Kargar B Amir H, Mollahosseini Ali, Struemph Taylor, Pace Wilson, Nielsen Rodney D, Mahoor Mohammad H
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3492-5. doi: 10.1109/EMBC.2014.6944375.
Get-Up-and-Go Test is commonly used for assessing the physical mobility of the elderly by physicians. This paper presents a method for automatic analysis and classification of human gait in the Get-Up-and-Go Test using a Microsoft Kinect sensor. Two types of features are automatically extracted from the human skeleton data provided by the Kinect sensor. The first type of feature is related to the human gait (e.g., number of steps, step duration, and turning duration); whereas the other one describes the anatomical configuration (e.g., knee angles, leg angle, and distance between elbows). These features characterize the degree of human physical mobility. State-of-the-art machine learning algorithms (i.e. Bag of Words and Support Vector Machines) are used to classify the severity of gaits in 12 subjects with ages ranging between 65 and 90 enrolled in a pilot study. Our experimental results show that these features can discriminate between patients who have a high risk for falling and patients with a lower fall risk.
起身行走测试通常由医生用于评估老年人的身体活动能力。本文提出了一种使用微软Kinect传感器对起身行走测试中的人体步态进行自动分析和分类的方法。从Kinect传感器提供的人体骨骼数据中自动提取两种类型的特征。第一种特征与人体步态有关（例如，步数、步长时间和转弯时间）；而另一种则描述解剖结构（例如，膝关节角度、腿部角度和肘部之间的距离）。这些特征表征了人体身体活动能力的程度。在一项初步研究中，使用了最先进的机器学习算法（即词袋模型和支持向量机）对12名年龄在65岁至90岁之间的受试者的步态严重程度进行分类。我们的实验结果表明，这些特征可以区分跌倒风险高的患者和跌倒风险低的患者。
