To clarify the autonomic dysfunctions in amyotrophic lateral sclerosis (ALS) patients, we investigated the relationship between blood pressure (BP) and pulse rate (PR), QTc interval, and coefficient of variation in the R-R interval (CVR-R) in ALS patients. BP and PR were determined automatically every 30 min for 24 h in 6 patients with ALS and in 18 healthy age-matched volunteers. The QTc interval and CVR-R were also evaluated using an electrocardiogram. The relationship between BP and PR was lost in the ALS group. There was no significant difference in the QTc interval or CVR-R between the ALS and control groups. We speculate that the imbalance between BP and PR observed in the ALS group reflects a disorder in the balance between the sympathetic and parasympathetic nervous systems.

Pursuit of the lowest size-limit of wireless sensor node may not only reduce production cost and power consumption, but also enable its layout-free ubiquitous applications, especially in `green' sensor network to compress energy consumption through visibility and optimization. In this work, we engage in developing the world smallest wireless sensor node form both system block integration and physical interconnection points-of-view. A customized RF-transmitter IC with low power consumption, universal interface to analog and digital sensors, and power management function was developed by using 0.18um 1.8V/3.3V 1P6M logic process. By introducing buried bump interconnection technology (B2it™), one of the world smallest wireless sensor nodes for humidity and temperature monitoring was successfully achieved with the size of 3.9×3.9×3.5mm. Configuration of above sensor node also enables its easy assembly with stand-alone power source and flexible antenna for wide variety of applications.

We have fabricated and evaluated the mechanical, optical and fluidic characteristics a 50µm wide and a 30µm deep micro-channel device produced by hot emboss and direct bonding of PMMA plate with dimensions of 20mm × 20mm × 1mm. The fabricated micro-channel device was evaluated the bond strength, which was confirmed to be high enough for practical use as well as for quite severe cleaning conditions as ultrasonic cleaning in pure water. The optical loss around bonded interface was also evaluated and no increase in the light absorption was observed. The above results confirmed that the hot emboss and direct bonding technologies for micro-channel manufacturing using the PMMA plates realizes high performance micro channel devices.

This paper presents a CMOS-compatible triaxial Hall sensor featuring a novel vertical Hall device (VHD) structure that achieves high sensitivity and low cross-axis interference. The proposed sensor utilizes both vertical and horizontal Hall effects by exploiting the high conductivity of the Deep N-Well (DNW) layer, thereby eliminating the need for space-consuming shallow trench isolation (STI). The architecture supports compact and highly sensitive sensor integration by avoiding exclusive reliance on the vertical Hall effect, which is highly dependent on device dimensions. Moreover, a spatially symmetrical emitter structure is introduced to effectively minimize cross sensitivity. The sensor was fabricated using a standard 180nm TSMC CMOS process and experimentally evaluated using on-chip operational amplifiers, whose bias conditions were adjusted to maximize the voltage gain during measurement. The measured sensitivities were 967.5 mV/T for the vertical Hall element and 656.7 mV/T for the horizontal Hall element. Angular response measurements confirm cross sensitivities below 0.01 %, demonstrating excellent directional selectivity and validating the effectiveness of the proposed structure.

In this paper, the relation between estimated instantaneous sleep satisfaction using nonlinear multiple regression analysis and activity of the autonomic nervous system (ANS) is visualized and evaluated using the proposed scatter plot chart. We made a hypothesis and tested it such that the parasympathetic nerve activity (PNA) index representing the relaxation component rises as enhancement of the estimated instantaneous satisfaction level, and the sympathetic nerve activity index representing the stress component rises as reduction of the estimated instantaneous satisfaction level, during sleep. In the results of these experiments, when the average value per night of estimated instantaneous sleep satisfaction during sleep was level 3 or more, the number of points which are estimated satisfaction of level 3 or more, in the PNA index above the average value, was greater than that of less than level 3. It was found that the proposed method is effective for evaluating states of the ANS in sleep.

Agitation-related movements in intensive care units (ICUs), such as unintended tube removal and bed exit attempts, pose significant risks to patient safety. The wearable inertial measurement units (IMUs) offer a potential means of capturing such movements. However, the technical feasibility of discriminating ICU-relevant agitation motion patterns from multi-site IMU data remains insufficiently established. To evaluate the technical feasibility of using a convolutional neural network (CNN) applied to multi-site IMU signals to discriminate predefined ICU-relevant agitation-related motion patterns under controlled experimental conditions. Fifteen healthy volunteers performed six scripted movements designed to emulate ICU-relevant agitation-related behaviors while wearing seven IMU sensors on the limbs and waist. A CNN comprising three convolutional layers with kernel sizes of 3, 5, and 7 was trained using 1-s windows extracted from 8-s trials and evaluated using leave-one-subject-out cross-validation. The performance was summarized using macro-averaged accuracy, sensitivity, specificity, precision, and F1 score. Across 135 independent training runs, the CNN achieved a median macro-averaged accuracy of 77.0%, sensitivity of 77.0%, specificity of 95.4%, and F1 score of 77.4%. These results indicate stable window-level discrimination of the predefined motion classes under standardized conditions. This proof-of-concept study demonstrates that multi-site IMU signals combined with CNN-based modeling can technically discriminate ICU-relevant agitation-related motion patterns in a controlled laboratory setting. Although these findings do not establish clinical validity in ICU patients, they provide a methodological foundation for future studies aimed at patient-level validation and real-world critical care deployment.