A new approach for physical human activity recognition based on co-occurrence matrices

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dc.authoridKUNCAN, Fatma/0000-0003-0712-6426
dc.authoridKUNCAN, Melih/0000-0002-9749-0418
dc.contributor.authorKuncan, Fatma
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorTekin, Ramazan
dc.contributor.authorKuncan, Melih
dc.date.accessioned2024-12-24T19:24:49Z
dc.date.available2024-12-24T19:24:49Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractIn recent years, it has been observed that many researchers have been working on different areas of detection, recognition and monitoring of human activities. The automatic determination of human physical activities is often referred to as human activity recognition (HAR). One of the most important technology that detects and tracks the activity of the human body is sensor-based HAR technology. In recent days, sensor-based HAR attracts attention in the field of computers due to its wide use in daily life and is a rapidly growing field of research. Activity recognition (AR) application is carried out by evaluating the signals obtained from various sensors placed in the human body. In this study, a new approach is proposed to extract features from sensor signals using HAR. The proposed approach is inspired by the Gray Level Co-Occurrence Matrix (GLCM) method, which is widely used in image processing, but it is applied to one-dimensional signals, unlike GLCM. Two datasets were used to test the proposed approach. The datasets were created from the signals obtained from the accelerometer, gyro and magnetometer sensors. Heralick features were obtained from co-occurrence matrix created after 1D-GLCM (One (1) Dimensional-Gray Level Co-Occurrence Matrix) was applied to the signals. HAR operation has been carried out for different scenarios using these features. Success rates of 96.66 and 93.88% were obtained for two datasets, respectively. It has been observed that the new approach proposed within the scope of the study provides high success rates for HAR applications. It is thought that the proposed approach can be used in the classification of different signals.
dc.identifier.doi10.1007/s11227-021-03921-2
dc.identifier.endpage1070
dc.identifier.issn0920-8542
dc.identifier.issn1573-0484
dc.identifier.issue1
dc.identifier.pmid34103787
dc.identifier.scopus2-s2.0-85107489156
dc.identifier.scopusqualityQ1
dc.identifier.startpage1048
dc.identifier.urihttps://doi.org/10.1007/s11227-021-03921-2
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6130
dc.identifier.volume78
dc.identifier.wosWOS:000658092100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Supercomputing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectHuman activity recognition
dc.subject1D-GLCM
dc.subjectHeralick features
dc.subjectWearable sensor
dc.subjectSensor signals
dc.subjectFeature extraction
dc.titleA new approach for physical human activity recognition based on co-occurrence matrices
dc.typeArticle

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