Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques Article Swipe
Huaijun Wang
,
Jing Zhao
,
Junhuai Li
,
Ling Tian
,
Pengjia Tu
,
Ting Cao
,
Yang An
,
Kan Wang
,
Shancang Li
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1155/2020/2132138
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1155/2020/2132138
Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring. Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2020/2132138
- https://downloads.hindawi.com/journals/scn/2020/2132138.pdf
- OA Status
- hybrid
- Cited By
- 111
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3044454104
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- OpenAlex ID
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https://openalex.org/W3044454104Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2020/2132138Digital Object Identifier
- Title
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Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning TechniquesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-07-27Full publication date if available
- Authors
-
Huaijun Wang, Jing Zhao, Junhuai Li, Ling Tian, Pengjia Tu, Ting Cao, Yang An, Kan Wang, Shancang LiList of authors in order
- Landing page
-
https://doi.org/10.1155/2020/2132138Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/scn/2020/2132138.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/scn/2020/2132138.pdfDirect OA link when available
- Concepts
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Computer science, Activity recognition, Convolutional neural network, Deep learning, Wearable computer, Artificial intelligence, Machine learning, Wearable technology, Identification (biology), Embedded system, Botany, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
111Total citation count in OpenAlex
- Citations by year (recent)
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2025: 13, 2024: 23, 2023: 29, 2022: 25, 2021: 20Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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