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• W3132604259 abstract "Human activity plays a significant role in various fields, such as manufacturing, healthcare, and public safety; therefore, recognizing human activity is crucial to enable smart innovative services. The development of ubiquitous sensing and pervasive computing allows studying what humans perform in real time and mobility. Single- and multi-user activity recognition (AR) differ by the number of involved users. With recent developments of multi-sensor and multi-information fusion, multi-user activity recognition is gradually becoming an emerging and relevant research frontier. In this paper, we propose a software architecture which combines cloud and edge computing with collaborative body sensor networks (CBSNs) to support the development of CBSNs-enabled services and in particular we provide its case-study in the context of multi-user AR." @default.
• W3132604259 created "2021-03-01" @default.
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• W3132604259 date "2021-01-01" @default.
• W3132604259 modified "2023-10-16" @default.
• W3132604259 title "A Collaborative BSN-Enabled Architecture for Multi-user Activity Recognition" @default.
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• W3132604259 doi "https://doi.org/10.1007/978-3-030-67197-6_6" @default.
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