Abstract
This paper describes the use of fuzzy systems identification for the real time detection of discrete events of a cyclic process. The process is the locomotion of a paraplegic individual, generated using electrical stimulation of paralyzed muscles. Locomotion was represented as a finite state model consisting of five states with five discrete events at the transitions between the states. Event detection was performed in two-part procedure. First, the sensor signals were classified into one of the five states using fuzzy logic. Second, a supervisory algorithm monitored the state classification at each time sample to determine if a state transition occurred. This second supervisory portion forced the “forward” progression through the finite state model and eliminated the occurrence of certain errors.
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