A lightweight neural network framework using linear grouped convolution for human activity recognition on mobile devices
Abstract Human activity recognition (HAR) has played an indispensable role in ubiquitous computing scenario, from smart homes to game console designing, elderly care, and fitness tracking. It is very hard to manually extract most suitable activity features from raw sensor time series. Due to an obvi...
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| Autor*in: |
Liu, Tianyi [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Springer US, 1987, 78(2021), 5 vom: 25. Okt., Seite 6696-6716 |
|---|---|
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volume:78 ; year:2021 ; number:5 ; day:25 ; month:10 ; pages:6696-6716 |
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| DOI / URN: |
10.1007/s11227-021-04140-5 |
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OLC2078292451 |
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| 520 | |a Abstract Human activity recognition (HAR) has played an indispensable role in ubiquitous computing scenario, from smart homes to game console designing, elderly care, and fitness tracking. It is very hard to manually extract most suitable activity features from raw sensor time series. Due to an obvious advantage, convolutional neural networks (CNNs) that can extract features automatically have been widely utilized for activity recognition. Despite exceptional performance, CNNs are computation-intensive and memory-demanding algorithms because of a large number of internal parameters. As a result, the research attention in HAR implementations over resource-limited embedded platforms has turned to computationally lightweight convolution architectures. In this paper, we offer a contribution in the direction. Simple linear transformations with low cost are combined with a convolution-based HAR classifier to decrease overall computational/memory overhead and, simultaneously, which establishes an efficient classification method with satisfactory performance. Our new method is evaluated against standard convolution-based and residual counterparts, over several popular HAR datasets for algorithm benchmarking. Experimental results verify that, the cheap linear operations can significantly reduce computational and memory cost, and meanwhile producing satisfactory recognition performance, which is able to ensure faster inference on mobile devices. Our new method could be a strong candidate for real HAR implementations on embedded platforms with limited computing resources. | ||
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| 700 | 1 | |a Quan, Weiming |4 aut | |
| 700 | 1 | |a Zhang, Lei |0 (orcid)0000-0001-8749-7459 |4 aut | |
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10.1007/s11227-021-04140-5 doi (DE-627)OLC2078292451 (DE-He213)s11227-021-04140-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Liu, Tianyi verfasserin aut A lightweight neural network framework using linear grouped convolution for human activity recognition on mobile devices 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Human activity recognition (HAR) has played an indispensable role in ubiquitous computing scenario, from smart homes to game console designing, elderly care, and fitness tracking. It is very hard to manually extract most suitable activity features from raw sensor time series. Due to an obvious advantage, convolutional neural networks (CNNs) that can extract features automatically have been widely utilized for activity recognition. Despite exceptional performance, CNNs are computation-intensive and memory-demanding algorithms because of a large number of internal parameters. As a result, the research attention in HAR implementations over resource-limited embedded platforms has turned to computationally lightweight convolution architectures. In this paper, we offer a contribution in the direction. Simple linear transformations with low cost are combined with a convolution-based HAR classifier to decrease overall computational/memory overhead and, simultaneously, which establishes an efficient classification method with satisfactory performance. Our new method is evaluated against standard convolution-based and residual counterparts, over several popular HAR datasets for algorithm benchmarking. Experimental results verify that, the cheap linear operations can significantly reduce computational and memory cost, and meanwhile producing satisfactory recognition performance, which is able to ensure faster inference on mobile devices. Our new method could be a strong candidate for real HAR implementations on embedded platforms with limited computing resources. Sensor Convolutional neural networks Activity recognition Deep learning Linear grouped convolution Wang, Shuoyuan aut Liu, Yue aut Quan, Weiming aut Zhang, Lei (orcid)0000-0001-8749-7459 aut Enthalten in The journal of supercomputing Springer US, 1987 78(2021), 5 vom: 25. Okt., Seite 6696-6716 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:78 year:2021 number:5 day:25 month:10 pages:6696-6716 https://doi.org/10.1007/s11227-021-04140-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 78 2021 5 25 10 6696-6716 |
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10.1007/s11227-021-04140-5 doi (DE-627)OLC2078292451 (DE-He213)s11227-021-04140-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Liu, Tianyi verfasserin aut A lightweight neural network framework using linear grouped convolution for human activity recognition on mobile devices 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Human activity recognition (HAR) has played an indispensable role in ubiquitous computing scenario, from smart homes to game console designing, elderly care, and fitness tracking. It is very hard to manually extract most suitable activity features from raw sensor time series. Due to an obvious advantage, convolutional neural networks (CNNs) that can extract features automatically have been widely utilized for activity recognition. Despite exceptional performance, CNNs are computation-intensive and memory-demanding algorithms because of a large number of internal parameters. As a result, the research attention in HAR implementations over resource-limited embedded platforms has turned to computationally lightweight convolution architectures. In this paper, we offer a contribution in the direction. Simple linear transformations with low cost are combined with a convolution-based HAR classifier to decrease overall computational/memory overhead and, simultaneously, which establishes an efficient classification method with satisfactory performance. Our new method is evaluated against standard convolution-based and residual counterparts, over several popular HAR datasets for algorithm benchmarking. Experimental results verify that, the cheap linear operations can significantly reduce computational and memory cost, and meanwhile producing satisfactory recognition performance, which is able to ensure faster inference on mobile devices. Our new method could be a strong candidate for real HAR implementations on embedded platforms with limited computing resources. Sensor Convolutional neural networks Activity recognition Deep learning Linear grouped convolution Wang, Shuoyuan aut Liu, Yue aut Quan, Weiming aut Zhang, Lei (orcid)0000-0001-8749-7459 aut Enthalten in The journal of supercomputing Springer US, 1987 78(2021), 5 vom: 25. Okt., Seite 6696-6716 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:78 year:2021 number:5 day:25 month:10 pages:6696-6716 https://doi.org/10.1007/s11227-021-04140-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 78 2021 5 25 10 6696-6716 |
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10.1007/s11227-021-04140-5 doi (DE-627)OLC2078292451 (DE-He213)s11227-021-04140-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Liu, Tianyi verfasserin aut A lightweight neural network framework using linear grouped convolution for human activity recognition on mobile devices 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Human activity recognition (HAR) has played an indispensable role in ubiquitous computing scenario, from smart homes to game console designing, elderly care, and fitness tracking. It is very hard to manually extract most suitable activity features from raw sensor time series. Due to an obvious advantage, convolutional neural networks (CNNs) that can extract features automatically have been widely utilized for activity recognition. Despite exceptional performance, CNNs are computation-intensive and memory-demanding algorithms because of a large number of internal parameters. As a result, the research attention in HAR implementations over resource-limited embedded platforms has turned to computationally lightweight convolution architectures. In this paper, we offer a contribution in the direction. Simple linear transformations with low cost are combined with a convolution-based HAR classifier to decrease overall computational/memory overhead and, simultaneously, which establishes an efficient classification method with satisfactory performance. Our new method is evaluated against standard convolution-based and residual counterparts, over several popular HAR datasets for algorithm benchmarking. Experimental results verify that, the cheap linear operations can significantly reduce computational and memory cost, and meanwhile producing satisfactory recognition performance, which is able to ensure faster inference on mobile devices. Our new method could be a strong candidate for real HAR implementations on embedded platforms with limited computing resources. Sensor Convolutional neural networks Activity recognition Deep learning Linear grouped convolution Wang, Shuoyuan aut Liu, Yue aut Quan, Weiming aut Zhang, Lei (orcid)0000-0001-8749-7459 aut Enthalten in The journal of supercomputing Springer US, 1987 78(2021), 5 vom: 25. Okt., Seite 6696-6716 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:78 year:2021 number:5 day:25 month:10 pages:6696-6716 https://doi.org/10.1007/s11227-021-04140-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 78 2021 5 25 10 6696-6716 |
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10.1007/s11227-021-04140-5 doi (DE-627)OLC2078292451 (DE-He213)s11227-021-04140-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Liu, Tianyi verfasserin aut A lightweight neural network framework using linear grouped convolution for human activity recognition on mobile devices 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Human activity recognition (HAR) has played an indispensable role in ubiquitous computing scenario, from smart homes to game console designing, elderly care, and fitness tracking. It is very hard to manually extract most suitable activity features from raw sensor time series. Due to an obvious advantage, convolutional neural networks (CNNs) that can extract features automatically have been widely utilized for activity recognition. Despite exceptional performance, CNNs are computation-intensive and memory-demanding algorithms because of a large number of internal parameters. As a result, the research attention in HAR implementations over resource-limited embedded platforms has turned to computationally lightweight convolution architectures. In this paper, we offer a contribution in the direction. Simple linear transformations with low cost are combined with a convolution-based HAR classifier to decrease overall computational/memory overhead and, simultaneously, which establishes an efficient classification method with satisfactory performance. Our new method is evaluated against standard convolution-based and residual counterparts, over several popular HAR datasets for algorithm benchmarking. Experimental results verify that, the cheap linear operations can significantly reduce computational and memory cost, and meanwhile producing satisfactory recognition performance, which is able to ensure faster inference on mobile devices. Our new method could be a strong candidate for real HAR implementations on embedded platforms with limited computing resources. Sensor Convolutional neural networks Activity recognition Deep learning Linear grouped convolution Wang, Shuoyuan aut Liu, Yue aut Quan, Weiming aut Zhang, Lei (orcid)0000-0001-8749-7459 aut Enthalten in The journal of supercomputing Springer US, 1987 78(2021), 5 vom: 25. Okt., Seite 6696-6716 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:78 year:2021 number:5 day:25 month:10 pages:6696-6716 https://doi.org/10.1007/s11227-021-04140-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 78 2021 5 25 10 6696-6716 |
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A lightweight neural network framework using linear grouped convolution for human activity recognition on mobile devices |
| abstract |
Abstract Human activity recognition (HAR) has played an indispensable role in ubiquitous computing scenario, from smart homes to game console designing, elderly care, and fitness tracking. It is very hard to manually extract most suitable activity features from raw sensor time series. Due to an obvious advantage, convolutional neural networks (CNNs) that can extract features automatically have been widely utilized for activity recognition. Despite exceptional performance, CNNs are computation-intensive and memory-demanding algorithms because of a large number of internal parameters. As a result, the research attention in HAR implementations over resource-limited embedded platforms has turned to computationally lightweight convolution architectures. In this paper, we offer a contribution in the direction. Simple linear transformations with low cost are combined with a convolution-based HAR classifier to decrease overall computational/memory overhead and, simultaneously, which establishes an efficient classification method with satisfactory performance. Our new method is evaluated against standard convolution-based and residual counterparts, over several popular HAR datasets for algorithm benchmarking. Experimental results verify that, the cheap linear operations can significantly reduce computational and memory cost, and meanwhile producing satisfactory recognition performance, which is able to ensure faster inference on mobile devices. Our new method could be a strong candidate for real HAR implementations on embedded platforms with limited computing resources. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract Human activity recognition (HAR) has played an indispensable role in ubiquitous computing scenario, from smart homes to game console designing, elderly care, and fitness tracking. It is very hard to manually extract most suitable activity features from raw sensor time series. Due to an obvious advantage, convolutional neural networks (CNNs) that can extract features automatically have been widely utilized for activity recognition. Despite exceptional performance, CNNs are computation-intensive and memory-demanding algorithms because of a large number of internal parameters. As a result, the research attention in HAR implementations over resource-limited embedded platforms has turned to computationally lightweight convolution architectures. In this paper, we offer a contribution in the direction. Simple linear transformations with low cost are combined with a convolution-based HAR classifier to decrease overall computational/memory overhead and, simultaneously, which establishes an efficient classification method with satisfactory performance. Our new method is evaluated against standard convolution-based and residual counterparts, over several popular HAR datasets for algorithm benchmarking. Experimental results verify that, the cheap linear operations can significantly reduce computational and memory cost, and meanwhile producing satisfactory recognition performance, which is able to ensure faster inference on mobile devices. Our new method could be a strong candidate for real HAR implementations on embedded platforms with limited computing resources. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Human activity recognition (HAR) has played an indispensable role in ubiquitous computing scenario, from smart homes to game console designing, elderly care, and fitness tracking. It is very hard to manually extract most suitable activity features from raw sensor time series. Due to an obvious advantage, convolutional neural networks (CNNs) that can extract features automatically have been widely utilized for activity recognition. Despite exceptional performance, CNNs are computation-intensive and memory-demanding algorithms because of a large number of internal parameters. As a result, the research attention in HAR implementations over resource-limited embedded platforms has turned to computationally lightweight convolution architectures. In this paper, we offer a contribution in the direction. Simple linear transformations with low cost are combined with a convolution-based HAR classifier to decrease overall computational/memory overhead and, simultaneously, which establishes an efficient classification method with satisfactory performance. Our new method is evaluated against standard convolution-based and residual counterparts, over several popular HAR datasets for algorithm benchmarking. Experimental results verify that, the cheap linear operations can significantly reduce computational and memory cost, and meanwhile producing satisfactory recognition performance, which is able to ensure faster inference on mobile devices. Our new method could be a strong candidate for real HAR implementations on embedded platforms with limited computing resources. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| title_short |
A lightweight neural network framework using linear grouped convolution for human activity recognition on mobile devices |
| url |
https://doi.org/10.1007/s11227-021-04140-5 |
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| author2 |
Wang, Shuoyuan Liu, Yue Quan, Weiming Zhang, Lei |
| author2Str |
Wang, Shuoyuan Liu, Yue Quan, Weiming Zhang, Lei |
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| doi_str |
10.1007/s11227-021-04140-5 |
| up_date |
2024-07-03T19:44:25.667Z |
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