首页> 外文会议>Proceedings of the 2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics >Walking motion analysis of intermittent claudication and its application to medical diagnosis
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Walking motion analysis of intermittent claudication and its application to medical diagnosis

机译:间歇性lau行的步行运动分析及其在医学诊断中的应用

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There are mainly two kinds of diseases in intermittent claudication. One is lumbar spinal canal stenosis (LSS) and the other is peripheral arterial disease (PAD). Differentiating LSS and PAD is a critical issue. Wrong differentiation might cause amputation of lower extremities. At small clinics and hospitals, simple and cheap differentiation system is required. Concerning this, this paper investigated walking motions of the patients. The subject with LED markers walked on the treadmill until she or he felt pain. We recorded the walking motion by camera and tracked the LED markers. Treadmill enables to measure walking motion for a long time in a small space, and LED marker provides position of every joint in the walking. Then, we can get the information such as joint angle trajectory, hemi-foot step, stance and swing phases without any other sensors like foot switch or force plate. We compared walking motions of healthy persons, LSS patients and PAD patients, found their features and 3 factors for disease differentiation; average bending angle of knee joint at the start of stance phase, average dorsiflexion angle of ankle joint, and average hemi-foot step length. The results indicate that 2 dimensional images of walking motion for several seconds are enough for deriving the factors. Then, we can construct the simple examination system for the disease differentiation.
机译:间歇性lau行主要有两种疾病。一种是腰椎管狭窄(LSS),另一种是外周动脉疾病(PAD)。区分LSS和PAD是一个关键问题。错误的分化可能会导致下肢截肢。在小型诊所和医院中,需要简单廉价的区分系统。对此,本文研究了患者的步行运动。带有LED标记的对象在跑步机上行走,直到感到疼痛为止。我们通过摄像头记录了步行动作,并跟踪了LED标记。跑步机可在很小的空间内长时间测量步行运动,LED标记可显示步行中每个关节的位置。然后,无需脚踏开关或压板等任何其他传感器,我们就可以获取诸如关节角度轨迹,半脚步距,姿态和挥杆相位之类的信息。我们比较了健康人,LSS患者和PAD患者的步行运动,发现了他们的特征和3种导致疾病分化的因素。站立阶段开始时膝关节的平均弯曲角度,踝关节的平均背屈角度和平均半脚步长。结果表明,步行几秒钟的二维图像足以推导这些因素。然后,我们可以构建用于疾病鉴别的简单检查系统。

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