Khan Taha, Westin Jerker, Dougherty Mark
Computer Engineering, School of Technology and Business Studies, Dalarna University, 79188, Falun, Sweden.
Open Biomed Eng J. 2013;7:1-8. doi: 10.2174/1874120701307010001. Epub 2013 Jan 15.
This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson's disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject's body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.
本文提出了一种基于计算机视觉的无标记方法，用于检测帕金森病患者（PWP）的步态损伤。该系统基于这样一种理念，即正常人体在步态过程中通过以双脚为支撑基础，使身体姿势与重力轴（AOG）对齐来保持平衡。相比之下，帕金森病患者由于肌肉强直，不太能够使身体与重力轴对齐，因此似乎向前倾倒。正常步态呈现出周期性的步幅周期，双腿之间的步幅角度约为45度，而帕金森病患者行走时步幅角度缩短，步幅周期之间的变异性较大。为了分析帕金森步态（PG），对受试者进行了多个步态周期的录像。使用颜色分割方法对受试者的身体进行分割以形成轮廓。对轮廓进行骨架化处理以提取运动线索。分析的运动线索包括步幅周期（基于骨架的腿部循环运动）和姿势倾斜（基于骨架倾斜躯干与重力轴之间的角度）。虚拟完美步态模式与受试者步态模式之间的余弦相似度对4名正常对照者和3名帕金森病患者的帕金森步态识别率达到了100%。结果表明，该方法是一种有前景的工具，可用于家庭环境中的帕金森步态评估。
