In this paper, we describe a new ultrasonic oscillosensor and its application in a biological information measurement system. This ultrasonic sensor has a cylindrical tank of 26 mm (diameter)20 mm (height) filled with water and an ultrasonic probe. It detects the vibration of the target object by obtaining echo signals reflected from the water surface. This sensor can noninvasively detect the vibration of a patient by placing it under a bed frame. We propose a recognition system for humans in bed. Using this sensor, we could determine whether or not a patient is in the bed. Moreover, we propose a heart rate monitoring system using this sensor. When our system was tested on four volunteers, we successfully detected a heart rate comparable to that in the case of using an electrocardiograph. Fuzzy logic plays a primary role in the recognition. Consequently, this system can noninvasively determine whether a patient is in the bed as well as their heart rate using a constraint-free and compact device.

This paper proposes an automated 3D visualization method of embedded tubes applicable to the scanned result of pulse-radar Non-Destructive Testing (NDT). The proposed method consists of three stages. First, our method defines the processing region which includes a pattern generated by a tube. This region is determined by referring to the composition of a received wave. Second, after expert knowledge of a tube is translated into fuzzy inference rules, the positions of embedded tubes are identified by inferring them. Third, 3D links of the identified positions are formed to visualize the continuous shape of the tubes. Consequently, the tubes are extracted, and their 3D shapes are visualized. The experimental result on the specimens shows that our method was able to find all tubes that exist in the radiograph and the schematic. Our method could thus provide the internal information of concrete with sufficient accuracy required in the practical construction work.

We describe a new automated method for detecting embedded objects in the ultrasonic non-destructive testing (NDT) system. A-scan waves collected by our developed system are converted into a B-scan image. The sensor system has the noise signals independent from targets to be detected. In the ultrasonic NDT system, the signals are due to disturbing of echoes produced by the transducers and multiple reflections. These signals are called inherent wave. This paper first proposes the estimation method of the inherent wave from the B-scan image. After this method subtracts the inherent wave, the resultant image (suppression image) is considered as the image consisting of only echoes from the embedded objects. Second, analysis of the intensity histogram of the suppression image leads the candidate points of embedded objects. Finally, fuzzy if-then rules can represent information on distribution of the intensity histogram and the homogeneous intensity levels of the objects. Evaluated degrees from the inference results can demonstrate the embedded objects. The method was applied to concrete members with reinforcing bars, resin tubes and steel pipes. The experimental results showed that this method was able to automatically detect the embedded objects with high accuracy and to display the location of embedded objects.

This paper proposes a sensing system for a behavior detection system using an ultrasonic oscillosensor and an air pressure sensor. The ultrasonic oscillosensor sensor has a cylindrical tank filled with water. It detects the vibration of the target object from the signal reflected from the water surface. This sensor can detect a biological vibration by setting to the bottom bed frame. The air pressure sensor consists of a polypropylene sheet and an air pressure sensor, and detects the pressure information by setting under the bed's mattress. An increase (decrease) in the load placed on the bed is detected by the increase (decrease) in the pressure of the air held in the tube attached to the sheet. We propose a behavior detection system using both sensors, complementally. The system recognizes three states (nobody in bed, keeping quiet in bed, moving in bed) using both sensors, and we detect the behavior before getting out of bed by recognized these states. Fuzzy logic plays a primary role in the system. As the fundamental experiment, we applied the system to five healthy volunteers, the system successfully recognized three states, and detected the behavior before getting out of bed. As the clinical experiment, we applied the system to four elderly patients with dementia, the system exactly detected the behavior before getting out of the bed with enough time for medical care support.