Predicting quadriceps muscle activity during gait with an automatic rule determination method.

Abstract

It has been suggested that control using a skill-based expert system can be applicable to gait restoration. Rule-based systems have several advantages for this application: they generate a fast response (they are not computationally intensive) and they are easy to comprehend and implement. A major problem with using such systems is the inability of users to determine its rules. In this study, an automatic method for obtaining the production rules from a set of examples is described. The rule base was automatically induced from a model which used external sensor signals as inputs and electromyogram (EMG) patterns as outputs. The method is based on the minimization of entropy. A production rule estimated the muscle activity pattern using the sensor information. The algorithm was tested using data recorded from six able-bodied individuals during ground level walking, with and without ankle-foot orthoses. The data showed that gait variability will increase in able-bodied subjects when the motion of ankle joints is restricted, thus, providing a good test for generalization. The experimental results illustrate performance of the production rule that estimates quadriceps muscle group activity pattern for ground level walking in able-bodied subjects.