Finite state model of locomotion for functional electrical stimulation systems.
A finite state model of locomotion was developed to simplify a controller design for motor activities of handicapped humans. This paper presents a model developed for real time control of locomotion with functional electrical stimulation (FES) assistive systems. Hierarchical control of locomotion was adopted with three levels: voluntary, coordination and actuator level. This paper deals only with coordination level of control. In our previous studies we demonstrated that a skill-based expert system can be used for coordination level of control in multi-joint FES systems. Basic elements in this skill-based expert system are production rules. Production rules have the form of If-Then conditional expressions. A technique of automatic determination of these conditional expressions is presented in this paper. This technique for automatic synthesis of production rules uses fuzzy logic and artificial neural networks (ANN). The special class of fuzzy logic elements used in this research is called preferential neurons. The preferential neurons were used to estimate the relevance of each of the sensory inputs to the recognition of patterns defined as finite states. The combination of preferential neurons forms a preferential neural network. The preferential neural network belongs to a class of ANNs. The preferential neural network determined the set of finite states convenient for a skill-based expert system for different modalities of locomotion.
Popović
D B
DB
Faculty of Electrical Engineering, Univerisity of Belgrade, Yugoslavia.
D000843:Ankle Joint / Q000502:physiology; D001696:Biomechanical Phenomena; D004558:Electric Stimulation; D004599:Electric Stimulation Therapy; D005103:Expert Systems; D017143:Fuzzy Logic; D006621:Hip Joint / Q000502:physiology; D006801:Humans; D007719:Knee Joint / Q000502:physiology; D008124:Locomotion; D008954:Models, Biological; D009132:Muscles / Q000502:physiology; D009424:Nervous System Physiological Phenomena; D016571:Neural Networks, Computer; D016138:Walking