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OA很重要的力学因素包括由于肢体对线异常和/或既往损伤导致的关节负荷异常、步态异常、肥胖或对关节的过度要求。步态分析是目前唯一可用于测量日常生活活动中关节运动和力量的方法。挑战在于从步态数据中提取临床相关信息。申请人已经描述了一种基于模式识别技术的新方法，其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。这项研究建议建立在这项工作的基础上，通过开发额外的分析工具，用于膝关节OA患者的步态分析。这些工具中的一些是用于这些模式识别技术的扩展，而另一些是提供在日常生活活动期间获得更好的关节接触力估计的手段。这些估计将使我们能够更好地了解膝关节炎的原因，以便我们能够改善这种疾病的治疗。

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.