摘要
针对现有超宽带雷达人体姿态识别研究领域缺少对相似动作辨识的问题,提出一种时频分析结合随机森林(RF)的动作辨识模型。提出基于平滑伪维格纳-威利分布(SPWVD)的二维平滑伪维格纳-威利分布(2D-SPWVD)时频分析方法,对预处理后的人体动作回波信号进行时频特征提取;利用主成分分析法(PCA)对特征矢量进行降维处理,选择累计贡献率较高的前30个主成分作为新的特征矢量输入到麻雀搜索算法(SSA)优化的RF分类模型中,用于有障碍条件下5种不同人体相似跌落动作辨识。实验结果表明:预处理算法有效地提升了动作回波信号信噪比,PCA-SSA-RF分类模型能有效辨识5种不同人体跌落动作,克服了数据的特殊性以及障碍物的干扰,准确率高达96.6%。在实时数据流中的跌倒检测任务中,模型的分类平均准确率达到了93%,并与RF、PSO-RF等多个不同经典分类模型深入对比,准确率较高且整体所需时间较短,兼具了准确性和分类效率。验证了所提方法的优越性与有效性。
Aiming at the deficiency of similar motion recognition in the current UWB radar attitude recognition research domain,a motion recognition model integrating time-frequency analysis and random forest(RF)is proposed.A time-frequency analysis method of two-dimensional smoothed pseudo Wigner-Ville distribution(2D-SPWVD)based on smoothed pseudo Wigner-Ville distribution(SPWVD)is proposed to extract the time-frequency features of the preprocessed human motion echo signals.Principal component analysis(PCA)was employed to reduce the dimension of the feature vectors,and the top 30 principal components with a high cumulative contribution rate were selected as new feature vectors to be input into the RF classification model optimized by sparrow search algorithm(SSA)for the identification of five distinct human similar drop actions in the presence of obstacles.The experimental outcomes demonstrate that the pretreatment algorithm can effectively enhance the SNR of the action echo signal,and the PCA-SSA-RF classification model can effectively distinguish five different human fall movements,overcome the particularity of data and the interference of obstacles,with an accuracy rate as high as 96.6%.In the fall detection task within the real-time data stream,the average classification accuracy of the model reaches 93%,and it is profoundly compared with RF,PSO-RF and other diverse classical classification models,featuring high accuracy and short overall time,and possessing both accuracy and classification efficiency.The superiority and effectiveness of the proposed method are verified.
作者
杨桢
段雨昕
李鑫
吴方泽
纪力文
冯丰
Yang Zhen;Duan Yuxin;Li Xin;Wu Fangze;Ji Liwen;Feng Feng(School of Electrical and Control Engineering,Liaoning Technical University,Huludao 125105,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2024年第10期147-158,共12页
Journal of Electronic Measurement and Instrumentation
基金
辽宁省应用基础研究计划项目(2023JH2/101300138)
辽宁省教育厅基本科研项目(重点攻关项目)(LJKZZ20220046,JYTZD2023075)
辽宁“百千万人才工程”培养经费资助项目(2021921083)。
