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

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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.