Miao T, Sakamoto K
Department of Communications and Systems, University of Electro-Communications, Tokyo, Japan.
Appl Human Sci. 1995 Jan;14(1):29-36. doi: 10.2114/ahs.14.29.
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.
在本研究中，基于在食指加载重物期间及之后的生理震颤，采用块数据结构自回归（AR）方法来评估疲劳。确定了预测系数和反射系数的时间变化。获得了九阶的AR谱估计并以图形形式呈现。结果表明，第一预测系数a1可用于表征手指肌肉的疲劳状态，而其他预测系数在手指加载时未显示出任何趋势。系数a1可用于监测一段时间内由重量加载引起的累积疲劳。
