Mon, April 5
Speaker: Qi Shao, PhD (Postdoctoral Candidate)
Title: EMG-driven modeling and its application in simulating rehabilitation
Abstract: The human neuromusculoskeletal system is complicated and different muscles are finely coordinated to accomplish various tasks. Electromyography (EMG) includes real-time information about the electrical activity of a specific muscle. EMG-driven biomechanical models have been developed to estimate muscle forces, and they have great potential in studying the rehabilitation of patients with neurological disorder.
In the first study, I will introduce a biomechanical model to estimate the corrective increases in muscle activation patterns that would enable post-stroke patients to walk with a similar joint kinematics to that of an unimpaired person during functional electrical stimulation (FES) training. The approach was based on an EMG-driven model to estimate joint moments. These calculated corrective muscle activation changes can be used in studying FES protocols, to determine the feasibility of gait retraining with FES for a given subject and to determine which protocols are most reasonable. In the second study, an EMG-driven forward dynamics model was developed. The model used EMGs, kinematics and ground reaction force data as inputs, and calculated muscle forces, as well as joint torques to drive a forward simulation during the stance phase of normal gait. This EMG-driven approach has the advantage of identifying different muscle activation patterns, and it has great potential in applications to the rehabilitation of patients with neurological disorder. In the third study, a biomechanical model was developed to estimate anterior tibial translation and ligament loading in the healthy and anterior cruciate ligament (ACL)-deficient knee joint during gait. This model can take into account the abnormal muscle activation strategies of ACL-deficient patients, and can be used to explore differences in ligament biomechanics associated with different rehabilitation protocols.