Deep Learning-Based Human Recognition Through the Wall using UWB radar

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dc.contributor.authorAssawaroongsakul, Pongpol
dc.contributor.authorKhumdee, Mawin
dc.contributor.authorPhasukkit, Pattarapong
dc.contributor.authorHoungkamhang, Nongluck
dc.date.accessioned2026-08-06T10:30:39Z
dc.date.available2026-08-06T10:30:39Z
dc.date.issued2021-01-01
dc.description.abstractHuman activity detection in obscured or invisible area, for instance, human detection through the wall has become an interesting topic because it has potential for security, rescue, activity analysis application, etc. UWB radar, a detection system produces short radio frequency pulses and measures the reflected signals which UWB pulses have high spatial resolution and enable penetration in dielectric materials, was used to collect human activity through the wall signals at the frequency range of 3 GHz in this research. Subsequently, we applied signal data with the Deep Neural Network model to classify 5 classes of human activity including standing, walking, sitting, laying, and no-human gave the F1 score up to 96.94%.
dc.identifier.citation16th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2021, 2021
dc.identifier.doi10.1109/iSAI-NLP54397.2021.9678182
dc.identifier.other2-s2.0-85125339909
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/11515
dc.source16th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2021
dc.subjectdeep neural network
dc.subjecthuman activity recognition
dc.subjectUWB radar
dc.subjectwall
dc.titleDeep Learning-Based Human Recognition Through the Wall using UWB radar
dc.typeConference Paper

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