Effects of weight load on physiological tremor: the AR representation

The propriety of the autoregressive (AR) method as a means of processing the signals of physiological tremor of human finger (finger tremor) was investigated. Application of the Akaike's criterion demonstrated that the 15-th order AR model was required to describe the recordings of finger tremor. According to Burg's algorithm, both AR spectrum and AR parameters were estimated to study the effects of various weight loads on finger tremor. It was found that, (1) the amplitude of AR spectrum was apparently enhanced by adding the load; (2) the first prediction coefficient (a1) and the first reflection coefficient (rho 1) significantly declined by increasing the weight loads. The results were compared with the calculations from FFT (Fast Fourier Transform) and autocorrelation function. Simple physical interpretation of the AR parameters (i.e. a1 and rho 1) was discussed in relation with system's resonant modes.

Physiological tremor under pseudo-fraction gravity

The effect of pseudo-fraction gravity on physiological tremor of the human finger (finger tremor) has been examined experimentally by immersing an index finger into water at different immersion levels. The pseudo-fraction gravity, gamma G, was established by water buoyancy at immersion level omega, G being gravitation acceleration and gamma between zero and unit. The nature of variations of finger tremor under the influence of gamma G is estimated based on FFT spectral analysis. It is illustrated that with a decrease in gamma, or equivalently an increase in omega, two dominant peaks remaining approximately constant in frequencies around 10Hz and 20Hz are found, while peak amplitude is decreased rapidly for higher peak and slowly for lower one. Theoretically the effect of pseudo-fraction gravity is analyzed in terms of a specific model for finger tremor. The experimental results presented in this paper are predicted rather well by two resonant modes which occurred in our model system. It is possible to conclude that the model, which is characterized by a pair of antagonistic muscles and two reflex pathways, provides an adequate quantitative description of finger tremor.

Monitoring accumulative fatigue of finger by autoregressive modeling of physiological tremor

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.