Fatigue estimation with a multivariable myoelectric mapping function.
A novel approach to muscle fatigue assessment is proposed. A function is used to map multiple myoelectric parameters representing segments of myoelectric data to a fatigue estimate for that segment. An artificial neural network is used to tune the mapping function and time-domain features are used as inputs. Two fatigue tests were conducted on five participants in each of static, cyclic and random conditions. The function was tuned with one data set and tested on the other. Performance was evaluated based on a signal to noise metric which compared variability due to fatigue factors with variability due to nonfatiguing factors. Signal to noise ratios for the mapping function ranged from 7.89 under random conditions to 9.69 under static conditions compared to 3.34-6.74 for mean frequency and 2.12-2.63 for instantaneous mean frequency indicating that the mapping function tracks the myoelectric manifestations of fatigue better than either mean frequency or instantaneous mean frequency under all three contraction conditions.
MacIsaac
Dawn T
DT
Electrical and Computer Engineering Department and the Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 5A3, Canada. dmac@unb.ca
Parker
Philip A
PA
Englehart
Kevin B
KB
Rogers
Daniel R
DR
D000200:Action Potentials / Q000502:physiology; D000328:Adult; D000368:Aged; D000465:Algorithms; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D009119:Muscle Contraction / Q000502:physiology*; D018763:Muscle Fatigue / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D009415:Nerve Net; D010363:Pattern Recognition, Automated / Q000379:methods