Gait waveform analysis tools: application to knee osteoarthritis

骨关节炎（OA）发生在世界各地，可能是最常见的风湿性疾病，导致残疾。对骨性关节炎很重要的机械因素包括由于肢体排列异常和/或以前的损伤导致的关节异常负荷，不寻常的步态模式，肥胖或对关节的过度要求。步态分析是目前唯一可用的手段来测量关节运动和力量在日常生活活动。挑战在于从步态数据中提取临床相关信息。申请人描述了一种基于模式识别技术的新方法，该方法克服了步态波形数据的高度复杂性，同时保持了波形数据中的显著信息。本研究计划建立在这项工作的基础上，通过开发额外的分析工具来分析膝关节OA患者的步态。其中一些工具用于扩展这些模式识别技术，而其他工具则提供了在日常生活活动中更好地估计联合接触力的方法。这些估计将使我们更好地了解膝关节关节炎的原因，以便我们能够改进这种疾病的治疗方法。

Osteoarthritis (OA) occurs worldwide, and is probably the most common rheumatic condition that causes disability. Mechanical factors that are important to OA include abnormal loading of joints due to oddities of limb alignment and/or previous injury, unusual patterns of gait, obesity, or excessive demands made on joints. Gait analysis is the only means currently available to measure joint motion and forces during activities of daily living. The challenge has been the distilling of clinically relevant information from the gait data. A new approach, based on pattern recognition techniques, has been described by the applicant that overcomes the highly complex, nature of gait waveform data, while maintaining the salient information in the waveform data. This research proposal builds upon this work through the development of additional analytical tools for gait analysis of patients with knee joint OA. Some of these tools are for the extension of these pattern recognition techniques, while others are to provide the means to obtain better estimates of the joint contact forces during activities of daily living. These estimates will allow us to better understand the causes of knee arthritis so that we can improve treatments for this disease.