摘要翻译：
能够预测临床参数以诊断患者的步态障碍在计划治疗中具有重要价值。已知步态、步长和行走速度等决策参数是诊断步态障碍的关键。该项目的目的是通过两种方法预测决策参数，然后给出患者是否需要治疗的建议。一方面，我们使用临床测量的参数，如踝关节背屈、年龄、步行速度、步长、步长、身高平方体重(BMI)等来预测决策参数。在第二种方法中，我们使用从病人在诊所的行走测试中录制的视频，以提取病人关节随时间变化的坐标，并预测决策参数。最后，利用决策参数对患者的步态障碍强度进行预分类，从而对患者是否需要治疗做出决策。
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英文标题：
《Clinical Parameters Prediction for Gait Disorder Recognition》
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作者：
Soheil Esmaeilzadeh, Ouassim Khebzegga, Mehrad Moradshahi
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  Being able to predict clinical parameters in order to diagnose gait disorders in a patient is of great value in planning treatments. It is known that \textit{decision parameters} such as cadence, step length, and walking speed are critical in the diagnosis of gait disorders in patients. This project aims to predict the decision parameters using two ways and afterwards giving advice on whether a patient needs treatment or not. In one way, we use clinically measured parameters such as Ankle Dorsiflexion, age, walking speed, step length, stride length, weight over height squared (BMI) and etc. to predict the decision parameters. In a second way, we use videos recorded from patient's walking tests in a clinic in order to extract the coordinates of the joints of the patient over time and predict the decision parameters. Finally, having the decision parameters we pre-classify gait disorder intensity of a patient and as the result make decisions on whether a patient needs treatment or not.
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PDF链接：
https://arxiv.org/pdf/1806.04627