Diapers are used by elderly people who require nursing and have difficulty in urinating in the toilet. Some sensors have been developed that detect urination in a diaper. However, most of them cannot evaluate the urine absorption volume. Moreover, if elderly people urinate more than once before a diaper change, the second and subsequent urinations cannot be detected by the developed sensors. Therefore, a capacitive sensor system that can quantitatively evaluate the urine absorption volume multiple times by attaching the sensor to the outer surface of a diaper is developed. In this study, a phantom, i.e., a waist-type torso mannequin, filled with saline solution was used. When pseudo-urine was infused into the diaper, the capacitance rapidly decreased. Afterward, as the absorbed pseudo-urine spread on the diaper, the capacitance drifted and decreased. Furthermore, when the same amount of pseudo-urine was additionally infused into the diaper, the rapid decrease in capacitance became larger. To compensate for the decrease in capacitance, the time from the first urination was employed as a variable. As a result, the compensated values of capacitance change increased linearly with the amount of urine absorption volume in multiple urinations before changing a diaper. An elderly female who required nursing and used diapers participated in this study. In the feasibility test, multiple urinations were quantitatively evaluated.
Mild cognitive impairment (MCI) is a precursor stage to dementia. It is necessary to understand the appropriate condition of MCI to prevent the progression of dementia. For MCI diagnosis, there is a study report on memory feature measured by a device, in addition to paper-based assessments. Motor sequence learning memory, a trait of memory feature could be used as an indicator to distinguish MCI. The purpose of this study was to develop a new method to distinguish MCI from an unimpaired condition. The data obtained from proposed method are intended to be applied to a decision tree approach to establish a classification algorithm by clarifying the variables required for classification. Sixty-seven subjects were examined in the study:a group of healthy young people, a group of healthy elderly people, and a group of people with MCI. The motor sequence learning memory was quantitatively evaluated with a visual tracking exercise using hand dexterity movement with the help of a grasping force control training device (iWakka). Sine waves were combined to create a synthetic wave for evaluation tasks. For the evaluation parameters, the difference between the target value and atonal value was used, as well as assignment accuracy and its distributed standard deviation. MCI was differentiated based on these variables. The result showed that the value obtained from iWakka was significantly lower in the elderly people with MCI compared with the other groups. Furthermore, the classification algorithm was created using a decision tree classification. The evaluation variables adopted were mAGF8 and the learning rate. In addition, the decision tree classifier was validated in terms of the area under the curve (0.79), sensitivity (0.66), specificity (0.92). From these results, it can be concluded that the proposed method with iWakka is highly likely to provide useful information when classifying MCI.
A creative engineer tends to understand the principles and phenomena not only based on mathematical physics but also by intuition. Author has picked up several topics among such principles and phenomena in the area of medical ultrasound and challenges to explain them intuitively in this article. The chosen topics are the lateral resolution, Doppler blood flow measurement, a piezoelectric element, ultrasonic absorption by biological tissues, and intensive/extensive variables. Author hopes that the presented intuitive explanation will help the readers to enhance their creativity as an engineer.