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- W4377993230 abstract "In recent years, the research in Human Activity Recognition (HAR) has grown manifold due to the easy availability of data and its important role in many real-world applications. Since the performance of classical machine learning algorithms is not up to the mark, the focus is on applying deep learning algorithms for enhancing the efficacy of HAR systems. This review includes the research works carried out during the period of 2019–2022 in three recognition domains-human activity, surveillance systems and sign language. This review considers the methodologies applied, dataset used and the major findings and achievements of these recent HAR studies. Finally, the paper points out the various challenges in the field of Activity Recognition requiring further attention from researchers." @default.
- W4377993230 created "2023-05-25" @default.
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- W4377993230 date "2023-01-01" @default.
- W4377993230 modified "2023-09-30" @default.
- W4377993230 title "A Review of Deep Learning Techniques for Human Activity Recognition" @default.
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- W4377993230 doi "https://doi.org/10.1007/978-3-031-27409-1_28" @default.
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