TY - JOUR
T1 - Application of a neuro-fuzzy network for gait event detection using electromyography in the child with cerebral palsy
AU - Lauer, Richard T.
AU - Smith, Brian T.
AU - Betz, Randal R.
N1 - Funding Information:
Manuscript received June 11, 2004; revised January 23, 2005. This work was supported in part by Shriners Hospitals under Grant 8530. Asterisk indicates corresponding author. *R. T. Lauer is with the Research Department, Shriners Hospitals for Children, 3551 North Broad Street, Philadelphia, PA 19140 USA (e-mail: [email protected]). B. T. Smith and R. R. Betz are with the Research Department, Shriners Hospitals for Children, Philadelphia, PA 19140 USA. Digital Object Identifier 10.1109/TBME.2005.851527
PY - 2005/9
Y1 - 2005/9
N2 - An adaptive neuro-fuzzy inference system (ANFIS) with a supervisory control system (SCS) was used to predict the occurrence of gait events using the electromyographic (EMG) activity of lower extremity muscles in the child with cerebral palsy (CP). This is anticipated to form the basis of a control algorithm for the application of electrical stimulation (ES) to leg or ankle muscles in an attempt to improve walking ability. Either surface or percutaneous intramuscular electrodes were used to record the muscle activity from the quadriceps muscles, with concurrent recording of the gait cycle performed using a VICON motion analysis system for validation of the ANFIS with SCS. Using one EMG signal and its derivative from each leg as its inputs, the ANFIS with SCS was able to predict all gait events in seven out of the eight children, with an average absolute time differential between the VICON recording and the ANFIS prediction of less than 30 ms. Overall accuracy in predicting gait events ranged from 98.6% to 95.3% (root mean-squared error between 0.7 and 1.5). Application of the ANFIS with the SCS to the prediction of gait events using EMG data collected two months after the initial data demonstrated comparable results, with no significant differences between gait event detection times. The accuracy rate and robustness of the ANFIS with SCS with two EMG signals suggests its applicability to ES control.
AB - An adaptive neuro-fuzzy inference system (ANFIS) with a supervisory control system (SCS) was used to predict the occurrence of gait events using the electromyographic (EMG) activity of lower extremity muscles in the child with cerebral palsy (CP). This is anticipated to form the basis of a control algorithm for the application of electrical stimulation (ES) to leg or ankle muscles in an attempt to improve walking ability. Either surface or percutaneous intramuscular electrodes were used to record the muscle activity from the quadriceps muscles, with concurrent recording of the gait cycle performed using a VICON motion analysis system for validation of the ANFIS with SCS. Using one EMG signal and its derivative from each leg as its inputs, the ANFIS with SCS was able to predict all gait events in seven out of the eight children, with an average absolute time differential between the VICON recording and the ANFIS prediction of less than 30 ms. Overall accuracy in predicting gait events ranged from 98.6% to 95.3% (root mean-squared error between 0.7 and 1.5). Application of the ANFIS with the SCS to the prediction of gait events using EMG data collected two months after the initial data demonstrated comparable results, with no significant differences between gait event detection times. The accuracy rate and robustness of the ANFIS with SCS with two EMG signals suggests its applicability to ES control.
KW - Adaptive neuro-fuzzy inference system (ANFIS)
KW - Cerebral palsy (CP)
KW - Electrical stimulation (ES)
UR - https://www.scopus.com/pages/publications/26844512219
U2 - 10.1109/TBME.2005.851527
DO - 10.1109/TBME.2005.851527
M3 - Article
C2 - 16189966
AN - SCOPUS:26844512219
SN - 0018-9294
VL - 52
SP - 1532
EP - 1540
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 9
ER -