Observation of daily human activity and status is important from the viewpoints of maintaining health and preventive medical care. In this study, we describe a system for monitoring human activities and conditions that uses microelectromechanical systems (MEMS) sensors. The system contains four MEMS sensors for environmental monitoring-3-axis acceleration, barometric pressure, temperature, and relative humidity -as well as the peripheral circuitry for each sensor. Measured human activity data are stored in a memory via an on-board microprocessor. We measured environmental data for a subject's daily life. To estimate the subject's activity and his condition from a huge volume of data, we applied a soft computing technique to machine learning for the automatic extraction of human-activity classification.
Measurement of cortical thickness using human brain magnetic resonance (MR) imaging can assist physicians in quantifying cerebral atrophy. Most of the conventional measurement methods assign the same class to all pixels with a similar MR signal independent of their locations, and are therefore unsuitable for MR images that have strong intensity nonuniformity (INU) artifact. We propose an automated method that locally segments the cerebral cortex using an adapted fuzzy spatial model representing the transit of MR signals from the cerebral cortex to the white matter. This method assigns fuzzy degrees belonging to brain tissues using the adaptive fuzzy spatial model for local intensity transition from the cerebral cortex to inside the cerebrum. We also introduce an evaluation method of cortex segmentation algorithms that consists of reproducibility, quantitative, and qualitative tests; we use this method to evaluate and discuss the proposed segmentation method in comparison with the conventional method.
In this paper, a scheme of recognizing hematopoietic tumor patients is presented, using self-organizing maps constructed by fast block-matching-based learning. This fast learning is referred to as T-BMSOM leaning. To classify the patients, screening data of examinees are presented to a constructed map. In T-BMSOM learning, a set of neurons arranged in square is regarded as a block, and one of the blocks is chosen as a winner per the presented data. It is assumed that members of a training data set to construct the map never change in static environments, whereas the data set is suddenly updated during learning in dynamic environments. While adopting the concept of blocks makes it possible to construct well-organized maps in dynamic environments, it lengthens the time for learning. To overcome this issue, T-BMSOM learning is based on a decision-tree-like winner search and a batch process. The screening data of an examinee frequently lacks several of the item values, and hence the data is presented to the map after averages of non-missing item values substitute for items with no values. The class of the data to be classified is basically judged by observing the label of a winner block. Simulation results establish that the proposed scheme achieves high accuracy of correctly recognizing the data of hematopoietic tumor patients, even if training the map is conducted in a dynamic environment.
Diagnostic imaging system is a necessity for brain diagnosis. Transcranial Ultrasonography can noninvasively image the intracranial blood flow and brain tissue in real time from only temple area of human head. However, the ultrasonic wave causes attenuation, decentration, and refraction in the skull, so the ultrasonography can not provide the transcranial brain surface image from arbitrary place. In this paper, we propose an imaging system of brain surface and skull from arbitrary places by considering the ultrasonic refraction of the skull. We do an experiment by using a cow scapula to imitate the skull bone and a biological phantom to imitate the cerebral sulcus. We first visualize the shape of scapula, and grasp the shape of scapula surface. We second remove the delay and the multi echoes of refracted wave. We third calculate the thickness of the scapula by using fuzzy inference. In the inference, we employ amplitude, correlation coefficient and the elapsed time. Finally, we calculate the refractive angle of ultrasonic wave and visualize the image referring to the refraction of ultrasonic wave. In the result of applying our method, we can estimate the thickness of scapula at all points, and successfully visualize the phantom surface image.