The emergence of autonomous driving is becoming a reality worldwide. However, in emergency situations, human drivers must assume control of the vehicle, hence detecting driver vigilance is critical. Although many electroencephalographic studies have documented alterations in brain rhythms associated with fatigue, the results have been inconsistent. Furthermore, in high-level automated driving contexts, the driver does not operate the vehicle except in emergencies. In this study, magnetoencephalograms from eight healthy participants were recorded as they watched a 20-minute video from the driver’s perspective. The participants were instructed to press a button when a checkered flag appeared, simulating their response to an emergency. Statistical analysis demonstrated that response time to the checkered flag was prolonged during the End period (final third) of the experiment (p = 0.04). The increase in reaction time from the Beginning (first third) to the Middle (middle third) period correlated negatively with alpha-band activities in the occipital cortices during all three periods (Beginning, r = −0.72; p = 0.04; Middle, r = −0.73, p = 0.04; End r = −0.78, p = 0.02). In other words, participants with higher alpha-band activity in the Beginning period did not experience prolonged reaction time toward the Middle period. This result suggests that alpha-band activity in the occipital cortex may serve as an indicator of whether participants maintain vigilance during autonomous driving.

This study applies the attractor reconstruction method to photoplethysmography (PPG) signals to explore their potential in preventive health monitoring. Conventional PPG analysis methods typically rely on beat-by-beat segmentation and feature point detection, both of which are highly sensitive to noise, waveform distortion, and individual variability. To address these limitations, we applied attractor reconstruction trajectories (ACTs) as an alternative analytical approach to PPG signal analysis. ACTs utilize the entire waveform to reconstruct signal dynamics, facilitating a more holistic and noninvasive assessment of vascular health. Fifty-five participants aged 20-89 years, comprising healthy individuals and patients with chronic liver or kidney disease were studied. Three-dimensional ACTs were constructed from fingertip PPG signals and their first (DPPG) and second (SDPPG) derivatives. ACT morphologies were classified using a template-matching approach, and their distributions were compared across age groups and signal types. ACT classification revealed distinct structural characteristics across signal types. For PPG ACTs, Approximate Triangle (40.5%) and Three Points (29.5%) were the most frequent patterns, while Three Points pattern was observed only in younger and healthy middle-aged groups and exhibited the lowest uncertainty rate (14.3%). DPPG ACTs showed the greatest structural diversity, with Radiating pattern predominating in younger participants and a clear transition toward dominance of Three-lobed pattern in older groups (60.7% in participants aged 80-89 years). In contrast, SDPPG ACTs were dominated by Irregular pattern (48.6%) across all age groups and showed the highest variability and uncertainty (17.6-20.7%). These results demonstrate that ACTs can capture age-dependent differences in PPG-derived signals, with DPPG ACTs exhibiting the clearest age-related structural transitions. While limitations remain regarding sample size and classification automation, the findings support the potential of ACT-based analysis as a complementary framework for noninvasive assessment of vascular aging using PPG signals.

Standard methods for evaluating swallowing function, such as videofluoroscopic swallowing studies and fiberoptic endoscopic evaluation, are invasive and environment-dependent. In our previous study, we confirmed through simultaneous VF measurement that specific waveforms detected by an earphone-type sensor correspond to the timing of soft palate movement during swallowing. The aim of this pilot study was to assess the feasibility and potential applicability of this sensor by examining the temporal relationship between swallowing sounds and sensor waveforms. The earphone-type sensor uses an LED and a phototransistor to detect ear canal movements via reflected infrared light. Six healthy adults swallowed 3 mL of water five times while wearing the sensor in the left ear and a throat microphone on the neck. Swallowing sounds (S I, S II, S III) and waveform points (SA, SB) were analyzed for timing. The mean SA-SB interval was 0.37 ± 0.14 s. SA preceded S I in 52.2% and S II in 100% of cases. No significant difference was observed between SA and S I, whereas SB and S II showed a significant difference (p < 0.001). These findings suggest that the earphone-type sensor waveform reflects the timing of soft palate movement prior to passage of the food bolus into the hypopharynx and esophagus. Although limited by a small sample size, this pilot study provides fundamental evidence supporting the feasibility of noninvasive swallowing assessment using an earphone-type sensor.

Motor imagery (MI)-based brain-computer interfaces (BCIs) have received increasing attention in recent years. While most of the existing electroencephalogram (EEG) based MI-BCIs have demonstrated satisfactory performance in binary and four-class classifications, it is essential to develop intuitive systems with higher dimensionality for object-centered control to support daily living. However, increasing the dimensionality in MI-BCI has been a major challenge. One of the main reasons is the difficulty in selecting appropriate MI tasks. To solve this problem, we proposed a novel MI paradigm that selected six real life movements that have underlying intents aligned with the object-centered control of external devices along three axes: front-back, left-right, and up-down. The users' perspective of intuitiveness was evaluated using an intuitiveness questionnaire. The results from twelve participants indicated that using the proposed MI tasks achieved closer cognitive matches compared to the traditional tasks. Information-preserved data conversion was implemented to preserve temporal, spatial and band power features in video-like data. A spatiotemporal convolutional neural network (CNN) was then applied for six-class classification. The highest accuracy achieved by the proposed system in six-class within-participant classification was 46.88% for the hold-out validation dataset and 39.66% for the test dataset. The average validation accuracy was 33.99% and the test accuracy was 26.26% for seven healthy participants, which were significantly higher than the chance level of 16.67%. Furthermore, the key metrics obtained from the confusion matrices suggested that both the similarity of somatotopic representations and the symmetry involved in the MI signals significantly affected the classification performance. In this study, we introduced the use of intuitive movements in intuitive MI-BCI paradigms and demonstrated their suitability for controlling external objects in 3D space. Our results indicate that compound MI signals involving multiple joints are separable using deep neural networks, which would greatly expand the available tasks for multiclass MI-BCIs. Although large individual differences were observed, the possibility of adopting spatiotemporal CNN classifiers to decode complex MI signals was demonstrated. Based on our findings, future research with larger sample sizes should consider task-specific modifications of the architecture of the classification model.

We investigated a system identification method for modeling the standing posture without external perturbation. Assuming that postural sway during quiet standing is caused by internal white noise, the method adopts an autocorrelation matrix consisting of body position and velocity parameters. We showed that the method can be applied to center-of-mass fluctuation measurement data. Since we used only a force plate to minimize the burden on the subjects, the velocity could not be measured directly. Therefore, we first demonstrated through simulation that ankle joint stiffness can be estimated by numerically deriving the velocity from the displacement. The velocity was calculated using the central difference approximation, and the ankle stiffness estimation error was obtained. The system identification method was then applied to the center-of-mass measurement data, and the ankle stiffness was estimated. Simulations showed that even when the velocity was calculated through numerical differentiation, the ankle joint stiffness could still be estimated with an accuracy of less than 1% error. The ankle stiffness estimated from the measured center of mass was slightly higher than the gravitational torque coefficient, but lower than that obtained in our previous electrical stimulation study (Uchiyama T: Adv Biomed Eng. 13, 223‒229, 2024). This suggests that the control method for ankle stiffness may depend on the presence or absence of external disturbances.

Purpose: We developed the Shared Child Health Record (SCHR) using the Fast Health Interoperability Resources International Patient Summary (FHIR IPS) standards to facilitate data interoperability between the Lao Electronic Health Record-Bahmni (EHR-Bahmni) and Electronic Immunization Record-District Health Information Software Version 2 (EIR-DHIS2). This study evaluated the impact of vaccination completeness and coverage on the value of the SCHR.

This study aimed to compare the sound pressure levels (SPL) generated by four different ventilator models operating in high-flow nasal cannula (HFNC) mode during HFNC therapy. Although HFNC therapy is widely used for respiratory support, the acoustic characteristics of the HFNC mode in ventilators remain largely unexplored. Four ventilators were investigated: Vivo3, EO-150, LUISA, and prismaVENT50-C. SPL were measured using a digital sound level meter positioned 1 m from each ventilator operating at flow rates of 30, 40, 50, and 60 L/min. At the low flow range (30-40 L/min), all ventilators exhibited similar SPL of 32.6-35.0 A-weighted decibels [dB(A)]. However, a marked difference was observed at high flow rates; the Vivo3 demonstrated a substantial increase to over 50 dB(A) at flow rates exceeding 40 L/min, whereas the other three ventilators maintained gradual increases, reaching only 38.8-40.4 dB(A) at maximum flow. Analysis of SPL change rates across different flow ranges revealed that the Vivo3 exhibited a dramatic rate of 1.69 dB/L/min in the 40-50 L/min range; this rate was approximately 6 to 8 times higher than those observed in the other ventilators. The notable variation in acoustic generation among the ventilators, particularly at high flow rates, highlights the importance of considering SPL characteristics when selecting devices for clinical use. At high flow rates, the Vivo3 generated SPL exceeding the recommended SPL limits for healthcare settings, which may potentially impact patient outcomes, including sleep quality and recovery.

Microneedle arrays (MNAs) consisting of numerous tiny needles have attracted considerable attention for selective intradermal and subcutaneous delivery of pharmaceutical agents. Since reliable skin puncture and insertion are essential for dose control, applicators that push MNAs into the skin using springs or other mechanisms have been extensively researched. The puncture and insertion capabilities of an MNA depend on the compressive load generated when it is pressed against the skin. The compressive load is considered to be influenced not only by the MNA per se and the application method, but also by the structures and mechanical properties of the biological tissues at the application site. Nevertheless, most previous studies on evaluating the puncture and insertion abilities of MNAs did not consider the impact of subcutaneous soft tissues. In this study, the compressive loads generated by an applicator were measured at various sites of the body in 15 healthy adults. We used a proprietary test applicator that employed a spring to impact the skin at high speed, and a system that can measure the short-term load generated during impact. Furthermore, the structures and mechanical properties of the skin and subcutaneous soft tissues at the same body locations among the 15 individuals were evaluated and correlated with the measured compressive loads. The compressive load induced by the impact varied significantly with location, and correlated highly with the distance to the bone. There was no correlation with age, sex, or body mass index, or with the structure or mechanical properties of the skin. These results indicate that the structure of the subcutaneous tissue has a strong influence on the compression load and the ability to puncture and insert the MNA. The findings of this study can be useful for developing MNAs and applicators, determining their essential physical characteristics, selecting the appropriate administration locations, and developing non-clinical methods for evaluation.

The ventricular assist device (VAD) provides supports for cardiac function and is used in the long term by many patients on the heart transplant waiting list. Thus, during VAD operation, it is ideal to monitor circulatory status and cardiovascular parameters online to reduce complications, detect abnormal conditions early and control the ventricular assist device. However, wearing additional sensors in the body for monitoring is not ideal, and it is desirable to obtain various types of information about the body using as few and easily measurable sensors as possible. In this study, we propose a method for real-time estimation of the circulatory state [left ventricular pressure (LVP), aortic pressure (AoP), aortic flow (AoF)], and cardiovascular parameters, such as peripheral vascular resistance, and determination of valve opening using the estimated AoF. The proposed method utilizes information generated by the VAD, which is easy to measure. First, the method approximates the heart and circulatory system using an electric circuit model based on the Windkessel model. This model is represented by a state-space model with pump flow rate and pump inlet pressure as inputs and pump outlet pressure as output. In the next step, the model parameters are estimated using an unscented Kalman filter, which can handle nonlinear functions, enabling the estimation of circulatory systems. Validation using data from a hybrid simulated circulatory system and animal studies demonstrate that the normalized root mean square errors (nRMSE) for LVP, AoP, and AoF were within 30%, 13%, and 7%, respectively. Moreover, it was possible to estimate opening of the aortic valve. In particular, data from the animal experiments illustrated that the proposed method can accurately estimate changes in the circulatory state by taking medication, highlighting the effectiveness of the proposed method.