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- W2296456827 abstract "In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In context-aware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods." @default.
- W2296456827 created "2016-06-24" @default.
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- W2296456827 date "2015-01-01" @default.
- W2296456827 modified "2023-10-16" @default.
- W2296456827 title "Comparative Analysis of Artificial Hydrocarbon Networks and Data-Driven Approaches for Human Activity Recognition" @default.
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- W2296456827 doi "https://doi.org/10.1007/978-3-319-26401-1_15" @default.
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