Tear interferometers are used to observe the tear film lipid layer (TFLL) for dry eye examination in the clinical setting. However, these devices are large and expensive, and they are not accessible to the general public for eye care. Smartphone-based interferometry (SBI) has been developed for daily monitoring of eye health. In this study, the kinetics of the TFLL in 40 participants, including 9 contact lens (CL) wearers, was analyzed using tear film interference images taken by SBI. The lipid spread time (LST), the initial velocity of TFLL spread calculated from Voigt model of viscoelasticity, and the tear meniscus height (TMH) were measured. The average LST was 1.1±0.5 s and 1.6±0.6 s for non-CL and CL wearers, respectively. TFLL spread height and the Voigt model were strongly correlated for both non-CL wearers (R²=0.97) and CL wearers (R²=0.98), respectively. Furthermore, the initial velocity of TFLL spread was found to correlate with the TMH for only non-CL wearers (r=0.534, p=0.003). In summary, this study shows the potential of SBI as a new tool for non-invasive assessment of TFLL kinetics.

Exposure to time-varying, low-frequency and high-intensity magnetic field (MF) induce electric field (EF) inside the human body, producing stimulus effects such as nerve fiber excitation or synaptic modulation. To measure such stimulus effects by low-frequency MF expsoure in real-time, we developed a fluorescent recording system using optical fibers that is neither affected by the MF nor affects the MF distribution. In this study, a numerical calculation model composed of voxels with a 6.25 µm spatial resolution was developed. Using this numerical model, we evaluated the distribution of the EF generated inside three-dimensional neuronal tissue called neural spheroid, under 50 Hz sinusoidal wave, 300 mT (root mean square) uniform MF exposure. We also investigated the influence of the optical fiber on the electric field distribution in neural spheroid. As a result, MF produced an induced EF in the neural spheroid of more than 4 V/m, well above the theoretical threshold of synaptic modulation. These results indicated that our experimental system was suitable for the evaluation of the threshold of stimulus effects using neural spheroid.

The Pick-and-place movements are used in the fields of light work and rehabilitation, but the evaluation method of the movements is based on the number of times and time, and the results may change depending on the subject’s intention. In this study, we used a synchronous pick-and-place movement to extract the temporal fluctuations of movements, and we investigated the degree of proficiency of the movement by focusing on the body control system. As a result of the experiment, it was shown that there is a relationship between the trigger period, which represents the frequency of pick-and-place, and the proficiency in the pick motion.

Single-walled carbon nanotubes (CNTs) are carbon materials with unique thermal, optical, mechanical, and electrical properties, with hollow cylindrical structures of a few nanometers in diameter. CNTs cut to about 10 nm (Ultrashort CNTs, US-CNTs) can spontaneously insert into lipid bilayers. Therefore, applications have been proposed to combine CNTs with lipid bilayers to give the membranes the properties of CNTs. However, CNTs interact with membranes to induce morphological changes in the membranes, which may hinder these applications. In this study, to investigate the effects, US-CNTs are exposed to lipid bilayer vesicles (giant unilamellar vesicles, GUVs), which are used as a model for cell membranes, and the changes in membrane morphology with each US-CNT concentration were evaluated by fluorescence microscopy. As a result, GUVs show morphological changes upon exposure to US-CNTs, eventually transforming into a multiple vesicle-linked shape. This result suggests an increase in the area and asymmetry of the GUV membrane. Based on these results, we have proposed a hypothesis regarding the mechanism of morphological changes induced in the GUV membranes by US-CNTs exposure.

Motor imagery (MI) based brain-computer interface (BCI) has been extensively studied and advanced in several fields of applied brain science. This study investigates a neurofeedback training protocol for left- and right-hand grasp MI-BCI systems. An obstacle within MI-BCI is the inability of participants to perform the BCI task, commonly referred to as BCI illiteracy. Low performance amongst these users is common as well. To improve the performance of BCI, various training protocols have been investigated by other research groups. The problem with these protocols is that they are designed with a balanced dataset. Similarly, regarding the biases seen towards hand dominance for motor execution tasks, participants have also shown a performance bias in MI tasks. A trial-adjusted neurofeedback protocol is proposed to address this MI bias in participants. The number of trials in each condition is adjusted for the proposed neurofeedback by MI bias. Trials are adjusted to increase the number of times participants can perform their weak MI task. This study aims to investigate the effects the trial-adjusted neurofeedback had on participants' cognitive and classification performance in the MI-BCI. Band power analysis and classification evaluations are investigated to identify the proposed neurofeedback's effects. Both the band power analysis and classification performance results show a difference in effect between control-balanced neurofeedback and the proposed trial-adjusted neurofeedback. The trial-adjusted neurofeedback positively affects participants' cognitive performance and classification ability in MI-BCI. This study demonstrates the positive effects of neurofeedback when addressing the bias in MI performance.

Geometric optical illusions consist of three factors: space/position, angle/direction, and assimilation/contrast. Illusions in this category can be explained by how much each factor affects the magnitude of the illusion. Each factor affects one of the stages of human information processing: sensation, perception and cognition. However, the relationship between the factors in geometric optical illusions and the stages of human information processing remains unclear. Therefore, we aimed to clarify this relationship by analysing visual evoked potentials during Delboeuf illusion recognition. In particular, we focused on the Delboeuf illusion. In this paper, we recorded the EEG signal during the display of the Delboeuf illusion and conducted a behavioural experiment with the Delboeuf illusion. As EEG results, we observed P100, N200, P200 and N300 EEG during the recognition of the Delboeuf illusion. Significant differences were observed in each EEG with and without the Delboeuf illusion effect. In terms of behavioural results, we observed a significantly higher occurrence of the illusion during depth perception due to the Delboeuf illusion. These results suggest that the Delboeuf illusion is related to information processing in depth perception. Since the main cause of the Delboeuf illusion is assimilation/contrast, it is likely that assimilation/contrast is related to information processing in depth perception.

In the medical field, several studies have been conducted to elucidate the characteristics of human cells using microorganisms that have the same functions as human cells. To utilize microorganisms, it is necessary to cultivate microorganisms efficiently. The existence of eggs is an important indicator for assessing the growth of microorganisms. The purpose of this research is to automatically determine the presence or absence of microbial eggs. In this study, since it is necessary to reduce the number of pixels from high-resolution images, the presence or absence of eggs is classified by cutting out the area around the body center of the microorganism and inputting it into EfficientNet. To estimate the center of the body of the microorganism, we consider applying Dijkstra's algorithm to a single pixel wide image obtained by the thinning process. The shortest path between any two end points of the narrowing shape is calculated using Dijkstra's algorithm. The midpoint of the longest shortest path is taken as the body center.

As an elemental technology for an expression recognition training system, a head mounted display with gaze detection function was used to display three-dimensional facial expressions, and the characteristics of expression recognition for three distances (near, medium and far) as personal space were investigated.

In recent years, many devices have been developed for stroked patients to evoke pseudo experiences through the movement of their paralyzed limbs in virtual reality. These systems require immersive environments, such as head mounted displays or large booths, which are highly expensive. Therefore, this study focuses on rubber hand illusion (RHI) to create immersive environments without expensive setups. We propose an automatic evocation of RHI using a laptop computer and vibrator to develop simple immersive environments.

Many studies have reported inconsistent results and features even when imagining the same movement using kinesthetic motor imagery (KMI). This inconsistency may be attributed to how individuals perform kinesthetic motor imagery, which has not been compared in previous research. To address this gap, this study categorizes kinesthetic motor imagery into two types of KMI; double-hand kinesthetic (D-KMI) and single-hand kinesthetic motor imagery (S-KMI). The primary objective of this study is to investigate whether there are differences between D-KMI and S-KMI and whether these differences contribute to variations in the results reported in previous research.

Recently, the use of cultured skeletal muscle tissue for actuators has been studied, but such actuators have weak contractile force. Conventional three-dimensional cultured bio-actuators improve contractility by utilizing the linear direction of muscle fiber extension. In this study, however, pupillary sphincter muscle was mimicked by controlling the orientation of muscle fibers using pillars. Experimental results showed that pupil contractile movement was realized and that the proposed device improves the contractile force. In the future, we plan to investigate the mechanism of electrical stimulation to the muscle tissue to use this device as an actuator.

Transdermal drug delivery system is a technique that delivers drugs via transdermal absorption, and we have studied ultrasound-responsive liposome-encapsulated gel patches for this system. The liposomes contained the drug models to prevent the leakage through the gel pores and to provide sustained release. The drug models could also be released by breaking the liposomes by ultrasound irradiation. In this study, we investigate the optimal parameters of gel patches for this model. Nanosized liposomes containing fluorescent compounds as drug models are embedded in agarose gels of different concentrations, and the gel concentration that most efficiently traps the liposomes are selected. Furthermore, we investigate the ultrasound intensity that best promotes drug model release by irradiating ultrasound at different intensities and observing the fluorescent intensity of the buffer solution outside the patch before and after irradiation with a spectrophotometer.

Studying biological activities over an extended period is essential for a deeper understanding of human physiological function including brain plasticity. However, meeting this requirement demands a highly biocompatible device with favorable electrical properties. Hence, carbon nanotubes (CNTs) emerge as a suitable material for electrodes due to their exceptional electrical conductivity and ability to enhance electrode surface area. In our research, we initially apply a polyethyleneimine (PEI) coating to the glass substrate to enhance the interfacial adhesion between the CNTs and the substrate. Subsequently, we culture mouse myoblast cells (C2C12) on the CNT-coated glass substrate to illustrate that CNTs have minimal impact on cell adhesion and are therefore suitable for use as electrodes.

In recent years, wearable interfaces have gained significant research attention. Additionally, since wearing masks have become a common practice due to the spread of the new coronavirus, our focus was on a mask-type interface. In a previous study, we successfully developed a facial expression recognition system using the expansion and contraction of the face mask, along with conductive threads. In this study, our objective was to verify the feasibility of the developed facial expression recognition system for personal authentication. The experiment results provided strong evidence that personal identification is possible by utilizing specific facial expressions characteristic of each user.

We have proposed exponentiation conversion circuit utilizing weak inversion operation that enables voltage input and output. This circuit enables exponentiation conversion by converting input signal logarithmically, amplifying by power exponent value and converting signal exponentially. In this study, in order to expand input voltage range and signal dynamic range of exponentiation conversion circuit, the number of vertical stacking of devices was reduced and the accuracy of the multiplying circuit was improved.

The purpose of this study is to classify three types of cooking methods, "deep fried", "stir fried", and "boiled", using the characteristics of temperature changes. In the classification of cooking methods, the temperature change measured using a thermo sensor is used. From the temperature data of each cooking method, we focused on the mean value and its slope higher than the threshold, and the Number of pixels (Range above 100 degrees), and created and verified the prototype of the cooking method judgment program. As a result of verifying each cooking method four times, it was possible to classify the cooking methods "deep fried", "stir fried", and "boiled" under the current measurement environment by using at least 90 sample data (about 7.5 minutes).

The iterative learning control (ILC) achieves optimal feedforward control laws to follow a prescribed reference signal. Among several studies on the ILC approaches, the ILC incorporated with Proportional, Integral, and Derivative (PID) controllers have been studied. In these approaches, PID controller works for improving feedback properties, such as disturbance attenuation properties. However, the conventional approaches keep fixed PID gain parameters without evaluating the feedback properties. Hence, the study introduces a PID gain tuning approach using the iterative feedback tuning (IFT). The study also introduces a compensation method for feedforward control law achieved by ILC. Finally, the paper shows the effectiveness of the proposed method through a numerical example.

The initiative of digital transformation (DX) in the infrastructure has been promoted to improve productivity at construction sites utilizing data and digital technologies. Research and development on autonomous excavators have been conducted in the DX. To achieve more productivity improvement, the cooperative construction by multiple autonomous machines is required. For realizing the construction method, utilizing operation and construction data collected in the data platform is effective. This paper presents a cooperative control system based on data-driven numerical optimization method to optimize motion plan of an autonomous excavator. The effectiveness of the proposed method is verified by a numerical simulation.

Reinforcement learning (RL) has been studied as an effective method for tasks without explicit training data. In recent years, it has been shown that deep RL (DRL), which introduces deep learning algorithms into RL, can be applied to more complex tasks. Recent studies have introduced DRL into sliding mode control (SMC), yielding important research results. However, the black box in the computational process of a neural network (NN) is a troublesome problem, especially when NNs treat control engineering. In this paper, we propose a novel approach to determine parameters for SMC using DRL. In this method, a policy model constructed to be equivalent to the equation of the SMC input is trained by a DRL method that can treat continuous action spaces. The results show that the proposed method can determine the SMC parameters that successfully control the ball beam system through numerical experiments.

In this paper, a novel synthesis method of disturbance observer which can asymptotically estimate a biased sinusoidal disturbance using the conventional disturbance observer is studied. The basic idea is to synthesize the disturbance observer gain matrix based on the following two considerations: 1) a zero located at the origin can be assigned to the closed-loop transfer function G_dd(s) from the disturbance to the disturbance estimation error, 2) all poles of G_dd(s) can be obtained in the complex left half-plane. The zero assignment of G_dd(s) can be accomplished using only one element of the gain matrix, and all poles of G_dd(s) can be assigned using the remaining elements of the gain matrix. The usefulness of the proposed method is verified by the angle control of a direct current(DC) motor system with the estimation performance of disturbances.

As part of a study on the potential medical applications of electric fields (EFs), we used an energy metabolism evaluation system based on breath gas analysis to evaluate the effects of restraint and EF treatment on the circadian rhythm indicated by the measured parameters. The results suggest that electric field treatment may affect the diurnal variation of carbohydrate and lipid metabolism induced by restraints. The results of this study will contribute to a new non-invasive approach for evaluating the safety and medical applicability of EFs.