Citation

Pang, Ying Han and Raja Sekaran, Sarmela and Ooi, Shih Yin and Tan, Teck Guang (2022) Principal coefficient encoding for subject-independent human activity analysis. International Journal of Electrical and Computer Engineering (IJECE), 12 (4). p. 4391. ISSN 2088-8708

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Abstract

Tracking human physical activity using smartphones is an emerging trend in healthcare monitoring and healthy lifestyle management. Neural networks are broadly used to analyze the inertial data of activity recognition. Inspired by the autoencoder neural networks, we propose a layer-wise network, namely principal coefficient encoder model (PCEM). Unlike the vanilla neural networks which apply random weight initialization andback-propagation for parameter updating, an optimized weight initialization is implemented in PCEM via principal coefficient learning. This principal coefficient encoding allows rapid data learning with no back-propagation intervention and no gigantic hyperparameter tuning. In PCEM, the most principal coefficients of the training data are determined to be the network weights. Two hidden layers with principal coefficient encoding are stacked in PCEM for the sake of deep architecture design. The performance of PCEM is evaluated based on a subject-independent protocol where training and testing samples are from different users, with no overlapping subjects in between the training and testing sets. This subject-independent protocol can better assess the generalization of the model to new data. Experimental results exhibit that PCEM outperforms certain state-of-the-art machine learning and deep learning models, including convolutional neural network, and deep belief network. PCEM can achieve ~97% accuracy in subject-independent human activity analysis.

Item Type:	Article
Uncontrolled Keywords:	1D inertial motion data, Autoencoder, Deep analytic model, Principal coefficient encoding, Subject-independent
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Nov 2022 02:04
Last Modified:	03 Nov 2022 02:04
URII:	http://shdl.mmu.edu.my/id/eprint/10216

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