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• W4283705653 abstract "In this article we present SHARP, an original approach for obtaining human activity recognition (HAR) through the use of commercial IEEE 802.11 (Wi-Fi) devices. SHARP grants the possibility to discern the activities of different persons, across different time-spans and environments. To achieve this, we devise a new technique to clean and process the channel frequency response (CFR) phase of the Wi-Fi channel, obtaining an estimate of the Doppler shift at a radio monitor device. The Doppler shift reveals the presence of moving scatterers in the environment, while not being affected by (environment-specific) static objects. SHARP is trained on data collected as a person performs seven different activities in a single environment. It is then tested on different setups, to assess its performance as the person, the day and/or the environment change with respect to those considered at training time. In the worst-case scenario, it reaches an average accuracy higher than <inline-formula><tex-math notation=LaTeX>$95%$</tex-math></inline-formula> , validating the effectiveness of the extracted Doppler information, used in conjunction with a learning algorithm based on a neural network, in recognizing human activities in a subject and environment independent way. The collected CFR dataset and the code are publicly available for replicability and benchmarking purposes [1]." @default.
• W4283705653 created "2022-06-30" @default.
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• W4283705653 date "2023-10-01" @default.
• W4283705653 modified "2023-10-11" @default.
• W4283705653 title "SHARP: Environment and Person Independent Activity Recognition With Commodity IEEE 802.11 Access Points" @default.
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• W4283705653 doi "https://doi.org/10.1109/tmc.2022.3185681" @default.
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