Hip Motion Measurement and Classification Using Millimeter Wave Radar and Convolutional Neural Networks

被引：1
|
作者：
Bresnahan, Drew G. [1 ]
Li, Yang [1 ]
机构：
[1] Baylor Univ, Dept Elect & Comp Engn, Waco, TX 76798 USA
来源：
PROCEEDINGS OF THE 2022 IEEE TEXAS SYMPOSIUM ON WIRELESS AND MICROWAVE CIRCUITS AND SYSTEMS (WMCS) | 2022年
基金：
美国国家科学基金会;
关键词：
Hip; motions; human; movement; physiology; millimeter wave radar; neural networks; FMCW RADAR; HEAD;
D O I：
10.1109/WMCS55582.2022.9866093
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
After hip joint injury or replacement surgery, patients must undergo regular hip motion rehabilitation. This study seeks to replace the in-person doctor's assessment with non-contact radar sensing. Multiple test subjects perform three hip motion patterns while being scanned by a millimeter wave radar. The micro-Doppler signatures are processed into a spectrogram image format for analysis. The different hip motions exhibit unique time-frequency features which are exploited by a convolutional neural network to classify the activities.
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页数：5
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