The effects of food consistency on jaw movement and posterior temporalis and inferior orbicularis oris muscle activities during chewing in children.

Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs)

Rate of change in movement acceleration (jerk) is a universally accepted quantity to evaluate irregularities of human limb and eye movements. This study was aimed to attest reliability of the jerk measurement of masticatory jaw movements and to identify its sensitivity to discriminate between movements performed with the presence of varied occlusal interference. Jaw movements during gum chewing were recorded with a 3D tracking device. Twelve adult subjects participated in the experiments. For five subjects, normalized jerk-costs (NJC) during jaw closing were compared between those measured on two separate occasions. For seven subjects, the NJCs during closing were compared with those measured with/without introduction of four different types of occlusal interference. The NJCs did not differ significantly between the two recording occasions. The interference at the canine tooth induced greater increase in the NJCs than that at the molar tooth. The comparison between repeated measures revealed reproducibility of the NJCs. In addition, the NJC was shown to be capable of discriminating between irregularities of the movements induced by occlusal interference at the canine and molar tooth sites. These findings suggest that the NJC is a valid indicator of masticatory jaw movement irregularity induced by disturbances of full intercuspation between upper and lower teeth.

https://iopscience.iop.org/article/10.1088/0967-3334/27/7/005/pdf

Reliability and sensitivity of jerk-cost measurement for evaluating irregularity of chewing jaw movements.

Human limb movements are successfully modeled based on the assumption that the central nervous system controls the movements by maximizing movement smoothness. Movement smoothness is quantified by means of a time integral of squared jerk (jerk-cost), where jerk is defined as the rate of change in acceleration. This study was performed to investigate whether the control of human masticatory vertical jaw movements can also be explained by a minimum-jerk (maximum-smoothness) model. Based on the assumption that minimum-jerk models account for vertical jaw-opening and -closing movements during chewing, the actual time profile of the movement trajectory was simulated by the model. The simulated jerk-costs and peak velocities were compared with those obtained by actual measurements of jaw movements during chewing. Jerk-costs and peak velocities of the jaw movements during chewing were significantly correlated with those predicted by minimum-jerk models (P < 0.0001, r between 0.596 and 0.799). The minimum-jerk models predicted closing movement trajectories more accurately than opening movement trajectories (jaw opening, root-mean-square error = 1.19 mm; jaw closing, 0.52 mm, t = 4.375, P < 0.0001). The results indicated that the vertical jaw movement control during chewing was represented by the minimum-jerk control model and that the vertical jaw-closing movement is smoother than the opening movement during gum-chewing.

Smoothness of human jaw movement during chewing.

Changes in jaw movement and jaw closing muscle activity after orthodontic correction of incisor crossbite

An improved nonlinear interpolation method for estimating distorted kinesiographic recording of interlattice points in space is proposed, and its correction accuracy is evaluated. The group method of data handling (GMDH) correction method reported previously by the authors (1984, 1986) can be adapted to a measurement environment in which kinesiographic signals are subject to ferromagnetic interferences, but it was restricted to kinesiographic signals representing lattice points in space. The sensitivity method for GMDH correction modeling is explored. This provides for nonlinear interpolation of kinesiographic signals for lattice points. Nominals, i.e., true values, of 3-D coordinates of the interlattice points were determined by means of a mandibular jaw movement simulator together with simultaneous recording of distorted kinesiographic signals which correspond to the nominals. Distorted signals were corrected by the new method of correction modeling. A mean estimation error of 0.16 mm (SD 0.19 mm) was determined for 24 interlattice coordinates. Thus, nonlinear interpolation by the sensitivity method is confirmed to be effective. >

Nonlinear interpolation of mandibular kinesiographic signals by applying sensitivity method to a GMDH correction model

Image processing for boundary extraction of remotely sensed data

Prediction of River Flows by Multiplicative Model

A new correction method is investigated for distorted measurement signals recorded by a mandibular kinesiograph, an electromagnetic device to quantify jaw movement by a magnetic transducer fixed on the mandible. The method employs a self-organized system identification known as a group method of data handling (GMDH). The proposed GMDH correction method has the capability to design a two-dimensional nonlinear autoregressive model which represents the relationship between distorted measurements and their corrected estimates of the mandibular displacement for the initial adjustment of kinesiographic signals under ferromagnetic influences. A computational procedure of a GMDH correction method is described in which the mathematical structure of a nonlinear autoregressive model is identified in a self-organized manner. Heuristic determination of a restricted sensor array zone of a kinesiograph is proposed to obtain effective fitness of a particular GMDH correction model which corresponds to each of the concomitant restricted zones. Corrected signals evaluated in the present study reveal a mean estimation error of 0.101 mm with a standard deviation of 0.125 mm which is regarded as highly accurate in a clinical sense.

GMDH Correction Modeling of Distorted Signals Recorded by Mandibular Kinesiograph

There are many occasions when approximate design of the Kalman filter is inevitable in the identification. This paper proposes the algorithms for two kinds of suboptimal Kalman filters based on the error analysis of the filter. It is rare that each noise added to the dynamical equation, or the measurement equation may be regarded as sequentially correlated in the practical situation. The suboptimal Kalman filter is constructed in order to replace the sequentially correlated measurement noise by the appropriate white measurement noise. The concept of the design for the proposed filter is delineated. The covariance of the estimation error due to the replacement of the measurement noise, called the actual covariance, is derived in the form of the differential equation. This is the known result of the error analysis of the Kalman filter. Secondly, the stationary condition is assumed on the actual covariance in place of the usual optimal covariance of the estimation error. One suboptimal Kalman filter with the...

Motoyasu Nagata

Papers

Nonlinear interpolation of mandibular kinesiographic signals by applying sensitivity method to a GMDH correction model

Image processing for boundary extraction of remotely sensed data

Prediction of River Flows by Multiplicative Model

GMDH Correction Modeling of Distorted Signals Recorded by Mandibular Kinesiograph

A suboptimal Kalman filter considering error analysis