The purpose of this study is to evaluate the influence of the whole body vibration to the infants on a cradle. Fifty-four normal healthy infants were exposed to horizontal whole body vibration in the Z-axis for 5 minutes. A sine wave vibration with the frequency of 1Hz, amplitude of 55mm and acceleration level of 1.0m/s² was used for stimulus to infants.
The results are summarized as follows: (1) The infants stopped crying rapidly by the vibration exposure. (2) During vibration exposure, heart rate, peripheral body temperature and CV_R-R decreased, and respiration rate on infants increased in crying infants. (3) Peripheral body temperature and CV_R-R decreased, and respiration rate increased in drawzing or sleeping infants. (4) The vibration not only showed the sedative effect on crying infants, but also became a strong stimulus to awake on sleeping infants.
It is supposed that the intensity of vibration stimuli used in this study was strong enough to stop the infants crying but is too strong for the drawzing or sleeping infants because it induce them arousal.

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A method based on an octave-band analysis is provided to perform real-time analysis of Heart Rate Variability (HRV) on a personal computer with high temporal resolution. This method requires a hardware and a software besides the computer. The hardware (R-wave detector) generates pulses synchronized with R-waves from an Electro Cardiogram (ECG) signal. And the software calculates spectral power within each of passbands by using four sets of digital band-pass filters. As outputs of the analysis, normalized spectral powers for these passbands and an HRV index are obtained. The HRV index is calculated as a weighted sum of the normalized spectral powers. With some modifications, this method is also applicable to a Holter EGG signal recorded on a tape.
This method was applied to EGG signals obtained through two VDT experiments employing a location task and a manual control task. In this paper, the method is explained in detail. Moreover, based on results obtained through those applications, aptness of the method is also discussed as to evaluate mental strain.

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Kansei engineering is a technology for translating human feelings into product design. Several multivariate analyses are conventional techniques for analyzing human feelings and building translation rules. Although these methods are reliable, they are nevertheless time and resource consuming, and require statistical expertise for users. In this paper, we propose an automatic semantic structure analyzer and Kansei expert systems builder using self-organizing neural networks, ART1.5-SSS and PCAnet. ART1.5-SSS is our modified version of ART1.5, a variant of the Adaptive Resonance Theory neural network. Improvement on learning rule makes ART1.5-SSS a stable non-hierarchical cluster analyzer and feature extractor, even in a small sample size condition. PCAnet performs principal component analysis. The networks enables quick and automatic rule building in Kansei engineering expert systems.

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A camera, while it is being used, is constantly subjectd to vibration by its users. That vibration, the subject of this paper, was studied in two experiments. One was the measurement of the bias size of a target in a picture. Three types of cameras were used in this experiment. The pictures taken by subjects had a target image. They were measured for bias by normal-photography and by timer-photography. The other was an analysis of oscillation sampled from the cameras. Two types of cameras were used in this experiment. Each of the cameras had three dimensional accelerometers on top of it and a manometer on its shutter button. When a subject pushes the manometer to take a picture, the oscillations were determined by the accelerometers.
From these experiments, two causes of camera vibration may be distinguished, as follows: One is camera vibration caused by microvibration on the surface of the subject's hands and physiological tremor on forearm. This oscillation is about 4.0Hz. The other is caused by the subject's pushing action against the shutter button. This oscillation is about 2.0Hz. This kind of vibration occurs because the user's pressure on the shutter button is too strong to keep the camera fixed. The results show that most of the vibration of cameras is caused by manipulation against the camera.

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This paper considers a method of computing the predicted value of the controlled variable on the manual preview-predictive control systems. The proposed method is operated as follows. First, the human model that expresses the control characteristics of the human operator in the present time is identified. Second, the future reference signal is fed to the man-machine model that is composed of the human model and the simulator of the controlled system, connected in series. Such above processes are operated in a sampling period, and the output of man-machine model assumes a predicted locus of the controlled variable. The usefulness of the proposed method is investigated from the difference in the controlled performance between the proposed method and the conventional method. In this conventional method, the prediction of the controlled variable is computing, based on an assumption that the operated variable in the present time is constant. As the results, we conclude that the proposed method is efficient in case of difficult dynamic-characteristics for the human operator to control.

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