A hybrid method based on fuzzy inference and non-linear oscillators for real-time control of gait

Robust generation of motor commands for real-time control of locomotion with artificial means is crucial for human safety. This paper addresses the combination of fuzzy inference for determination of rules with a non linear oscillator system, as generators of motor commands for the control of human leg joints during walking, by means of external gait compensators, e.g. exoskeletons, functional electrical stimulation or hybrid systems. The response of the proposed method is evaluated for variations in stride frequency and step length. The testing during gait conditions is performed considering inertial sensing as feedback in a simulation study. The reference data considered is obtained in multiple experiments with healthy subjects walking with a controllable exoskeleton designed to compensate quadriceps weakness. A model of the operation of the knee joint compensation provided by the exoskeleton is obtained as reference to evaluate the method based on real data. The results demonstrate the benefits of both incorporating a) the fuzzy inference system in cyclical decision making for generation of motor commands and b) the dynamic adaptation of the timing parameters of the external compensator provided by the van der Pol oscillator.