Movement is a basic feature of human existence and is intimately connected to our quality of life. Movement training can prevent injury, improve athletic performance, delay musculoskeletal disease and accelerate rehabilitation. Until now, training has been limited in scope to specialized training facilities and limited in effectiveness to the verbal recommendations of physical trainers. In this project, the PI's goal is to expand the scope and effectiveness of human movement training in order to extend health and lifestyle benefits to the general public. The primary outcome of this research at the intersection of robotics, biomechanics and human-computer interaction will be gait modeling software that integrates sensor data in real time to compute kinematics, kinetics and joint/tendon forces to predict adjustments to motion parameters to achieve an end goal. The PI's hypothesis is that wearable computation can fundamentally change the way people move. The miniaturization of computational hardware, as well as advances in movement analysis algorithms, wearable sensors and feedback devices, all serve as catalysts for a new level of human interaction. The work will focus on everyday repetitive dynamic activities like walking, running and jumping, whose nature is that information gathered and analyzed during one cycle can be applied to subsequent cycles to achieve gradual improvement. Feedback that builds upon advances in robotics, motion tracking and biomechanical modeling (that have led to efficient monitoring and simulation of complex multi-degree of freedom systems) will be provided in real time and will be adaptive, robust with respect to cycle-to-cycle variations, and user specific. For maximum impact, movement training will be accessible to the average citizen instead of confined to the laboratory or clinic. To this end, the PI will create a system that could be used while walking around the house, hiking outdoors or running in a gymnasium. Preliminary experiments in motion tracking, dynamic analysis and wearable feedback for gait retraining to reduce knee loading associated with injury and arthritis will be extended to evaluate which types of sensing and feedback, in combination with algorithms to detect and analyze motion anomalies, are effective outside of the laboratory. The PI will conduct a series of experiments to validate the portable solution, comparing it with results obtained in a fully instrumented laboratory setting and assessing how the effects of training are retained over time.
Broader Impacts: The PI argues that with wearable retraining devices middle-aged women could be taught to walk in a way that slows or prevents osteoarthritis as well as injury at the hips, ankles, etc., college athletes could be trained to jump and land while playing volleyball so as to prevent ACL and other common sports-related injuries (while perhaps improving performance as well), and victims of stroke and other neurological disorders could be rehabilitated at home instead of at the clinic. This project will focus initially on walking and knee joint loading, a problem of immediate importance for the aging U.S. population. The PI will provide open source software (e.g., for monitoring sensors and predicting target gait parameters) and wearable hardware licensing to promote adaptation of project outcomes to other applications.
