The present study investigated the role of visual feedback in upright posture control in the sagittal plane. In posture control, each sensory system has the following roles: initial detection of sway, suppression of short-term sway around the equilibrium, and suppression of long-term sway induced by slow shift of the equilibrium. Experiments were performed to examine characteristics of each sensor and then visual contribution was studied. On the basis of measured sensory thresholds for the perception of sway during standing, it was suggested that visual inputs provided sensitive means of perceiving postural sway. Under several conditions in which a subject controlled his upright posture utilizing the definite number of sensors, subject's body sway was measured. By analysis and comparison of measured sway waveforms under each condition, it was clear that the visual system suppressed short-term sway. Spectral analysis provided the result that the visual system suppressed body sway in the low frequency range around 0.2Hz. Though the visual feedback system contains a large time delay, influences of the delay are small in the low frequency range. It is rational that the visual system is efficient to suppress body sway in the low frequency range.

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We have been developing a new diagnostic method for Alzheimer's disease utilizing miotic reflex to light. In our previous studies, we found that a miotic ratio and miotic time were significantly smaller and longer, respectively, in Alzheimer's disease and cerebrovascular dementia patients than those in normal aged persons when pupils were exposed to visible light. In this study, we improved our measurement system so that we could measure miotic reflex quantitatively and in real time. We developed a glasses-type measurement device that consisted of visible and infrared light emitting diodes and an infrared CCD camera. In this system, a flashlight was for light stimulation and miotic reflex curves were obtained automatically and in real time. We also examined the usefulness of several parameters for diagnosis and found that delay time, miotic ratio, recovery time and recovery velocity of patients significantly differed from those of normal subjects, suggesting that these parameters might be effective for the diagnosis.

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Currently, the images of brain and minute biotissue of the body are obtained by MRI, and it is used for the diagnosis of cancer and injury. Moreover, for diagnosis of diseases of brain and research for brain physiology, it gives us important evidence. So far, the extraction of brain region in MR image has been processed by medical doctors manually. It requires a large amount of labor. Then several algorithms were developed for extracting brain region. Region growing, snake and density histogram are popular algorithms. It was, however, very difficult to extract brain region automatically by these algorithms. Region growing easily induces leakage from the brain region. A snake is not appropriate to apply when detecting an objective region that is surrounded by other objects like brain and scalp. The density histogram is heavily affected by the distribution of density of each MR image, which is changed by MR protocols. In this study, we developed an algorithm for automatic extraction of the brain region from head images acquired from an MRI scanner. This algorithm uses a snake and multi-seed region growing. In this algorithm, the scalp region was deleted before extracting the brain region. Moreover, the leak from brain is checked at the points of scalp detected by the snake. By this method, leakage of the region growing from the brain region was well suppressed. In our research, it was revealed that the brain region was extracted more easily and accurately by using our proposed method compared with conventional methods.

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We developed percutaneous intramuscular electrodes for controlling paralyzed extremities by FES and also applied them to TES. In this study, the mechanical and electrical properties of stainless steel SUS 316L, Co-Cr based alloy NAS 604 PH and newly developed high nitrogen high manganese stainless steel NAS 106N were investigated. Furthermore, we performed a clinical evaluation of the newly developed estimation method of the implanted electrodes using impedance characteristics which we proposed in previous papers. NAS 106N showed superior mechanical properties at rotating-bending fatigue test both in air and in 0.9% NaCl solution. Noise spectrum analysis proved that all three electrodes had low noise properties, i. e., no excessive noise. Therefore, those stainless steel electrodes are useful not only for stimulation but also for detection of small signals from the nerve-muscular system. In the clinical verification of the electrodes after implantation, the failure of terminals had occurred when the electrode impedance was inversely proportional to the measurement frequency. It was also confirmed with X-ray photographs that a complete breakage in the body corresponded to estimation by the impedance characteristic. Furthermore, we performed an cross-sectional investigation of 730 electrodes (SUS 316L) implanted for 0-to 72-month periods with the proposed method in 30 patients. 620 electrodes (85%) continued to produce stable muscle contractions. In the 110 electrodes that didn't work well, 96 (13.1%) and 14 (1.9%) electrodes were estimated to have suffered complete breakage, and failure of the terminal, respectively. From these results, it has been shown that the evaluation method proposed by the authors was available for clinical usage.

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An awake electroencephalographic (EEG) record, which consists of background EEG activity and paroxysmal abnormality such as spikes, gives significant information for clinical diagnosis. The interpretation of spikes is the most important item in the diagnosis of subjects with epileptic disorders. A method for automatic interpretation of awake background EEG was already been proposed by the authors. However, the existence or interpretation of the spikes was not considered in the previous method. In order to make the automatic EEG interpretation system a more effective tool on the clinical scene, a method of creating EEG spike reports based on results of an adaptive spike detection method developed in the past, was proposed and investigated. Parameters and rules for making report of EEG spikes were constructed by taking into account the method of visual inspection used by an electroencephalographer. Then, the proposed method was integrated with the method for automatic interpretation of awake background EEG. The proposed method was applied to the data acquired from 3 subjects, and satisfactory results were obtained as compared with those of visual inspection. The integrated system has achieved a total interpretation of awake EEG of both backgorund EEG and spikes in paroxysmal abnormality. The method will be a powerful tool for various clinical applications.

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We analyzed the physiological effect of sound whose pitch and tempo were changed in real time by instantaneous heart rate. Six algorithms were arranged to obtain differences in pitch and tempo. In the experiment, subjects performed a double-digit calculation task while watching a monitor display. They performed this task for 3 minutes while listening to sound arranged by an algorithm. The task was performed six times according to the respective six algorithms with a sufficient break between them. The 22 subjects (17 male, 5 female) wore a portable ECG monitor to measure their heart rate, etc., during task performance. At the end of each task the subjective evaluation criteria were measured: interference with task by sound, tension, pressure under time constraints, and fatigue. The physiological parameters were mean heart rate, standard deviation of instantaneous heart rate, coefficient of variance of R-R intervals, and HF. The correct reply rate and number of answers during the task were recorded for task evaluation. From one-way analysis of variance with all subjects, there was no significant difference among the algorithms (5% significance level). Subjects were then classified into three groups by the hierarchical clustering method. From one-way analysis and multiple comparison analysis of each group, a significant difference was found among the algorithms (5% significance). In the first group, the sound did not affect task performance. In the second, only the HF of heart rate variability varied with the sound. In the third group, the self-reported information, the heart rate, and the task performance varied. Thus, based on the foregoing results, the possibility that cardiac biofeedback affects a heart rate time series or task was confirmed.

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