TY - GEN
T1 - Peak Amplitude Curve Based Arm Motion Recognition Using IR-UWB Radar
AU - Lin, Guiping
AU - Men, Jing
AU - Lin, Enmin
AU - Zhuang, Zhihao
AU - Zhang, Tingting
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Accurate human motion classification is required in different fields, which currently mainly relies on WiFi and millimeter waves. Meanwhile, the Ultra-Wideband (UWB) signal offers high spatiotemporal resolution and robust interference resistance, making it suitable for distinguishing limb motions from other body movements, such as body tremors, heartbeat, and respiration. This paper proposes a simple but effective method for arm motion classification and recognition using an Impulse Radio UWB radar. We simplify the motion feature data by extracting the Peak Amplitude Curve (PAC) from the obtained time-frequency spectrum, resulting in reduced dimensionality and sample size. By employing traditional machine learning models instead of complex deep learning models, we achieve a recognition accuracy of up to 94.7%. To demonstrate the reliability of the model, we conducted ten-fold cross-validation, which yielded an average recognition accuracy of 93%.
AB - Accurate human motion classification is required in different fields, which currently mainly relies on WiFi and millimeter waves. Meanwhile, the Ultra-Wideband (UWB) signal offers high spatiotemporal resolution and robust interference resistance, making it suitable for distinguishing limb motions from other body movements, such as body tremors, heartbeat, and respiration. This paper proposes a simple but effective method for arm motion classification and recognition using an Impulse Radio UWB radar. We simplify the motion feature data by extracting the Peak Amplitude Curve (PAC) from the obtained time-frequency spectrum, resulting in reduced dimensionality and sample size. By employing traditional machine learning models instead of complex deep learning models, we achieve a recognition accuracy of up to 94.7%. To demonstrate the reliability of the model, we conducted ten-fold cross-validation, which yielded an average recognition accuracy of 93%.
UR - https://www.scopus.com/pages/publications/85181170178
U2 - 10.1109/VTC2023-Fall60731.2023.10333771
DO - 10.1109/VTC2023-Fall60731.2023.10333771
M3 - 会议稿件
AN - SCOPUS:85181170178
T3 - IEEE Vehicular Technology Conference
BT - 2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 98th IEEE Vehicular Technology Conference, VTC 2023-Fall
Y2 - 10 October 2023 through 13 October 2023
ER -