Human activity recognition based on smartphone sensor data using CNN

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dc.contributor.authorIsmail, K.
dc.contributor.authorÖzacar, K.
dc.date.accessioned2024-09-29T16:16:04Z
dc.date.available2024-09-29T16:16:04Z
dc.date.issued2020
dc.departmentKarabük Üniversitesien_US
dc.description5th International Conference on Smart City Applications, SCA 2020 -- 7 October 2020 through 8 October 2020 -- Safranbolu -- 167323en_US
dc.description.abstractHuman activity recognitions have been widely used nowadays by end users thanks to extensive usage of smartphones. Smartphones, by self-containing low-cost sensing technology, can track our daily activities for serving healthcare, sport, interactive AR/VR games and so on. However, smartphone technology is evolving and the techniques of using the data that smartphones go through are also improving. In this study, we used built-in sensing technologies (accelerometer and gyroscope) available in nearly every smartphone to detect the most common 5 daily activities of human by taking the data of these sensors and extract the features for a Convolutional Neural Network (CNN) model. We prepare a dataset and use TensorFlow to train the collected data from the sensors then filtered it to be processed. We also discuss the differences in CNN model accuracy with different optimizers. To demonstrate the model, we developed an android application that successfully predict an activity. We believe that after improving this application, it can be used for especially lonely old people to immediately warn authorities in case of any daily incidents. © 2020 International Society for Photogrammetry and Remote Sensing. All rights reserved.en_US
dc.identifier.doi10.5194/isprs-archives-XLIV-4-W3-2020-263-2020
dc.identifier.endpage265en_US
dc.identifier.issn1682-1750
dc.identifier.issue4/W3en_US
dc.identifier.scopus2-s2.0-85101922177en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage263en_US
dc.identifier.urihttps://doi.org/10.5194/isprs-archives-XLIV-4-W3-2020-263-2020
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8845
dc.identifier.volume44en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInternational Society for Photogrammetry and Remote Sensingen_US
dc.relation.ispartofInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archivesen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCNNen_US
dc.subjectHuman activity recognitionen_US
dc.subjectSensing dataen_US
dc.titleHuman activity recognition based on smartphone sensor data using CNNen_US
dc.typeConference Objecten_US

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