Learning Post-Stroke Gait Training Strategies by Modeling Patient-Therapist Interaction

Learning Post-Stroke Gait Training Strategies by Modeling Patient-Therapist Interaction

Blog Article

For safe and effective robot-aided gait training, it is essential to incorporate the knowledge and expertise of physical therapists.Toward this goal, we directly learn from physical therapists’ demonstrations of curve-novelties manual gait assistance in stroke rehabilitation.Lower-limb kinematics of patients and assistive force applied by therapists to the patient’s leg are measured using a wearable sensing system which includes a custom-made force sensing array.

The collected data is then used to characterize a therapist’s strategies in response to unique gait behaviors found within a patient’s gait.Preliminary analysis shows that knee extension and weight-shifting are the most important features that shape a therapist’s assistance strategies.These key features are then integrated into a virtual impedance model to predict the therapist’s assistive torque.

This model benefits from a goal-directed attractor and representative features that allow intuitive characterization and estimation of a therapist’s assistance strategies.The resulting model is able to accurately capture high-level therapist behaviors over the course of a full training session (r2 = 0.92, RMSE = 0.

23Nm) while still Upright Vacuum Cleaner explaining some of the more nuanced behaviors contained in individual strides (r2 = 0.53, RMSE = 0.61Nm).

This work provides a new approach to control wearable robotics in the sense of directly encoding the decision-making process of physical therapists into a safe human-robot interaction framework for gait rehabilitation.