Open AccessRandom Forest for Human Daily Activity Recognition
Author(s) -
Nurul Retno Nurwulan,
Gjergji Selamaj
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1655/1/012087
Subject(s) - random forest , naive bayes classifier , support vector machine , linear discriminant analysis , artificial intelligence , computer science , artificial neural network , activity recognition , machine learning , pattern recognition (psychology) , accelerometer , sitting , operating system , medicine , pathology
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.