To examine if the workload during motorcycle riding is estimated by heart rate variability, we analyzed the multivariate relationships between subjective feeling and indices of R-R interval variability in 14 healthy men during 400-km highway motorcycle driving. Logistic regression analysis revealed that the subjective fatigue assessed by questionnaire was estimated by a two-variate model including mean R-R interval (RRM) and the power of highfrequency component (HFP). By analyzed the pattern of the change in RRM and HFP, the fatigue occurred in the following three conditions: 1) increases in both RRM and HFP, 2) decreased RRM with increased HFP, and 3) decreases in both RRM and HFP, which seemed to correspond to the situations of 1) decreased arousal level, 2) difficulty in the concentration to drive despite high arousal level, and 3) maintaining concentration ability under distress, respectively. These results indicate that the subjective workload during motorcycle riding is estimated from autonomic indices of heart rate variability. The regression model obtained in this study seems useful for the quantitative assessment of the effects of difference in machine and in driving condition on the workload during motorcycle riding.

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Two-dimensional deformation of human left ventricles (Subjects were eight normal volunteers and a patient with hypertrophic cardiomyopathy) during the ejection period was investigated using MRI with the tagging technique. The regional displacements and strains in the myocardial wall were analyzed from the motions of intersections of tagged stripes formed in the short-axis images. The results for normal volunteers showed that the magnitudes of displacement differed among various regions (anterior, septal, posterior and lateral) of the left ventricle, but the magnitudes and the directions of the minimum principal strain were almost uniform over all regions. The magnitude of the minimum principal strain in the anterior wall of the patient with hypertrophic cardiomyopathy clearly decreased compared with that of normal volunteers. The study strongly suggested that the minimum principle strain could be a new effective index to evaluate cardiac contractility.

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We designed the metal coating hollow fiber membrane as a new bactericidal material that may provide a novel simple water purification system. This membrane was coated with metal (Au) on the outer surface of the membrane. The membrane was able to remove bacteria from the bacteria suspension and bacteria trapped into the membrane could be killed by passing a DC 100mA through the membrane surface. To determine whether this bactericidal mechanism was mediated by the destruction of the cell wall on passing an electric current, intracellular components were measured in the aspirated solution. The destruction of the cell wall was confirmed because both intracellular components of nucleic acid and protein were clearly detected by passing an electric current. In general, bacteria are good electrical conductors to have negative charges on the surface of the cell wall. About ten percent of the electric resistance of the metal coating hollow fiber membrane decrease after bacteria were caught on the membrane surface. This decrement means the pass of an electric current through the cell body. On bacteria death, we speculate that the destruction of the cell wall occurs since passing an electric current through the cell body directly leads to the difficulty of the membrane resealing.

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In clinical application of FES, the method, in which the standard stimulation data are made based on the EMG of normal subjects and adjusted to each patient, has been used. However, this method was burdensome to normal subjects, patients, and medical staff. The final goal of this study is to develop a practical musculoskeletal model which is easily applied in clinic and to automatically generate the stimulation data for a specific patient with this model. The purpose of this paper is, before the final goal, to show that the stimulation data generation method using the model simplified for clinical application is useful as an alternative of the EMG based method. We set the paraplegics' standing-up motion unassisted by the upper extremities as the first example problem, because standing-up motion is thought to be the starting point of motor function restoration of paraplegics. Standing-up motion pertains to the movement of each segment in the sagittal plane, so that 3-link model in the sagittal plane was selected as a skeletal model. Previous musculotendon model for FES of other groups often ignored the difficulty of identifying the model parameters, which made the model impractical in clinical application. Therefore, we made the musculotendon model easier to be clinically applied by simplifying it to be described only with the parameters available from noninvasive measurements. Then we generated the stimulation data for standing up without arm-assistance by dynamic optimization method using the model simulation. The resulted stimulation data and estimated motion were compared with the EMG patterns and motions of normal subjects' standing up, respectively. The joint torque trajectories developed by model simulation were similar to those of a normal subject calculated by Bajd, et. al, the generated stimulation data were roughly in agreement with the normal subjects' EMG, and the cost function is normally minimized in dynamic optimization process and properly incorporated in the stimulation data. From these, we may conclude that the developed model is proper in principle, and that the method of this paper is useful as one for generating standard stimulation data. The developed system is expected to become more practical through refinement of the cost function and constraints of optimization and through improvement and extension of the model considering clinical applicability.

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Many studies have been reported to develop monitoring systems for elderly and high risk patients. We are developing a new monitoring system which monitor the physiological bio-signals during their daily life. When the system catches an onset of symptoms, it will send an ambulatory report to a hospital via a telephone line. To develop this new monitoring system, physiological bio-signals processing is used to detect the symptoms. In this paper, electrocardiogram (ECG) processing, especially QRS-complex detection for the system, is reported. QRS-complex detection is a fight against noise and artifact. In our system, which is used in daily life activity, ECGs contain much more noises than ECGs measured in hospital's examinations. QRS-complex detection in the system must be robust and failure proof. In addition, processing speed must be fast enough for real time operation. So we applied “morphological QRS-complex detection” for the system. Morphological QRS-complex detection consists of two operations called opening and closing, as reported by P. E. Trahanias. Through the present study using a typical waveform including one real QRS-complex and two false QRS-complex, we found the optimal shape of our structuring element is 24ms horizontal line, where P. E. Trahanias reported 26ms through other method of experiment. Processing speed of the morphological operation was also measured in this paper. The speed was found to be 2.5×10^-5(s/datum). It was considerd to be fast enough for real time processing. The morphological operation erased the 50Hz power line noise, P-waves, T-waves, false QRS-complex and base-line drift. The morphological operation detected QRS-complex, but the ECG differential method yet not find them. The morphological QRS complex detection was proved to be suitable for our new monitoring system, because it could extract QRS-complex under a mixture of ECGs and noises.

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Near-infrared spectroscopy (NIRS) has been found to be a useful technique for noninvasive monitoring of tissue oxygenation. However, only the relative change in oxygenation from an initial level can be practically measured by conventional reflectance NIRS. Moreover, the degree of change can not be compared among subjects because the inhomogeneity of tissue structure greatly affects the sensitivity of measurement. In the present study, the influence of a fat layer on the sensitivity of muscle oxygenation measurement was systematically examined, and a method for correcting the sensitivity difference due to various fat layer thicknesses was proposed. To verify the influence of a fat layer on muscle oxygenation measurement, exercise tests employing a bicycle ergometer were carried out on 12 subjects. A newly developed multisensor-probe consisting of a two-wavelength LED and three photodiodes was used for the measurement. The probe was placed on the vastus lateralis. Peak-to-peak variations in muscle oxygenation in response to intermittent exercise were compared with fat layer thicknesses, which were measured by ultrasonography. The presence of a fat layer remarkably decreased the measurement sensitivity and increased the light intensity at the detector. This tendency was also confirmed by Monte Carlo simulation which modeled photon migration in two-layered tissues consisting of fat and muscle layers. Based on the inverse relationship between sensitivity and detected light intensity, oxygenation levels were successfully corrected, enabling us to compare oxygenation measurements among subjects despite different fat layer thicknesses. The results also showed that this method can be used to correct sensitivity variation due to different source-detector distances.

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The exact position of coronary artery (CA) stenosis cannot be determined from SPECT images and diagnostic procedures require high level of experience from the physician. In order to assist the physician's understanding of such images, we propose a new system, which can display clearly the relationships between the CA and the cardiac wall. We achieve this result by superimposing the 3-dimensional coronary artery model on the observed left ventricle (LV) shape. However, there are a number of problems, when superimposing a 3D CA model on the 3D LV shape. The actual shape of the heart is notably dependant on the subject and it is different from anatomical model shapes, especially in the ischemic heart. That is why we have developed an algorithm to estimate the ischemic wall and to superimpose the CA model on the LV shape. We use ellipsoid models, which fit on the CA model and on the observed heart wall respectively. To evaluate this algorithm, we introduce a measure for the average distance between the two models, and apply actual clinical data. The obtained results from these experiments could be considered promising.

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