PDF(Pubmed)
Abstract：
UNASSIGNED: Osteoarthritis (OA) is a highly prevalent global musculoskeletal disorder, and knee OA (KOA) accounts for four-fifths of the cases worldwide. It is a degenerative disorder that greatly affects the quality of life. Thus, it is managed through different methods, such as weight loss, physical therapy, and knee arthroplasty. Physical therapy aims to strengthen the knee periarticular muscles to improve joint stability.
UNASSIGNED: Pedobarographic data and pelvis and trunk motion of 56 adults are recorded. Among them, 28 subjects were healthy, and 28 subjects were suffering from varying degrees of KOA. Age, sex, BMI, and the recorded variables are used together to identify subjects with KOA using machine learning (ML) models, namely, logistic regression, SVM, decision tree, and random forest. Surface electromyography (sEMG) signals are also recorded bilaterally from two muscles, the rectus femoris and biceps femoris caput longus, bilaterally during various activities for two healthy and six KOA subjects. Cluster analysis is then performed using the principal components obtained from time-series features, frequency features, and time-frequency features.
UNASSIGNED: KOA is successfully identified using the pedobarographic data and the pelvis and trunk motion with the highest accuracy and sensitivity of 89.3% and 85.7%, respectively, using a decision tree classifier. In addition, sEMG data have been successfully used to cluster healthy subjects from KOA subjects, with wavelet analysis features providing the best performance for the standing activity under different conditions.
UNASSIGNED: KOA is detected using gait variables not directly related to the knee, such as pedobarographic measurements and pelvis and trunk motion captured by pedobarography mats and wearable sensors, respectively. KOA subjects are also distinguished from healthy individuals through clustering analysis using sEMG data from knee periarticular muscles during walking and standing. Gait data and sEMG complement each other, aiding in KOA identification and rehabilitation monitoring. It is important because wearable sensors simplify data collection, require minimal sample preparation, and offer a non-radiographic, safe method suitable for both laboratory and real-world scenarios. The decision tree classifier, trained with stratified k-fold cross validation (SKCV) data, is observed to be the best for KOA identification using gait data.
UNASSIGNED: Pedobarographic data and pelvis and trunk motion of 56 adults are recorded. Among them, 28 subjects were healthy, and 28 subjects were suffering from varying degrees of KOA. Age, sex, BMI, and the recorded variables are used together to identify subjects with KOA using machine learning (ML) models, namely, logistic regression, SVM, decision tree, and random forest. Surface electromyography (sEMG) signals are also recorded bilaterally from two muscles, the rectus femoris and biceps femoris caput longus, bilaterally during various activities for two healthy and six KOA subjects. Cluster analysis is then performed using the principal components obtained from time-series features, frequency features, and time-frequency features.
UNASSIGNED: KOA is successfully identified using the pedobarographic data and the pelvis and trunk motion with the highest accuracy and sensitivity of 89.3% and 85.7%, respectively, using a decision tree classifier. In addition, sEMG data have been successfully used to cluster healthy subjects from KOA subjects, with wavelet analysis features providing the best performance for the standing activity under different conditions.
UNASSIGNED: KOA is detected using gait variables not directly related to the knee, such as pedobarographic measurements and pelvis and trunk motion captured by pedobarography mats and wearable sensors, respectively. KOA subjects are also distinguished from healthy individuals through clustering analysis using sEMG data from knee periarticular muscles during walking and standing. Gait data and sEMG complement each other, aiding in KOA identification and rehabilitation monitoring. It is important because wearable sensors simplify data collection, require minimal sample preparation, and offer a non-radiographic, safe method suitable for both laboratory and real-world scenarios. The decision tree classifier, trained with stratified k-fold cross validation (SKCV) data, is observed to be the best for KOA identification using gait data.
摘要：
■骨关节炎（OA）是一种非常普遍的全球肌肉骨骼疾病，膝关节OA（KOA）占全球病例的五分之四。这是一种严重影响生活质量的退行性疾病。因此，它通过不同的方法进行管理，比如减肥，物理治疗,和膝关节置换术.物理疗法旨在加强膝关节周围肌肉，以改善关节稳定性。
■记录了56名成年人的Pedobarography数据以及骨盆和躯干运动。其中,28名受试者是健康的，28名受试者患有不同程度的KOA。年龄,性别,BMI,记录的变量一起使用机器学习(ML)模型识别KOA受试者，即,逻辑回归，SVM,决策树,和随机森林。表面肌电图（sEMG）信号也从两个肌肉两侧记录，股直肌和股二头肌股长肌，在两名健康受试者和六名KOA受试者的各种活动中进行双边活动。然后使用从时间序列特征获得的主成分进行聚类分析，频率特征，和时频特征。
■使用足动脉造影数据以及骨盆和躯干运动以89.3％和85.7％的最高准确度和灵敏度成功识别了KOA，分别,使用决策树分类器。此外,sEMG数据已成功用于将健康受试者与KOA受试者进行聚类，具有小波分析功能，为不同条件下的站立活动提供最佳性能。
■使用与膝盖不直接相关的步态变量检测KOA，如pedobarography测量和骨盆和躯干运动捕获的pedobarography垫和可穿戴传感器，分别。KOA受试者还通过使用来自行走和站立期间的膝关节周围肌肉的sEMG数据的聚类分析与健康个体区分开。步态数据和sEMG相互补充，协助KOA识别和康复监测。这很重要，因为可穿戴传感器简化了数据收集，需要最少的样品制备，并提供非射线照相，适用于实验室和现实世界场景的安全方法。决策树分类器,用分层k折交叉验证(SKCV)数据训练，观察到使用步态数据进行KOA识别是最佳的。
■记录了56名成年人的Pedobarography数据以及骨盆和躯干运动。其中,28名受试者是健康的，28名受试者患有不同程度的KOA。年龄,性别,BMI,记录的变量一起使用机器学习(ML)模型识别KOA受试者，即,逻辑回归，SVM,决策树,和随机森林。表面肌电图（sEMG）信号也从两个肌肉两侧记录，股直肌和股二头肌股长肌，在两名健康受试者和六名KOA受试者的各种活动中进行双边活动。然后使用从时间序列特征获得的主成分进行聚类分析，频率特征，和时频特征。
■使用足动脉造影数据以及骨盆和躯干运动以89.3％和85.7％的最高准确度和灵敏度成功识别了KOA，分别,使用决策树分类器。此外,sEMG数据已成功用于将健康受试者与KOA受试者进行聚类，具有小波分析功能，为不同条件下的站立活动提供最佳性能。
■使用与膝盖不直接相关的步态变量检测KOA，如pedobarography测量和骨盆和躯干运动捕获的pedobarography垫和可穿戴传感器，分别。KOA受试者还通过使用来自行走和站立期间的膝关节周围肌肉的sEMG数据的聚类分析与健康个体区分开。步态数据和sEMG相互补充，协助KOA识别和康复监测。这很重要，因为可穿戴传感器简化了数据收集，需要最少的样品制备，并提供非射线照相，适用于实验室和现实世界场景的安全方法。决策树分类器,用分层k折交叉验证(SKCV)数据训练，观察到使用步态数据进行KOA识别是最佳的。
