A Personalized FES-Assisted Foot Drop Correction Device via a Real-Time Fuzzy Controller based on the Patient’s Healthy Foot Condition

Foot Drop (FD) is considered a symptom of a neurological disorder, not a condition itself and is often caused by paralysis or weakness of the dorsiflexor muscle. Consequently, patients will not be able to lift the front part of their feet in the swing phase of gaiting. In this study, a wearable circuit generated functional electrical stimulation (FES) granting impulses to the tibialis anterior muscle via surface electrodes attached over the superficial peroneal nerve of 9 subjects (5 post-stroke patients and four healthy individuals) to provide the desired dorsiflexion. The novelty of this paper is personalizing the set point of the ankle dorsiflexion. This setpoint is continually updated according to the patient's gait pattern of the normal foot instead of using a predetermined value. A bipedal gait phase detection algorithm based on finite state machines was adopted for both feet to apply FES at the right time, and a real-time fuzzy controller adjusted the stimulation parameters. Field evaluations were conducted to validate the proposed method in motion analysis laboratories using force plates, foot scanners, sEMG, high-speed cameras, and motion landmarks. Foot drop corrected with an accuracy of over 90%, and patients described a better user experience in comparison with commercial FES-assisted devices.