A new approach for physical human activity recognition from sensor signals based on motif patterns and long-short term memory

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dc.authoridKAYA, Mahmut/0000-0002-7846-1769
dc.authoridKUNCAN, Fatma/0000-0003-0712-6426
dc.contributor.authorKuncan, Fatma
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorYiner, Zueleyha
dc.contributor.authorKaya, Mahmut
dc.date.accessioned2024-12-24T19:25:22Z
dc.date.available2024-12-24T19:25:22Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractNumerous studies have been carried out in recent years on the recognition, tracking, and discrimination of human activities. Automatic recognition of physical activities is often referred to as human activity recognition (HAR). There are generally vision-based and sensor-based approaches for activity recognition. The computer vision-based approach generally works well in laboratory conditions, but it can fail in real-world problems due to clutter, variable light intensity, and contrast. Sensor-based HAR systems are realized by continuously monitoring and analyzing physiological signals measured from heterogeneous sensors connected to the person's body. In this study, the Motif Patterns (MP) approach, which extracts features from sensor signals, is proposed for HAR. The success of the HAR systems depends on the effectiveness of the features extracted from the signals. The LSTM network is a special kind of recurrent neural network that has been used to make very successful predictions on time series data where long-term dependencies are. The LSTM network type offers a successful solution approach to solving long-term dependencies problems such as human activity recognition. The classification process was carried out with Long-Short Term Memory (LSTM) using MP features extracted from accelerometer, gyroscope, and magnetometer sensor signals. A large dataset of 9120 signals was used to test the proposed approach. A high success rate of 98.42 % was achieved with the proposed MP + LSTM method. As a result, it has been seen that the proposed approach has been obtained with a high success rate for HAR using sensor signals.
dc.description.sponsorshipScientific Research Projects Coordi-nation Unit of Siirt University [2021-SI?]
dc.description.sponsorshipThis work is supported by the Scientific Research Projects Coordi-nation Unit of Siirt University as a project with the number 2021-SI?UM?H-13. This study is performed in Siirt University Faculty of En-gineering Machine Vision (MaVi) Laboratory. The authors of this article would like to thank the staff of MaVi Laboratory for their support. Fatma KUNCAN and YIlmaz KAYA conceived of the presented idea. Mahmut KAYA and Z?leyha YI?NER developed the theory and performed the computations. Fatma KUNCAN and YIlmaz KAYA verified the analytical methods. All authors discussed the results and contributed to the final manuscript
dc.identifier.doi10.1016/j.bspc.2022.103963
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85134601122
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2022.103963
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6386
dc.identifier.volume78
dc.identifier.wosWOS:000861973000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectSensor signals
dc.subjectHAR
dc.subjectLSTM
dc.subjectMotif patterns
dc.subjectFeatureextraction
dc.titleA new approach for physical human activity recognition from sensor signals based on motif patterns and long-short term memory
dc.typeArticle

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