Research overview

Daily life requires navigating diverse and complex demands, such as changing speeds, turning, and stepping over or around obstacles. Unfortunately, over a quarter of community-dwelling adults over the age of 65 and half of older adults with neurological deficits fall annually. A major focus in our lab is understanding how the nervous, muscular, and skeletal systems interact to maintain balance (or not!) while navigating these demands. To identify general principles underlying the control of balance during movement, our studies span the spectrum from motor expertise (e.g., ballet dancers) to motor impairment (e.g., stroke survivors). Through a comprehensive approach combining laboratory-based experiments, real-world monitoring, data science, and computational modeling, we aim to provide guidance for clinical decision-making and device design to improve mobility in daily life and mitigate the risk of falling.

• We perform human biomechanics experiments (more info here) in which we record movement kinematics and muscle activity during various walking tasks in controlled lab settings and uncontrolled outdoor environments. We also use wearable sensors to monitor physical activity, balance, and falls during daily-life, providing real-world data to complement our biomechanics experiments. We then employ data science techniques such as machine learning to identify patterns and relationships between muscle activity, movement kinematics, and daily-life mobility. This comprehensive approach allows us to gain insight into the neuromuscular control of movement, the integration of balance into this control, and how these processes are affected by aging, disease, and injury.  
• We use balance perturbations (more info here) to study how people maintain their balance (or not) while performing different tasks in which falls often occur in the real-world, such as standing, arising from a chair, walking, etc. Our suite of perturbations leverages human-device interactions and mechatronics to allow for precise and varied challenges to balance. These approaches allow us to gain insight into the muscle, tendon, sensory, and cognitive contributions to balance control and changes thereof due aging, disease, and injury. 
• We use advanced musculoskeletal modeling, simulation, and optimization (more info here) techniques to test hypotheses regarding the neuromuscular control of movement, estimate quantities that cannot be measured experimentally, and make individual-specific predictions to guide rehabilitation and device design.