Citation

Raja Sekaran, Sarmela and Pang, Ying Han and Ooi, Shih Yin (2023) Cascading Global and Local Deep Features for Smartphone-based Human Activity Classification. In: Proceedings of the 2023 12th International Conference on Software and Computer Applications, 23 - 25 Feb 2023, Pahang, Malaysia.

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Abstract

The advancement in technology with multiple sensors embedded in smartphones results in the widespread of smartphones in the applications of human activity analysis and recognition. This promotes a variety of ambient assistive living applications, such as fitness tracking, fall detection, home automation system, healthcare monitoring etc. In this paper, a human activity recognition based on the amalgamation of statistical global features and local deep features is presented. The proposed model adopts temporal convolutional architecture to extract the long-range temporal patterns from the inertial activity signals captured by smartphones. To further enrich the information, statistical features are computed so that the global features of the time series data are encoded. Next, both global and local deep features are combined for classification. The proposed model is evaluated by using WISDM and UCI HAR datasets for user-dependent and independent protocols, respectively, to ensure its feasibility as user-dependent and independent HAR solutions. The obtained empirical results exhibit that the proposed model is outperforming the other existing deep learning models on both user-dependent and independent testing protocols.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Human Activity Recognition, Temporal Convolutional Network, Statistical Features, Global Features, Temporal Patterns
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	28 Jul 2023 08:52
Last Modified:	28 Jul 2023 08:52
URII:	http://shdl.mmu.edu.my/id/eprint/11569

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