“…Until now, various machine learning methods have been utilized for HoAR such as Naive Bayes (NB) (Mao et al, 2021;Kamminga et al, 2019a;Lee et al, 2016), Support Vector Machine (SVM) (Mao et al, 2021;Alves et al, 2021;Braganca et al, 2020;Lee et al, 2018Lee et al, , 2016, Decision Tree (DT) (Mao et al, 2021;Casella et al, 2020;Braganca et al, 2020;Lee et al, 2018), Random Forest (RF) (Eerdekens et al, 2021;Alves et al, 2021;Braganca et al, 2020), K-Nearest Neighbors (KNN) (Casella et al, 2020;Lee et al, 2018), Neural Network (NN) (Alves et al, 2021;Casella et al, 2020;Braganca et al, 2020;Gutierrez-Galan et al, 2018;Lee et al, 2016), Quadratic Discriminant Analysis (QDA) (Braganca et al, 2020), Linear Discriminant Analysis (LDA) (Braganca et al, 2020;Lee et al, 2018), and Extreme Learning Machine (ELM) (Lee et al, 2018). Furthermore, some deep learning methods have been investigated for HoAR such as Convolutional Neural Network (CNN) (Mao et al, 2021;Eerdekens et al, 2021;Alves et al, 2021;Eerdekens et al, 2020), Long-Short Term Memory (LSTM (Nunes et al, 2021;Braganca et al, 2020), Auto-Encoder (AE) (Lee et al, 2018), and Recurrent Neural Network (RNN) (Nunes et al, 2021).…”