Recognition delay and recognition rate of knee motor intention recognized by electromyogram and continuous hidden Markov model

A motor rehabilitation robot applied patient´s intention can enhance the rehabilitation efficacy. Continuous hidden Markov models of knee flexion and extension are trained using autoregressive model coefficients of knee flexor and extensor electromyograms. The patient´s intention of knee movement are recognized by the trained continuous hidden Markov models and the user´s knee flexor and extensor electromyograms. The suggested method was applied to a knee joint rehabilitation robot for identifying the suggested classification method in real time. A nondisabled healthy subject wore the robot, and its knee joint was extended when the subject´s intention was recognized as `Extension.´ The robot´s knee joint was bended when the subject´s intention was recognized as `Flexion´. If the user´s intention wasn´t recognized as `Extension´ nor `Flexion´, the robot´s knee joint was remained stationary. The robot had followed properly the subject´s knee joint motor intention. As a result of hidden Markov model classification, the robot reflects the subject´s intensions with the recognition delay shorter than 200 msec and the recognition rate of 94.23 %. The results show the suggested method has good potential as a bio-signal classification method for a motor rehabilitation robot.