Application of human activity/action recognition: a review

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dc.authorscopusidAli Ghaffari / 57197223215
dc.authorwosidAli Ghaffari / AAV-3651-2020
dc.contributor.authorSedaghati, Nazanin
dc.contributor.authorArdebili, Sondos
dc.contributor.authorGhaffari, Ali
dc.date.accessioned2025-04-17T14:26:54Z
dc.date.available2025-04-17T14:26:54Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractHuman activity recognition is a crucial domain in computer science and artificial intelligence that involves the Detection, Classification, and Prediction of human activities using sensor data such as accelerometers, gyroscopes, etc. This field utilizes time-series signals from sensors present in smartphones and wearable devices to extract human activities. Various types of sensors, including inertial HAR sensors, physiological sensors, location sensors, cameras, and temporal sensors, are employed in diverse environments within this domain. It finds valuable applications in various areas such as smart homes, elderly care, the Internet of Things (IoT), personal care, social sciences, rehabilitation engineering, fitness, and more. With the advancement of computational power, deep learning algorithms have been recognized as effective and efficient methods for detecting and solving well-established HAR issues. In this research, a review of various deep learning algorithms is presented with a focus on distinguishing between two key aspects: activity and action. Action refers to specific, short-term movements and behaviors, while activity refers to a set of related, continuous affairs over time. The reviewed articles are categorized based on the type of algorithms and applications, specifically sensor-based and vision-based. The total number of reviewed articles in this research is 80 sources, categorized into 42 references. By offering a detailed classification of relevant articles, this comprehensive review delves into the analysis and scrutiny of the scientific community in the HAR domain using deep learning algorithms. It serves as a valuable guide for researchers and enthusiasts to gain a better understanding of the advancements and challenges within this field.
dc.identifier.citationsedaghati, N., ardebili, S., & Ghaffari, A. (2025). Application of human activity/action recognition: a review. Multimedia Tools and Applications, 1-30.
dc.identifier.doi10.1007/s11042-024-20576-2
dc.identifier.issn13807501
dc.identifier.scopus2-s2.0-85217156333
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1007/s11042-024-20576-2
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6297
dc.indekslendigikaynakScopus
dc.institutionauthorGhaffari, Ali
dc.institutionauthoridAli Ghaffari /0000-0001-5407-8629
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/openAccess
dc.subjectHuman Action Detection
dc.subjectHuman Activity Recognition (HAR)
dc.subjectHuman–Computer Interaction (HCI)
dc.subjectSensors
dc.subjectSmartphones
dc.titleApplication of human activity/action recognition: a review
dc.typeArticle

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