Intelligent Diagnosis on Anterior Cruciate Ligament Deficiency Based on Plantar Pressure and ConvLSTM Neural Network

doi:10.13209/j.0479-8023.2023.089

Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2024, Vol. 60 ›› Issue (1): 109-117.DOI: 10.13209/j.0479-8023.2023.089

Previous Articles Next Articles

Intelligent Diagnosis on Anterior Cruciate Ligament Deficiency Based on Plantar Pressure and ConvLSTM Neural Network

LI Dai¹, WANG Tianmu^2,3, ZHANG Si¹, QIN Yue^2,3, XIE Fugui^2,3, LIU Xinjun^2,3, NIE Zhenguo^2,3,†, HUANG Hongshi^1,†

1. 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 2. The State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084 3. Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Department of Mechanical Engineering, Tsinghua University, Beijing 100084

Received:2022-12-26 Revised:2023-05-20 Online:2024-01-20 Published:2024-01-20
Contact: NIE Zhenguo, E-mail: zhenguonie(at)tsinghua.edu.cn, HUANG Hongshi, E-mail: huanghs(at)bjmu.edu.cn

基于足底压力和卷积长短期记忆神经网络的前交叉韧带断裂智能辅助诊断

李玳¹, 王天牧^2,3, 张思¹, 秦跃^2,3, 谢福贵^2,3, 刘辛军^2,3, 聂振国^2,3,†, 黄红拾^1,†

1. 北京大学第三医院运动医学科, 北京大学运动医学研究所, 运动医学关节伤病北京市重点实验室, 运动创伤治疗技术与器械教育部工程研究中心, 北京 100191 2. 摩擦学国家重点实验室, 清华大学机械工程系, 北京 100084 3. 精密/超精密制造设备与控制北京市重点实验室, 清华大学机械工程系, 北京 100084

通讯作者: 聂振国, E-mail: zhenguonie(at)tsinghua.edu.cn, 黄红拾, E-mail: huanghs(at)bjmu.edu.cn
基金资助:
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

Abstract

Abstract:

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. Classification 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.

Key words: intelligent diagnosis, anterior cruciate ligament deficiency (ACLD), plantar pressure, deep learning; ConvLSTM neural network

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。

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

LI Dai, WANG Tianmu, ZHANG Si, QIN Yue, XIE Fugui, LIU Xinjun, NIE Zhenguo, HUANG Hongshi. Intelligent Diagnosis on Anterior Cruciate Ligament Deficiency Based on Plantar Pressure and ConvLSTM Neural Network[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2024, 60(1): 109-117.

李玳, 王天牧, 张思, 秦跃, 谢福贵, 刘辛军, 聂振国, 黄红拾. 基于足底压力和卷积长短期记忆神经网络的前交叉韧带断裂智能辅助诊断[J]. 北京大学学报自然科学版, 2024, 60(1): 109-117.

[1]	HUANG Hongshi, WANG Zhengfei, XU Guoxiong, LI Wenxin, ZHANG Si, ZHANG Dongxia, AO Yingfang. Anterior Cruciate Ligament Deficiency Auxiliary Diagnosis Based on Plantar Pressure Information during Walking [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2019, 55(5): 859-864.
[2]	WANG Mo, FAN Zhuochen, MA Hao, PENG Chen, ZHANG Sixu, WANG Xin’an. Research of Foot Plantar Pressure Scanning System Design Based on Sensor Array [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2018, 54(6): 1159-1165.
[3]	HUANG Hongshi, QIU Jianwei, YU Yuanyuan, YANG Jie, AO Yingfang, LUO Dingsheng. Similarities Analysis on Center of Pressure Trajectories for Healthy Volunteers during Walking and Jogging [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2015, 51(6): 1035-1039.

Intelligent Diagnosis on Anterior Cruciate Ligament Deficiency Based on Plantar Pressure and ConvLSTM Neural Network

基于足底压力和卷积长短期记忆神经网络的前交叉韧带断裂智能辅助诊断

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 3

Recommended Articles

Metrics