太赫兹科学与电子信息学报2024，Vol.22Issue(5)：549-557,9.DOI:10.11805/TKYDA2022097
基于数据增强算法和CNN-LSTM的高精确度手势识别
High-precision gesture recognition based on data enhancement algorithm and CNN-LSTM
摘要
Abstract
In recent years,radar-based gesture recognition technology has been widely used in industry and life,and more complex application scenarios also put forward higher requirements on the accuracy and robustness of gesture recognition algorithms.A high-precision gesture recognition algorithm based on millimeter-wave radar is desgined.By comparing the existing classification algorithms,a Convolutional Neural Network-Long Short Term Memory(CNN-LSTM)deep learning algorithm model is constructed for gesture recognition.At the same time,the Blackman window is employed to suppress the problem of spectrum leakage in gesture signal processing,and efficient clutter suppression and data enhancement is achieved through the combining of wavelet threshold and dynamic zero-padding algorithm.The actual measurement results show that the proposed gesture recognition algorithm achieves a correct classification rate of 97.29%,and can maintain a good recognition accuracy rate under different distances and angles with very good robustness.关键词
手势识别/毫米波雷达/卷积神经网络-长短期记忆网络/杂波抑制/小波阈值算法Key words
gesture recognition/millimeter wave radar/Convolutional Neural Network-Long Short Term Memory(CNN-LSTM)/clutter suppression/wavelet threshold algorithm分类
信息技术与安全科学引用本文复制引用
唐高鹏,李从胜,巫彤宁..基于数据增强算法和CNN-LSTM的高精确度手势识别[J].太赫兹科学与电子信息学报,2024,22(5):549-557,9.基金项目
国家自然科学基金资助项目(61971445) （61971445）
