基于混合注意力的轻量级偏瘫步态评估系统  

Hybrid-attention-based Lightweight Hemiplegic Gait Assessment System

作　　者：余炳威 赖舒婷 詹润源 郑坤升 周成菊 冯达钦 潘家辉 YU Bing-Wei;LAI Shu-Ting;ZHAN Run-Yuan;ZHENG Kun-Sheng;ZHOU Cheng-Ju;FENG Da-Qin;PAN Jia-Hui(School of Software,South China Normal University,Foshan 528225,China)

机构地区：[1]华南师范大学软件学院,佛山528225

出　　处：《计算机系统应用》2023年第10期96-105,共10页Computer Systems & Applications

基　　金：国家自然科学基金(62076103);科技创新2030项目(2022ZD0208900)。

摘　　要：脑卒中患者通常会出现偏瘫步态,而视觉式步态分析可以用于检测这些变化.然而,当前公开的病理步态数据集规模较小、缺乏对偏瘫严重程度的详细分级,并且传统的视觉式深度学习步态分析方法通常需要较高计算量和较大参数量,不适用于小规模病理步态数据集.本文设计了一款轻量级偏瘫步态评估系统.系统使用一种轻量级卷积神经网络(convolutional neural networks, CNN)来评估偏瘫步态表现.通过线性拼接不同尺度的分组卷积,低成本地获得高效率特征.系统引入多维度混合的轻量级注意力模块来帮助CNN关注空间和通道维度上的显著特征,从而更好地平衡系统有效性与模型参数量.此外,本文还构建了一个专门用于步态识别的偏瘫模拟步态数据集,为模型训练和测试提供数据支撑.实验结果表明,系统的神经网络仅使用VGG-19 1/53的参数量,将步态识别准确率提高至96.91%,高于预训练后的VGG-19,与其他轻量化SOTA方法相比同样具有精度优势.系统的开发成本低,可部署于移动设备,并支持实时检测,为家庭式病理步态分析提供了一种可行的方案.Stroke patients often exhibit hemiparetic gait,and visual gait analysis can be applied to detect such changes.However,publicly available pathological gait datasets are small in scale and lack detailed grading of hemiplegia severity.Furthermore,state-of-the-art deep learning algorithms for gait analysis usually have a high need for parameter size and computational complexity,leading to low performance on small-scale pathological gait datasets.To address these challenges,this study designs a lightweight hemiplegic gait recognition system.The system utilizes an attention-based lightweight convolutional neural network(CNN)to access hemiplegic gait performance.By linear splicing grouped convolution at different scales,high-efficiency features can be obtained at a low cost.Additionally,a multidimensional hybrid lightweight attention module is introduced to assist CNN in focusing on distinctive features in both spatial and channel dimensions,achieving a good balance between system effectiveness and lightweight design.Moreover,a hemiplegic simulation gait dataset is constructed,specifically for hemiplegic gait recognition to support model training and testing.The results demonstrate that the proposed network that uses only 1/53 parameters of VGG-19 improves the accuracy of gait recognition to 96.91%,which is higher than that of pre-train VGG-19.Compared with other lightweight SOTA methods,it also has the advantage of accuracy.The system has low development costs and can be deployed on mobile devices.It supports real-time detection,providing a feasible solution for home-based pathological gait analysis.

关 键 词：卒中康复 步态分析 偏瘫数据集 轻量级神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] R743.3[医药卫生—神经病学与精神病学]