title: Monitoring accumulative fatigue of finger by autoregressive modeling of physiological tremor.
abstract: In this study, block data structured autoregressive (AR) method is used to evaluate fatigue, based on physiological tremor during and after loading a weight mass on the index finger. The temporal changes in the prediction coefficients and the reflection coefficients are determined. AR spectral estimation with the ninth-order is obtained and presented in graphical form. The results indicate that the first prediction coefficient a1 can be used to characterize the state of fatigue of finger muscle and the other prediction coefficients do not show any tendency for the finger load. The coefficient a1 can be applied to monitor the accumulative fatigue induced by the weight loading for a duration of time.
authors.affiliation: Department of Communications and Systems, University of Electro-Communications, Tokyo, Japan.
authors.forename: T
authors.initials: T
authors.lastname: Miao
authors.forename: K
authors.initials: K
authors.lastname: Sakamoto
mesh_terms: D000328:Adult; D003198:Computer Simulation; D005221:Fatigue / Q000503:physiopathology*; D005385:Fingers / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000502:physiology; D011237:Predictive Value of Tests; D014202:Tremor / Q000503:physiopathology*; D016474:Weight-Bearing / Q000502:physiology