首页|基于双模态智能鞋的偏瘫患者下肢肌力定量评估

基于双模态智能鞋的偏瘫患者下肢肌力定量评估

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卒中可导致患者下肢运动能力受损和偏瘫.准确评估下肢运动能力对诊断和康复很重要.为了使每次测试都可随时追溯,并避免主观性,我们使用配备有压敏鞋垫和惯性传感单元的双模态智能鞋进行数字化评估,并设计了一个包括左转弯和右转弯的 5 米步行测试方案.数据收集自 23 名患者和 17 名健康受试者.两位医生对所有患者的下肢的运动能力进行了观察,并使用医学研究委员会的五级肌肉检查量表进行了评估.两位医生对同一患者的平均评分被用作真实值.使用我们开发的特征集,在对患者和健康受试者进行分类时达到了100%的准确性.对于患者的肌肉力量,使用我们的特征集和回归方法获得了 0.143 的平均绝对误差和 0.395 的最大误差,与每位医生的评分相比 (平均绝对误差:0.217,最大误差:0.5) ,更接近实际情况.因此,我们验证了使用这种智能鞋客观准确地评估中风患者下肢肌肉力量的可能性.
Dual-modality smart shoes for quantitative assessment of hemi-plegic patients'lower limb muscle strength
Stroke can lead to the impaired motor function in patients'lower limbs and hemiplegia.Accurate assessment of lower limb motor ability is important for diagnosis and rehabilitation.To digitalize such assessments so that each test can be traced back at any time and subjectivity can be avoided,we test how dual-modality smart shoes equipped with pressure-sensitive insoles and inertial measurement units can be used for this purpose.A 5 m walking test protocol,including the left and right turns,is designed.The data are collected from 23 patients and 17 healthy subjects.For the lower limbs'motor ability,the tests are performed by two physicians and assessed using the five-grade Medical Research Council scale for muscle examination.The average of two physicians'scores for the same patient is used as the ground truth.Using the feature set we developed,100%accuracy is achieved in classifying the patients and healthy subjects.For patients'muscle strength,a mean absolute error of 0.143 and a maximum error of 0.395 are achieved using our feature set and the regres-sion method;these values are closer to the ground truth than the scores from each physician(mean absolute error:0.217,maximum error:0.5).We thus validate the possibility of using such smart shoes to objectively and accurately evaluate the muscle strength of the lower limbs of stroke patients.

strokemachine learningsmart shoeslower limbs'muscle strength

龙华君、李洁、李瑞、刘新峰、程敬原

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中国科学技术大学大数据学院,安徽合肥 230027

中国科学技术大学附属第一医院神经内科,安徽合肥 230001

中国科学技术大学计算机科学与技术学院,安徽合肥 230027

卒中 机器学习 智能鞋 下肢肌力

Fundamental Research Funds for the Central Universities

2150110020

2024

中国科学技术大学学报
中国科学技术大学

中国科学技术大学学报

CSTPCD北大核心
影响因子:0.421
ISSN:0253-2778
年,卷(期):2024.54(1)
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