PREDICTING QUADRICEPS MUSCLE-ACTIVITY DURING GAIT WITH AN AUTOMATIC RULE DETERMINATION METHOD

It has been suggested that control using a skill-based expert system c an be applicable to gait restoration. Rule-based systems have several advantages for this application: they generate a fast response (they a re 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, Th e 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 rul e estimated the muscle activity pattern using the sensor information. The algorithm was tested using data recorded from six able-bodied indi viduals during ground level walking, with and without ankle-foot ortho ses. The data show ed that gait variability will increase in able-bodi ed subjects when the motion of ankle joints is restricted, thus, provi ding a good test for generalization. The experimental results illustra te performance of the production rule that estimates quadriceps muscle group activity pattern for ground level walking in able-bodied subjec ts.