HARNAS: Human Activity Recognition Based on Automatic Neural Architecture Search Using Evolutionary Algorithms

Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of deep learning, the neural architecture search (NA...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 21; číslo 20; s. 6927
Hlavní autori:	Wang, Xiaojuan, Wang, Xinlei, Lv, Tianqi, Jin, Lei, He, Mingshu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 19.10.2021 MDPI
Predmet:	Accuracy Automation Back propagation Classification Datasets Deep learning Discriminant analysis Genetic algorithms human activity recognition Machine learning multi-objective optimization multimodal sensor data neural architecture search Neural networks Optimization Sensors Wearable computers
ISSN:	1424-8220, 1424-8220
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Abstract	Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of deep learning, the neural architecture search (NAS) has emerged in an attempt to minimize human intervention. We propose an approach for using NAS to search for models suitable for HAR tasks, namely, HARNAS. The multi-objective search algorithm NSGA-II is used as the search strategy of HARNAS. To make a trade-off between the performance and computation speed of a model, the F1 score and the number of floating-point operations (FLOPs) are selected, resulting in a bi-objective problem. However, the computation speed of a model not only depends on the complexity, but is also related to the memory access cost (MAC). Therefore, we expand the bi-objective search to a tri-objective strategy. We use the Opportunity dataset as the basis for most experiments and also evaluate the portability of the model on the UniMiB-SHAR dataset. The experimental results show that HARNAS designed without manual adjustments can achieve better performance than the best model tweaked by humans. HARNAS obtained an F1 score of 92.16% and parameters of 0.32 MB on the Opportunity dataset.
AbstractList	Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of deep learning, the neural architecture search (NAS) has emerged in an attempt to minimize human intervention. We propose an approach for using NAS to search for models suitable for HAR tasks, namely, HARNAS. The multi-objective search algorithm NSGA-II is used as the search strategy of HARNAS. To make a trade-off between the performance and computation speed of a model, the F1 score and the number of floating-point operations (FLOPs) are selected, resulting in a bi-objective problem. However, the computation speed of a model not only depends on the complexity, but is also related to the memory access cost (MAC). Therefore, we expand the bi-objective search to a tri-objective strategy. We use the Opportunity dataset as the basis for most experiments and also evaluate the portability of the model on the UniMiB-SHAR dataset. The experimental results show that HARNAS designed without manual adjustments can achieve better performance than the best model tweaked by humans. HARNAS obtained an F1 score of 92.16% and parameters of 0.32 MB on the Opportunity dataset. Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of deep learning, the neural architecture search (NAS) has emerged in an attempt to minimize human intervention. We propose an approach for using NAS to search for models suitable for HAR tasks, namely, HARNAS. The multi-objective search algorithm NSGA-II is used as the search strategy of HARNAS. To make a trade-off between the performance and computation speed of a model, the F1 score and the number of floating-point operations (FLOPs) are selected, resulting in a bi-objective problem. However, the computation speed of a model not only depends on the complexity, but is also related to the memory access cost (MAC). Therefore, we expand the bi-objective search to a tri-objective strategy. We use the Opportunity dataset as the basis for most experiments and also evaluate the portability of the model on the UniMiB-SHAR dataset. The experimental results show that HARNAS designed without manual adjustments can achieve better performance than the best model tweaked by humans. HARNAS obtained an F1 score of 92.16% and parameters of 0.32 MB on the Opportunity dataset.Human activity recognition (HAR) based on wearable sensors is a promising research direction. The resources of handheld terminals and wearable devices limit the performance of recognition and require lightweight architectures. With the development of deep learning, the neural architecture search (NAS) has emerged in an attempt to minimize human intervention. We propose an approach for using NAS to search for models suitable for HAR tasks, namely, HARNAS. The multi-objective search algorithm NSGA-II is used as the search strategy of HARNAS. To make a trade-off between the performance and computation speed of a model, the F1 score and the number of floating-point operations (FLOPs) are selected, resulting in a bi-objective problem. However, the computation speed of a model not only depends on the complexity, but is also related to the memory access cost (MAC). Therefore, we expand the bi-objective search to a tri-objective strategy. We use the Opportunity dataset as the basis for most experiments and also evaluate the portability of the model on the UniMiB-SHAR dataset. The experimental results show that HARNAS designed without manual adjustments can achieve better performance than the best model tweaked by humans. HARNAS obtained an F1 score of 92.16% and parameters of 0.32 MB on the Opportunity dataset.
Author	Lv, Tianqi Wang, Xiaojuan Jin, Lei Wang, Xinlei He, Mingshu
AuthorAffiliation	School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China; wxl2019@bupt.edu.cn (X.W.); lvtianqi@bupt.edu.cn (T.L.); jinlei@bupt.edu.cn (L.J.); hemingshu@bupt.edu.cn (M.H.)
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SubjectTerms	Accuracy Automation Back propagation Classification Datasets Deep learning Discriminant analysis Genetic algorithms human activity recognition Machine learning multi-objective optimization multimodal sensor data neural architecture search Neural networks Optimization Sensors Wearable computers
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Title	HARNAS: Human Activity Recognition Based on Automatic Neural Architecture Search Using Evolutionary Algorithms
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