Human activity recognition from multiple sensors data using deep CNNs

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dc.authoridTopuz, Elif Kevser/0000-0002-0207-8069
dc.authoridKAYA, Yasin/0000-0002-9074-0189
dc.contributor.authorKaya, Yasin
dc.contributor.authorTopuz, Elif Kevser
dc.date.accessioned2025-01-06T17:43:16Z
dc.date.available2025-01-06T17:43:16Z
dc.date.issued2024
dc.description.abstractSmart devices with sensors now enable continuous measurement of activities of daily living. Accordingly, various human activity recognition (HAR) experiments have been carried out, aiming to convert the measures taken from smart devices into physical activity types. HAR can be applied in many research areas, such as health assessment, environmentally supported living systems, sports, exercise, and security systems. The HAR process can also detect activity-based anomalies in daily life for elderly people. Thus, this study focused on sensor-based activity recognition, and we developed a new 1D-CNN-based deep learning approach to detect human activities. We evaluated our model using raw accelerometer and gyroscope sensor data on three public datasets: UCI-HAPT, WISDM, and PAMAP2. Parameter optimization was employed to define the model's architecture and fine-tune the final design's hyper-parameters. We applied 6, 7, and 12 classes of activity recognition to the UCI-HAPT dataset and obtained accuracy rates of 98%, 96.9%, and 94.8%, respectively. We also achieved an accuracy rate of 97.8% and 90.27% on the WISDM and PAMAP2 datasets, respectively. Moreover, we investigated the impact of using each sensor data individually, and the results show that our model achieved better results using both sensor data concurrently.
dc.identifier.doi10.1007/s11042-023-15830-y
dc.identifier.endpage10838
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85163166035
dc.identifier.scopusqualityQ1
dc.identifier.startpage10815
dc.identifier.urihttps://doi.org/10.1007/s11042-023-15830-y
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2574
dc.identifier.volume83
dc.identifier.wosWOS:001019903000012
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofMultimedia Tools and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectHuman activity recognition
dc.subject1D-CNN
dc.subjectDeep learning
dc.subjectSignal processing
dc.titleHuman activity recognition from multiple sensors data using deep CNNs
dc.typeArticle

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