论文标题
计算机视觉和异常患者步态评估机器学习模型的比较
Computer Vision and Abnormal Patient Gait Assessment a Comparison of Machine Learning Models
论文作者
论文摘要
步态异常，其相关的跌倒和并发症患者发病率高，死亡率。计算机视觉检测，预测患者步态异常，评估跌倒风险，并作为医生的临床决策支持工具。本文对计算机视觉，机器学习模型进行异常患者的步态评估进行了系统的审查。计算机视觉在步态分析中是有益的，它有助于捕获患者的姿势。一些文献表明，使用不同的机器学习算法，例如SVM，ANN，K-Star，Random Forest，KNN等，以对研究患者步态异常的特征进行分类。
Abnormal gait, its associated falls and complications have high patient morbidity, mortality. Computer vision detects, predicts patient gait abnormalities, assesses fall risk and serves as clinical decision support tool for physicians. This paper performs a systematic review of how computer vision, machine learning models perform an abnormal patient's gait assessment. Computer vision is beneficial in gait analysis, it helps capture the patient posture. Several literature suggests the use of different machine learning algorithms such as SVM, ANN, K-Star, Random Forest, KNN, among others to perform the classification on the features extracted to study patient gait abnormalities.
