Citation

Luwe, Yee Jia and Lee, Chin Poo and Lim, Kian Ming (2022) Wearable Sensor-Based Human Activity Recognition with Hybrid Deep Learning Model. Informatics, 9 (3). p. 56. ISSN 2227-9709

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Abstract

It is undeniable that mobile devices have become an inseparable part of human’s daily routines due to the persistent growth of high-quality sensor devices, powerful computational resources and massive storage capacity nowadays. Similarly, the fast development of Internet of Things technology has motivated people into the research and wide applications of sensors, such as the human activity recognition system. This results in substantial existing works that have utilized wearable sensors to identify human activities with a variety of techniques. In this paper, a hybrid deep learning model that amalgamates a one-dimensional Convolutional Neural Network with a bidirectional long short-term memory (1D-CNN-BiLSTM) model is proposed for wearable sensor-based human activity recognition. The one-dimensional Convolutional Neural Network transforms the prominent information in the sensor time series data into high level representative features. Thereafter, the bidirectional long short-term memory encodes the long-range dependencies in the features by gating mechanisms. The performance evaluation reveals that the proposed 1D-CNN-BiLSTM outshines the existing methods with a recognition rate of 95.48% on the UCI-HAR dataset, 94.17% on the Motion Sense dataset and 100% on the Single Accelerometer dataset.

Item Type:	Article
Uncontrolled Keywords:	Human activity recognition, convolutional neural network, long short-term memory, wearable sensor
Subjects:	Q Science > QP Physiology > QP351 Neurophysiology and Neuropsychology
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Nov 2022 04:02
Last Modified:	01 Nov 2022 04:02
URII:	http://shdl.mmu.edu.my/id/eprint/10608

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