In recent years, the number of people complaining about inappropriate sleeping habits has been increasing annually, and non-contact measurement of vital signs during sleep has attracted attention. A non-contact heart rate measurement method using an infrared camera was developed. However, camera-based heart rate estimation is problematic because motion artifacts significantly impact measurement results. The conventional method combines multiple heart rate estimation methods or makes appropriate peak selections using the coordinates of the facial image to perform heart rate estimation that considers motion artifacts. However, motion artifacts are caused not only by changes in facial position but also by changes in angle. Conventional methods cannot properly remove body motion, which affects the heart rate estimation system. Therefore, this study proposes motion artifact removal by combining an adaptive filter with singular spectrum analysis. Additionally, the Euler angle information of the head extracted using a convolutional neural network was incorporated as a parameter to estimate the heart rate corresponding to the motion artifacts caused by angular displacement. The estimation accuracy was demonstrated through the experimental results.

Abnormal breathing in daily life can be indicative of various diseases. Daily measurement of breathing rate enables the early detection of diseases and rescue of patients in severe cases. Wi-Fi, which is widely used in day-to-day life, is a suitable sensing method for daily measurements. Accordingly, a breathing rate estimation method using the Wi-Fi channel state information (CSI) was developed. In the conventional method, an accuracy of up to 99% was achieved in the estimation of breathing rates for singular individuals. However, this accuracy is insufficient for multiple individuals owing to the interference of vital signals obtained from CSI. Therefore, this study proposes a multi-person breathing rate estimation system that suppresses vital signal interference. To estimate the breathing rate of multiple people, the proposed method employs Tucker decomposition to decompose vital signals from the CSI. The processing speed and accuracy were improved using the proposed method. Finally, the effectiveness of the proposed method was demonstrated through experiments.

In recent years, population aging in developed countries has become an increasing problem, and labor shortages in the transport industry have become an issue. Human-robot interaction (HRI) is expected to address this problem. Admittance control, an HRI method, is a useful control technique for reducing the burden on humans. However, the vehicle may behave dangerously if the wheels or body collide with an obstacle, or if a sudden large force is applied by a human. This behavior is due to the windup caused by motor torque saturation and reduced tracking performance of the admittance controller. To address this problem, admittance control that considers torque saturation was developed. In this study, a power-assisted control system was constructed using admittance control. The usefulness of the proposed method for tracking performance degradation was demonstrated via numerical simulation. The root mean square errors (RMSE) of the error between the reference and response velocities were compared between the proposed method, the anti-windup method, and standard admittance control. It was confirmed that the RMSE of the proposed power-assisted system was the smallest and that a reduction of approximately 99% could be achieved compared to the standard admittance control-based power-assisted system.

Surface-mounted permanent magnet synchronous motors (SPMSMs) are used in various fields because of their high efficiency and high power density characteristics. Because they are generally driven by vector control, a type of current control, the position of the rotor must be detected. Rotor position detection is generally achieved by attaching an encoder or resolver to the exterior of a motor. This inevitably increases the volume and weight of the exterior of a motor. Although sensorless methods are available for determining the rotor position, their detection accuracy is low and it is difficult to acquire stable position information over the entire speed range. An embedded magnetic absolute encoder, which detects the rotor position by observing its magnetic flux, can acquire the rotor position without requiring the need for an external position sensor. However, calculating the absolute angle when two or more SPMSM pole pairs exist is difficult. This study proposes an embedded magnetic absolute encoder in an SPMSM that can calculate absolute angles. The proposed SPMSM enables the calculation of absolute angles using permanent magnets with different magnetic flux densities.

In recent years, owing to the implementation of advanced safety technologies, there has been a reduction in the number of automobile accidents. However, traffic accidents caused by human error, such as driving inattentively or while drowsy, are occuring more frequently. To address this problem, automated systems that detect whether a driver is drowsy have been developed. Typically, such systems use electrode-type sensors to acquire electroencephalogram or electrocardiogram readings and perform in-cabin monitoring using cameras. However, wearing such sensors involves some discomfort for the driver, and these cameras are susceptible to noise; moreover these systems generally record personal information such as the driver's face. In this study, we propose an estimation system using heart rate variability (HRV) features acquired using noninvasive devices that cause less restriction on the driver's movement. Our proposed system obtains biometric information from gyroscopes attached to the driver's seatbelt and derives features indicating HRV. Additionally, we have trained a random forest classifier to detect drowsiness as this approach showed relatively high accuracy in prior studies. The results of an experimental evaluation confirm that the proposed system achieved an accuracy of 85.4 % in distinguishing between wakeful and drowsy states. We used the Karolinska Sleepiness Scale (KSS) to validate the results of the estimations. Our method can obtain biometric data without using invasive sensors or substantially restricting the driver's movements, and the random forest model can distinguish sleepiness.

In this study, we developed a novel magnetic absolute encoder with different magnetic flux densities. Magnetic encoders can be created with a simple structure consisting of a magnet magnetized in a sinusoidal shape and a sensor that detects the magnetic field. They are known for their compact design and excellent environmental resistance. A method of obtaining even higher resolution signals while maintaining absolute accuracy is to use a magnet with different magnetic flux densities and Hall sensors. However, there are physical limitations in improving the accuracy of a multi-polarizing magnet with different magnetic flux densities. In this study, we developed a magnetic encoder with a new structure using a magnet with different magnetic flux densities and anisotropic magnetoresistive(AMR) sensors. The developed system can calculate the absolute angles and output angular frequency signals twice as high as those of AMR sensors, thereby improving the resolution without requiring a multipole magnet. In the past, distortion was considered to occur because AMR sensors were used outside the saturation sensitivity region to capture the amplitude characteristics of a magnet with different magnetic flux densities; however, this was solved by correction using a look-up table. Finally, an actual device was created and validated by comparing the measured values obtained in the experiment with those obtained using a Hall sensor.