Wearable Sensor-Based Human Activity Recognition with Hybrid Deep Learning Model
Open accessAbstract
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.
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Models such as CNN-LSTM [24] and CNN-BiLSTM [18] utilize standard convolutions, which restrict their capacity to effectively capture a range of spatial scales.
“In contrast, models such as CNN-LSTM [24] and CNN-BiLSTM [18] utilize standard convolutions, which restrict their capacity to effectively capture a range of spatial scales.”
From Adaptive temporal attention mechanism and hybrid deep CNN model for wearable sensor-based human activity recognition (2025) · cites this paper as [18]
Previous studies have amalgamated CNN, RNN, and attention mechanisms.
“Previous studies, including [10, 18, 23, 24], have amalgamated convolutional neural networks (CNN), recurrent neural networks (RNN), and attention mechanisms;”
From Adaptive temporal attention mechanism and hybrid deep CNN model for wearable sensor-based human activity recognition (2025) · cites this paper as [18]
Accuracy is an inadequate metric for comparison with other human activity recognition models because it merely reflects the ratio of correctly classified activities to the total activities in the test dataset.
“This study aims to identify human activities through accelerometer and gyroscope data; thus, accuracy is an inadequate metric for comparison with other human activity recognition models, as it merely reflects the ratio of correctly classified activities to the total activities in the test dataset.18”
From Adaptive temporal attention mechanism and hybrid deep CNN model for wearable sensor-based human activity recognition (2025) · cites this paper as [18]
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Flagged first, then by citations
Research works printed but not linked: 1
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Cited by
Sorted by citations
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- Authors
- Luwe, Y. J., Lee, C. P., & Lim, K. M.
- Affiliations
- Multimedia University (MY)
- Corresponding author
- Lee, Chin Poo
- Venue
- Informatics
- Publisher
- MDPI AG
- Published
- 2022-07-31
- Volume
- 9
- Issue
- 3
- Pages
- 56
- ISSN
- 2227-9709 (ISSN-L)
- OpenAlex
- W4289172806
- Cited by
- 92
- References
- 36
- Citations by year
- 2026: 13 · 2025: 19 · 2024: 26 · 2023: 29 · 2022: 5
- FWCI
- 6.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Informatics (publishedVersion, cc-by)
- Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fundamental Research Grant Scheme of the Ministry of Higher Education (FRGS/1/2021/ICT02/MMU/02/4, MMUI/220021)
- Multimedia University Internal Research (FRGS/1/2021/ICT02/MMU/02/4, MMUI/220021)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- human activity recognition, wearable sensors, 1D convolutional neural network, bidirectional LSTM, hybrid deep learning model, sensor time series, Internet of Things, UCI HAR dataset, motion sensing
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