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基于Dirichlet多项式混合模型的复杂人体行为识别 被引量:1

COMPLEX ACTIVITY RECOGNITION BASED ON DIRICHLET MULTINOMIAL MIXED MODEL
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摘要 在健康智能照顾护理领域,日常行为识别的准确率至关重要,但是由于日常行为本身的动态可变性以及个体之间的差异性的特点,造成基于可穿戴设备的日常行为识别模型的泛化性差、识别率低,无法对复杂日常行为进行识别的问题。提出一种优化的特征提取方法,将手腕动作聚合为若干个高层语义主题,进而将日常行为表征为一个有序的高层语义主题序列,有效地提升分类的效果。实验结果表明,高层主题语义特征能更准确地表征复杂日常行为的特征,提高了行为识别的准确性。 In the healthcare and nursing domain,the accuracy of activity recognition is critical.However,the daily activity is dynamic and varied,and the patterns of the activity vary from person to person,which decreases the accuracy and reliability of activity recognition model and cannot be suitable for identifying the complex behavior.In this work,an optimized feature extraction method is proposed.we improved the algorithm to extract high-level semantic topic features from the hand movements,and then the activity could be represented as a serial of high-level semantic features to improve the practicability and usability.The experiment shows that the system is more suitable for complex activity recognition by extracting high-level sematic features,and it improves the accuracy of recognition.
作者 苏春芳 傅立成 李梃颖 简易纬 Su Chunfang;Fu Licheng;Li Tingying;Jian Yiwei(Jiangyin Polytechnic College,Jiangyin 214405,Jiangsu,China;Taiwan University,Taibei 10617,Taiwan,China)
出处 《计算机应用与软件》 北大核心 2021年第2期205-212,共8页 Computer Applications and Software
基金 台湾科技部基金项目(MOST 105-2633-E-002-001) 台湾大学基金项目(NTU-105R104045)。
关键词 行为识别 Dirichlet多项式混合模型 主题模型 Collapsed 吉布斯采样 高层语义特征 Activity recognition Dirichlet multinomial mixed model Topic Model Collapsed Gibbs sampling High level semantic features
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