Real-Time Human Movement Recognition Using Ultra-Wideband Sensors

This study introduces a methodology for the real-time detection of human movement based on two legs using ultra-wideband (UWB) sensors. Movements were primarily categorized into four states: stopped, walking, lingering, and the transition between sitting and standing. To classify these movements, UW...

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Bibliographic Details
Published in:Electronics (Basel) 2024-04, Vol.13 (7), p.1300
Main Authors: Noh, Minseong, Ahn, Heungju, Lee, Sang C.
Format: Article
Language:English
Subjects:
Analysis
Classification
Fourier transforms
Human mechanics
Human motion
Lasers
Motion perception
Real time
Robotics
Robots
Sensors
Ultrawideband
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Summary:This study introduces a methodology for the real-time detection of human movement based on two legs using ultra-wideband (UWB) sensors. Movements were primarily categorized into four states: stopped, walking, lingering, and the transition between sitting and standing. To classify these movements, UWB sensors were used to measure the distance between the designated point and a specific point on the two legs in the human body. By analyzing the measured distance values, a movement state classification model was constructed. In comparison to conventional vision/laser/LiDAR-based research, this approach requires fewer computational resources and provides distinguished real-time human movement detection within a CPU environment. Consequently, this research presents a novel strategy to effectively recognize human movements during human–robot interactions. The proposed model effectively discerned four distinct movement states with classification accuracy of around 95%, demonstrating the novel strategy’s efficacy.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13071300