A video-based system for 3D gait analysis of elderly

加拿大正在经历人口的重大变化，人口正在迅速老龄化。这一重大的人口结构转变将对个人、社会和国家生活的许多方面产生深远影响。例如，老年人最大的危险之一是意外跌倒，老年人与伤害相关的住院治疗中有62%是跌倒的结果。因此，必须在老年人保健领域进行创新，以确定支持老年人的创新技术。有趣的是，步态异常现在被认为是最一致的跌倒预测因素之一。在这种背景下，本研究项目的总体目标是开发一种多摄像机系统来识别老年人步态的相关临床参数，以便进行早期跌倒风险评估和预防。虽然目前大多数步态分析技术依赖于复杂而昂贵的光电运动捕获系统，但本研究的目标是开发一种新的低成本、易于使用的临床应用系统。它将基于分布在跑步机周围的彩色深度同步摄像头，并将使用从轮廓形状方法来三维重建受试者在步态周期的每个时间步的体积或形状。然后进行统计分析和机器学习，以捕捉步态的结构和过渡特征，用于跌倒风险评估。将在真实的临床环境中进行评估，以验证该方法。

Canada is experiencing significant changes in its population, which is aging rapidly. This major demographic transformation will have far-reaching repercussions on many aspects of personal, social and national life. For instance, one of the greatest hazards for elderly is accidental falls and 62% of injury-related hospitalizations for seniors are the result of falls. It is therefore essential to innovate in the area of elder healthcare to identify innovative technologies to support the elderly. Interestingly enough, gait abnormalities are now considered among the most consistent predictors of falls. In this context, the general objective of this program of research is to develop a multiple-camera system to identify relevant clinical parameters in elder gait for early fall risk assessment and prevention. While most current gait analysis techniques rely on complex and expensive opto-electronic motion capture systems, this research aims at developing a new low-cost, easy-to-use system for use in a clinical setting. It will be based on color-depth synchronized cameras distributed around a treadmill and will use a shape-from-silhouette methodology for the 3D reconstruction of the subject's volume or shape at each time step of the gait cycle. Then statistical analyzes and machine learning will be carried out in order to capture structural and transitional characteristics of the gait for fall risk assessment. Evaluation in a real clinical environment will be conducted to validate the methodology.