Grant: Collaborative Research and Development Grants

The research conducted in our laboratory uses mathematical models for assessing movement adaptations or compensations associated with osteoarthritis (OA). An important goal is to predict the progression of OA, and to predict the outcomes of surgical interventions for OA for an individual. Achieving this goal of personalized care is challenged by the observation that OA symptoms, progression and clinical outcomes from surgical and non surgical interventions are unique to an individual. Ironically patient specific models require that research studies be conducted with large patient populations in order to classify a broad selection of attributes. For our research, accurate measures of three dimensional human motion, and estimation of the underlying forces that generated such motion, are derived using expensive cameras that track the location of markers placed on the body combined with sensors that measure foot-ground contact forces. The data collection volumes are small, and experimental protocols are time consuming and manpower intensive. More importantly for our research the repeatability of measures is poor because the repeatability and reliability of marker placement is poor, which precludes the large population studies of OA that we need to achieve our goals. HAS-Motion (our industrial partner) has developed a markerless 3D motion capture system that uses an array of standard video cameras and deep neural networks to record human motion consistently and quickly across different environments. This system has the potential to allow us to conduct longitudinal and multicentre studies of OA. Before we can adopt this system we must verify the accuracy, repeatability, and reliability of this system in the context of the class of movements that are important to our research, and to analyze and interpret the data in the context of the signals and models that are commonly used in our research. We also propose to collaborate with HAS-Motion to integrate musculoskeletal models and environmental constraints into their solution. This three year project will support one PDF, one PhD student, two Master's students, one lab technician, and three undergraduate summer students.