Control Strategy With Intra-Step Adaptation for Functional Electrical Stimulation Based Ankle-Foot Orthosis for Drop Foot

Every year, millions of people suffer from stroke, spinal cord injury, traumatic brain injury or other neurological or traumatic conditions that affect the peroneal nerve's motor pathway and lead to the weakness of the Tibialis Anterior and the inability to dorsiflex the foot during swing, a condition called Drop Foot. Nowadays, some Functional Electrical Stimulation devices are available commercially to cope with this condition but rely on an open-loop architecture. Closed-loop systems have only been presented in the academia with a time resolution of one step and some robustness issues. Our goal was then to tackle some robustness problems in part by increasing this time resolution to one third of the step. To do that the presented controller follows an Iterative Learning Control strategy that uses the ankle angle to correct the stimulation profile, and uses a three-part division of the swing phase to time-scale and correct this profile, avoiding some instability issues that arise from variations in the gait pattern. The algorithm was implemented and tested in a computational model of the human neuromusculoskeletal system and achieved Root Mean Square Error values of around $2^{\circ}$ being able to outperform the previously presented controller in speed of convergence and robustness.