Enhanced Human Activity Recognition Using Wearable Sensors via a Hybrid Feature Selection Method

Abstract

The study of human activity recognition (HAR) plays an important role in many areas such as healthcare, entertainment, sports, and smart homes. With the development of wearable electronics and wireless communication technologies, activity recognition using inertial sensors from ubiquitous smart mobile devices has drawn wide attention and become a research hotspot. Before recognition, the sensor signals are typically preprocessed and segmented, and then representative features are extracted and selected based on them. Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. In this paper, we propose to integrate bee swarm optimization (BSO) with a deep Q-network to perform feature selection and present a hybrid feature selection methodology, BAROQUE, on basis of these two schemes. Following the wrapper approach, BAROQUE leverages the appealing properties from BSO and the multi-agent deep Q-network (DQN) to determine feature subsets and adopts a classifier to evaluate these solutions. In BAROQUE, the BSO is employed to strike a balance between exploitation and exploration for the search of feature space, while the DQN takes advantage of the merits of reinforcement learning to make the local search process more adaptive and more efficient. Extensive experiments were conducted on some benchmark datasets collected by smartphones or smartwatches, and the metrics were compared with those of BSO, DQN, and some other previously published methods. The results show that BAROQUE achieves an accuracy of 98.41% for the UCI-HAR dataset and takes less time to converge to a good solution than other methods, such as CFS, SFFS, and Relief-F, yielding quite promising results in terms of accuracy and efficiency.

Studi Literatur Human Activity Recognition (HAR) Menggunakan Sensor Inersia

“…Sedangkan, perangkat pintar atau smart device mulai banyak digunakan untuk akuisisi data HAR karena penggunaannya yang meluas dan mudah. Perangkat pintar yang telah digunakan seperti smartphone [9], smartwatch, dan smartglasses [21]. Dari beberapa sensor yang ada pada perangkat pintar, akselerometer [9], giroskop [4] dan magnometer [14] merupakan sensor yang telah digunakan untuk pengambilan data HAR.…”

“…Perangkat pintar yang telah digunakan seperti smartphone [9], smartwatch, dan smartglasses [21]. Dari beberapa sensor yang ada pada perangkat pintar, akselerometer [9], giroskop [4] dan magnometer [14] merupakan sensor yang telah digunakan untuk pengambilan data HAR.…”

“…al. [3] [44] Multi-layer Perceptron (MLP) [7], [9], [20] Long-Short Term Memory (LSTM) [10], [55], [51] Deep Neural Network (DNN) [42] Deep Convolution Neural Networks (DCNN) [14], [46] Extreme Learning Machine (ELM) [60] Machine Learning Support Vector Machine (SVM) [8], [13], [14], [15], [44], [43], [48], [20], [58], [21], [41], [53], [61], [62], [51] Decision Tree (DT) [4], [63], [8], [43], [58], [21], [51] Genetic Algorithm (GA) [43] Artificial Neural Network (ANN) [4], [15], [44] Hidden Markov Model [7], [13] K Nearest Neighbor (KNN) [3], [8], [14], [20], [62], [63], [51] Extremely Randomized Tree [20] Linear Regression <...>…”

“…Pada metode ini, data diakusisi melalui alat yang ditempelkan pada bagian tubuh manusia. Kelebihan dari penggunaan metode ini adalah menanggulangi isu privasi, alat yang lebih ringan, lebih murah dan portable [9]. Sensor yang sering digunakan pada metode ini adalah akselerometer dan giroskop [10].…”

Migration-Based Moth-Flame Optimization Algorithm

“…During past decades, optimization techniques have been developed widely to solve complex problems that emerged in different fields of science, such as engineering [1][2][3][4][5][6][7][8][9], clustering [10][11][12][13][14][15][16][17][18], feature selection [19][20][21][22][23][24][25][26][27][28], and task scheduling [29][30][31][32]. Such optimization problems mainly involve characteristics such as linear/non-linear constraints, nondifferentiable functions, and a substantial number of decision variables.…”