Advanced Data Modelling
A key focus of our research is advancing data and biomechanical modeling in the field of clinical human movement biomechanics. Human movement biomechanics research often integrates precise instrumentation and tools for comprehensive biomechanics assessment systems. This instrumentation can provide large volumes of synchronized, time-varying biosignals that represent information relevant to movement dynamics, such as three-dimensional location, displacement, acceleration, forces, moment and neuromuscular activation of the body and its constituent parts.
Our research has focused on adapting multivariate statistical analysis techniques and computational models for reducing large volumes of correlated biosignals and distilling clinically relevant information. The application of advanced data modeling has allowed us to provide unique insight into the dimensionality of healthy and pathologic movement mechanics, with an emphasis on the changing mechanical environment of joints with osteoarthritis progression and in response to orthopaedic surgical treatments.
Joint Kinetics Modelling
Forces that propagate through the musculoskeletal system are paramount for health, and imbalances in terms of magnitude, timing, duration, and frequency of loading can have consequences for tissue health. The most common approach to modeling joint level kinetics during dynamic activities (inverse dynamics) uses Newtonian mechanics with input positional information of body segments and measured external human-environment kinetics (i.e. ground contact) to model net resultant joint forces and moments. These models do not account for variations in muscle force contributions to joint loading and therefore can underestimate actual forces borne by joint tissues.
Therefore, another major arm of our research has been in representing neuromuscular activation signals to interpret the contribution of muscle forces to the mechanical environment of joints during dynamic activity, and the implementation of musculoskeletal models and optimization routines that include muscle activation dynamics to provide further insight into joint contact kinetics during human movement. We further include integrated wearable accelerometry to model in vivo loading frequency. These tools have allowed us to advance our understanding of the implications of varying kinetic magnitudes and patterns in osteoarthritis and other clinical applications.
