基于CBAM DCNN-BiLSTM的蹴球动作识别与评估
Cuqiu Motion Recognition and Skill Assessment Based on CBAM DCNN-BiLSTM
投稿时间:2022-04-14  修订日期:2022-04-14
DOI:
中文关键词: 蹴球  民族体育  动作识别  注意力机制  融合模型
英文关键词: Cuqiu  national sports  motion recognition  attention mechanism  fusion model
基金项目:国家自然科学(61902437);中央高校基本业务经费项目(CZT20027)
作者单位邮编
王志佳 中南民族大学 计算机科学学院 
蓝雯飞 中南民族大学 计算机科学学院 430074
张潇 中南民族大学 计算机科学学院 
侯志涛 中南民族大学 体育学院 
金宁 中南民族大学 体育学院 
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中文摘要:
      蹴球是我国少数民族传统体育运动. 当前蹴球训练方式主要依赖于教练员个人经验,缺乏数据化的科学训练手段. 利用惯性传感器对蹴球运动数据进行采集和挖掘,可对运动动作进行有效识别和评估. 然而,惯性传感器对下半身运动动作的数据捕捉敏感度偏低,重要特征表达能力不强,且现有人体活动识别算法模型对时间信息利用不充分. 基于此,本文提出一种是一种空洞卷积神经网络(DCNN)和双向长短期记忆循环神经网络(BiLSTM)的融合模型,通过将通道注意力机制与空间注意力机制相结合的卷积注意力模块 CBAM 引入网络中,以提高模型对于重要特征的表达能力. 实验结果表明,该模型在蹴球动作识别与评估任务中比同类神经网络模型具有更好的性能,其精确率、召回率以及 F1 值可分别达到99.05%、99.04%、99.0.%.
英文摘要:
      Cuqiu is a Chinese Traditional Sport. At present, the way of Cuqiu training mainly depends on Coaches" personal experience and lacks of scientific training means. Using inertial sensors to collect and mine the motion data of Cuqiu can effectively identify and evaluate the motion activity. However, the inertial sensor has low sensitivity to capture the data of lower body motion, the expression ability of important features is weak, and the existing human activity recognition algorithm model does not make full use of time information. Based on this, this paper proposes a fusion model of dilated convolution neural network (DCNN) and bidirectional long short-term memory recurrent neural network (BiLSTM). The convolutional attention module CBAM, which combines channel attention mechanism and spatial attention mechanism, is introduced into the network to improve the expression ability of the model for important features. The experimental results show that the model has better performance than the similar neural network model in the task of Cuqiu motion recognition and evaluation, and its accuracy, recall and F1-score can reach 99.05%, 99.04% and 99.0% respectively.
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