Abstract
The aim of the Human-Activity Recognition (HAR) is to identify the actions carried out by an individual given a data set of parameters recorded by sensors. Successful HAR research has focused on the recognition of relatively simple activities, as sitting or walking and its applications are mainly useful in the fields of healthcare, tele-immersion or fitness tracking. One of the most affordable ways to recognize human activities is to make use of smartphones. This paper draws a comparison line between several ways of processing and training the data provided by smartphone sensors, in order to achieve an accurate score when recognizing the user’s activity.
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Ilisei, D., Suciu, D.M. (2020). Human-Activity Recognition with Smartphone Sensors. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2019 Workshops. OTM 2019. Lecture Notes in Computer Science(), vol 11878. Springer, Cham. https://doi.org/10.1007/978-3-030-40907-4_18
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DOI: https://doi.org/10.1007/978-3-030-40907-4_18
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