2019 International Conference on Data Science and Engineering (ICDSE) 2019
DOI: 10.1109/icdse47409.2019.8971476
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Human Activity Recognition using Deep Neural Network

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Cited by 10 publications
(4 citation statements)
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“…Analisis Komponen Utama (PCA) umum digunakan untuk menemukan jumlah data yang lebih kecil dan tidak berkorelasi untuk mengurangi dimensi fitur [1]. Teknik klasifikasi lain yang diurutkan berdasarkan perkembangan historis adalah K-Nearest Neighbor (KNN) classifier [1] dan SVM [1,2,7,10,13], Hyper-sphere Multi-class SVM [6], Naive-Bayes [9,10], Random Forest [10], Multilayer Perceptron (MLP) [7,10], Convolutional Neural Networks (CNN) [4,8,13], LSTM [12,14,15,16,17], ResNet [20], Transformer [21,22,23]. Kompleksitas komputasi dari algoritma-algoritma di atas dalam memprediksi satu sampel berdasarkan yang tertinggi hingga yang terendah adalah O(kN2d) untuk KNN Video input diekstraksi menjadi beberapa baris frame/gambar untuk analisis pada tahap preprocessing.…”
Section: Metode Penelitianunclassified
“…Analisis Komponen Utama (PCA) umum digunakan untuk menemukan jumlah data yang lebih kecil dan tidak berkorelasi untuk mengurangi dimensi fitur [1]. Teknik klasifikasi lain yang diurutkan berdasarkan perkembangan historis adalah K-Nearest Neighbor (KNN) classifier [1] dan SVM [1,2,7,10,13], Hyper-sphere Multi-class SVM [6], Naive-Bayes [9,10], Random Forest [10], Multilayer Perceptron (MLP) [7,10], Convolutional Neural Networks (CNN) [4,8,13], LSTM [12,14,15,16,17], ResNet [20], Transformer [21,22,23]. Kompleksitas komputasi dari algoritma-algoritma di atas dalam memprediksi satu sampel berdasarkan yang tertinggi hingga yang terendah adalah O(kN2d) untuk KNN Video input diekstraksi menjadi beberapa baris frame/gambar untuk analisis pada tahap preprocessing.…”
Section: Metode Penelitianunclassified
“…In a study [4], traditional algorithms such as naïve Bayes, hidden Markov model (HMM), hidden semi Markov model (HSMM) and conditional random fields (CRFs) are compared with deep learning models on raw sensory data and validated that those deep learning models outperformed the best result by 40%. Similar deep learning approaches have been previously employed for recognition in [5], [6]. The concept of combining AdaBoost with other classifiers (C4.5, multilayer perceptron and logistic regression (LR)) was introduced in [7].…”
Section: Related Workmentioning
confidence: 99%
“…On the one hand, the recognition could be done by extracting handcrafted time-domain and frequency-domain features from raw signals, to feed classifiers like k-Nearest Neighbors (KNN), Random Forest (RF), and Deep Neural Network (DNN) [22,30,36]. Moreover, Random Subspace (RS) technique has been proposed to process Quaternions and Euler angles [37].…”
Section: Towards Human Activity Recognitionmentioning
confidence: 99%
“…The input of CNN and LSTM is raw data (time-series representing Euler angles). Besides, a large set of timedomain and frequency-domain features (proposed in [22,30,36]) were extracted from Euler angles to feed DNN (same architecture as in [22]), KNN ( =5), and RF (100 estimators). Since large feature vectors could mislead the machine, they were subject to a feature selection/dimensionality reduc-…”
Section: Comparison With State-of-the-art Techniquesmentioning
confidence: 99%