An unsupervised statistical representation learning method for human activity recognition
With the evolution of smart devices like smartphones, smartwatches, and other wearable devices, motion sensors have been integrated into these devices to collect data and analyze human activities. Consequently, sensor-based Human Activity Recognition (HAR) has emerged as a significant research area...
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| Published in: | Signal, image and video processing Vol. 18; no. 10; pp. 7041 - 7052 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Springer London
01.09.2024
Springer Nature B.V |
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| ISSN: | 1863-1703, 1863-1711 |
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| Abstract | With the evolution of smart devices like smartphones, smartwatches, and other wearable devices, motion sensors have been integrated into these devices to collect data and analyze human activities. Consequently, sensor-based Human Activity Recognition (HAR) has emerged as a significant research area in the fields of ubiquitous computing and wearable computing. This paper presents a novel approach that employs Latent Dirichlet Allocation (LDA) to extract meaningful representations from activity signals. The method involves transforming the activity signal, which is a sequence of samples, into a sequence of discrete symbols using vector quantization. Subsequently, LDA is utilized to embed the symbol sequence into a fixed-length representation vector. Finally, a classifier is employed to classify the obtained representation vector. The effectiveness of the proposed method is evaluated using the UNIMIB-SHAR dataset. Experimental results demonstrate its competitive performance in terms of accuracy and F1-score metrics when compared to existing methods. Moreover, our method boasts a more lightweight architecture and incurs lower computational costs compared to deep learning-based approaches. The findings of this study contribute to the advancement of HAR and hold practical implications for HAR systems. |
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| AbstractList | With the evolution of smart devices like smartphones, smartwatches, and other wearable devices, motion sensors have been integrated into these devices to collect data and analyze human activities. Consequently, sensor-based Human Activity Recognition (HAR) has emerged as a significant research area in the fields of ubiquitous computing and wearable computing. This paper presents a novel approach that employs Latent Dirichlet Allocation (LDA) to extract meaningful representations from activity signals. The method involves transforming the activity signal, which is a sequence of samples, into a sequence of discrete symbols using vector quantization. Subsequently, LDA is utilized to embed the symbol sequence into a fixed-length representation vector. Finally, a classifier is employed to classify the obtained representation vector. The effectiveness of the proposed method is evaluated using the UNIMIB-SHAR dataset. Experimental results demonstrate its competitive performance in terms of accuracy and F1-score metrics when compared to existing methods. Moreover, our method boasts a more lightweight architecture and incurs lower computational costs compared to deep learning-based approaches. The findings of this study contribute to the advancement of HAR and hold practical implications for HAR systems. |
| Author | Momeni, Saleh BabaAli, Bagher Abdi, Mohammad Foad |
| Author_xml | – sequence: 1 givenname: Mohammad Foad surname: Abdi fullname: Abdi, Mohammad Foad organization: School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran – sequence: 2 givenname: Bagher surname: BabaAli fullname: BabaAli, Bagher email: babaali@ut.ac.ir organization: School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran – sequence: 3 givenname: Saleh surname: Momeni fullname: Momeni, Saleh organization: School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran |
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| Keywords | Feature embedding Latent Dirichlet allocation (LDA) Human activity recognition Unsupervised representation learning |
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