other classifiers with considering the time taken to build the models. Human activity daily living data, namely walking, walking upstairs, walking downstairs, sitting, standing, and lying down were collected from smartphone-based accelerometer with sampling frequency of 50Hz. The dataset was evaluated using artificial neural network (ANN), k-nearest neighbors (KNN), linear discriminant analysis (LDA), naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The results of the study showed that RF indeed predicted the activities with the highest accuracy. However, the time taken to build the models using RF was the second-longest after ANN.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1655/1/012087