摘要
提出一种基于卷积长短期记忆神经网络的深度学习模型PressureConvLSTM,用来提取行走过程中足底压力的空间特征和时序特征,并进行步态分类。通过对前交叉韧带断裂患者的足底压力数据分析,实现智能辅助诊断。结合临床数据的实验结果表明,PressureConvLSTM模型对前交叉韧带断裂的辅助诊断,能够达到95%的预测准确度;与卷积神经网络等其他模型相比,准确度得到大幅度提升。
Based on Convolutional Long-Short Term Memory Neural Network,the authors proposed a deep learning method PressureConvLSTM to extract features during walking in both spatial and temporal dimensions.Classi-fication based on plantar pressure of anterior cruciate ligament deficiency(ACLD)was applied to distinguish walking gait information.Experiment results combined with clinical data showed that PressureConvLSTM model obtained 95%test accuracy when diagnosing ACLD,which was well performed over other traditional deep learning models.
作者
李玳
王天牧
张思
秦跃
谢福贵
刘辛军
聂振国
黄红拾
LI Dai;WANG Tianmu;ZHANG Si;QIN Yue;XIE Fugui;LIU Xinjun;NIE Zhenguo;HUANG Hongshi(Department of Sports Medicine,Peking University Third Hospital,Institute of Sports Medicine of Peking University,Beijing Key Laboratory of Sports Injuries,Engineering Research Center of Sports Trauma Treatment Technology and Devices(Ministry of Education),Beijing 100191;The State Key Laboratory of Tribology,Department of Mechanical Engineering,Tsinghua University,Beijing 100084;Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control,Department of Mechanical Engineering,Tsinghua University,Beijing 100084)
出处
《北京大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2024年第1期109-117,共9页
Acta Scientiarum Naturalium Universitatis Pekinensis
基金
国家自然科学基金–区域创新发展联合基金(U23A20471)
北京市科技新星计划交叉合作课题(20230484412)
北京市自然科学基金–海淀原始创新联合基金(L222138)
北京大学第三医院创新转化基金(BYSYZHKC2022119)和北京大学第三医院临床重点项目(BYSYZD2021012)资助。
关键词
智能诊断
前交叉韧带断裂
足底压力
深度学习
卷积长短期记忆神经网络
intelligent diagnosis
anterior cruciate ligament deficiency(ACLD)
plantar pressure
deep learning
ConvLSTM neural network
