Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/128366
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Informatika / Informatics (N009)
Author(s): Kulikajevas, Audrius;Maskeliūnas, Rytis;Damaševičius, Robertas
Title: Detection of sitting posture using hierarchical image composition and deep learning
Is part of: PeerJ computer science. London : PeerJ Inc., 2021, article no. e442
Extent: p. 1-20
Date: 2021
Note: Article no. e442
Keywords: Posture detection;Computer vision;Deep learning;Artificial neural network;Depth sensors;Sitting posture;e-Health
Abstract: Human posture detection allows the capture of the kinematic parameters of the human body, which is important for many applications, such as assisted living, healthcare, physical exercising and rehabilitation. This task can greatly benefit from recent development in deep learning and computer vision. In this paper, we propose a novel deep recurrent hierarchical network (DRHN) model based on MobileNetV2 that allows for greater flexibility by reducing or eliminating posture detection problems related to a limited visibility human torso in the frame, i.e., the occlusion problem. The DRHN network accepts the RGB-Depth frame sequences and produces a representation of semantically related posture states. We achieved 91.47% accuracy at 10 fps rate for sitting posture recognition
Internet: https://www.vdu.lt/cris/bitstream/20.500.12259/128366/2/ISSN2376-5992_2021_E442.PG_1-20.pdf
https://hdl.handle.net/20.500.12259/128366
https://doi.org/10.7717/peerj-cs.442
Affiliation(s): Kauno technologijos universitetas
Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:1. Straipsniai / Articles
Universiteto mokslo publikacijos / University Research Publications

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