• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.340-351
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• 2023

Wearable sensors designed for strain, pressure, and temperature measurements are essential for monitoring human movements, health status, physiological data, and responses to external stimuli. Notably, recent research has led to the development of high-performance wearable sensors using innovative materials and device structures that exhibit ultra-high sensitivity compared with their commercial counterparts. However, the quest for accurate sensing has identified a critical challenge. Specifically, the mechanical flexibility of the substrates in wearable sensors can introduce interference signals, particularly when subjected to varying external stimuli and environmental conditions, potentially resulting in signal crosstalk and compromised data fidelity. Consequently, the pursuit of non-interference sensing technology is pivotal for enabling independent measurements of concurrent input signals related to strain, pressure, and temperature, ensuring precise signal acquisition. In this comprehensive review, we present an overview of the recent advances in noninterference sensing strategies. We explore various fabrication methods for sensing strain, pressure, and temperature, emphasizing the use of hybrid composite materials with distinct mechanical properties. This review contributes to the understanding of critical developments in wearable sensor technology that are vital for their ongoing application and evolution in numerous fields.

• Journal of Biomedical Engineering Research
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• v.45 no.2
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• pp.75-80
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• 2024

This research investigates the influence of chair tilt functionality on biometric signals and user comfort, addressing the ergonomic challenges posed by modern sedentary lifestyles. Through an experimental study involving eight male participants, the impact of chair tilt on electromyography (EMG), heart rate, metabolic rate, pressure distribution, and distance between the lumbar spine and the lumbar support part of the chair was measured across different seating postures. The study utilized chairs with both synchronous and non-synchronous tilt mechanisms to explore how adjustments in chair design affect user comfort and physiological responses during prolonged sitting. Key findings suggest that chair tilt functionality can significantly reduce muscle activity and energy expenditure, enhancing user comfort and potentially mitigating health risks associated with prolonged sedentary behavior. Notably, the study revealed a preference among participants for chairs that aligned the rotational center of the tilt with the hip joint, highlighting the importance of this ergonomic feature in enhancing user comfort. Additionally, the research proposes a novel methodology for assessing seating comfort through the analysis of both biometric and physical signals, providing valuable insights for the development of ergonomic chair designs focused on user health and comfort.

• Fibers and Polymers
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• v.7 no.4
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• pp.450-456
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• 2006

The objectives of this study were to investigate physiological and psychological responses to the rustling sound of Korean traditional silk fabrics and to figure out objective measurements such as sound parameters and mechanical properties determining the human responses. Five different traditional silk fabrics were selected by cluster analysis and their sound characteristics were observed in terms of FFT spectra and some calculated sound parameters including level pressure of total sound (LPT), Zwicker's psychoacoustic parameters - loudness(Z), sharpness(Z), roughness(Z), and fluctuation strength(Z), and sound color factors such as ${\Delta}L\;and\;{\Delta}f$. As physiological signals, the ratio of low frequency to high frequency (LF/HF) from the power spectrum of heart rate variability, pulse volume (PV), heart rate (HR), and skin conductance level (SCL) evoked by the fabric sounds were measured from thirty participants. Also, seven aspects of psychological state including softness, loudness, sharpness, roughness, clearness, highness, and pleasantness were evaluated when each sound was presented. The traditional silk fabric sounds were likely to be felt as soft and pleasant rather than clear and high, which seemed to evoke less change of both LF/HF and SCL indicating a negative sensation than other fabrics previously reported. As fluctuation strength(Z) were higher and bending rigidity (B) values lower, the fabrics tended to be perceived as sounding softer, which resulted in increase of PV changes. The higher LPT was concerned with higher rating for subjective loudness so that HR was more increased. Also, compression linearity (LC) affected subjective pleasantness positively, which caused less changes of HR. Therefore, we concluded that such objective measurements as LPT, fluctuation strength(Z), bending rigidity (B), and compression linearity (LC) were significant factors affecting physiological and psychological responses to the sounds of Korean traditional silk fabrics.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.67-78
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• 2011

This paper analyzes the characteristics of drivers' workload observed from with 30 participant drivers with respect to two physio-physiological parameters. For investigating physio-physiological characteristics of road drivers, bio-signals from brain's occipital lobe between simulation experiment and real driving experiment are collected and analyzed. The major findings from the analysis are summarized as follows: First, the drivers' physio-physiological workload is a good parameter for explaining the workload characteristics of road drivers. Secondly, the two physio-physiological workload parameters selected, i.e., beta value and relative energy parameter, are revealed to be statistically significant. Thirdly, it is also revealed to be statistically significant to select 90 percentile measurements in simulator experiment to explain the road drivers' characteristics. Finally, the maximum workload of road design driver is 31.72 in beta parameter, whereas the minimum workload is 1.296 in relative energy parameter.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2B
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• pp.232-243
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• 2010

In this paper, various physiological signals of humans were measured and analyzed to inference their psychological state and biological information, and Bio-Signal Context aware system (BSC), which recognizes the current context of its users as well as the information of exterior environment and offers the service appropriate for them, was designed and implemented. The BSC extracts and analyzes the features from bio-signals, such as the measured electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR), with its different sensors, has the input of the analyzed results, and discriminates four psychological states of rest, concentration, tension and melancholy. In addition to the results of the discriminated psychological states, the information of biological condition analyzed from the user's bio-signals, for example, heart rate variability (HRV), Galvanic skin response (GSR) and body temperature, and the information of external environment related to the user's are collected to offer the service fit for the user's present biological condition by inferring and recognizing the user's present situation.

• Progress in Medical Physics
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• v.10 no.3
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• pp.141-149
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• 1999

This research analyzed the HRV signals by using wavelet transform to observe the activities of autonomous nervous system in a sleep state. This research also restructured the HRV signals from electrocardiogram and by using coefficient which was obtained through wavelet transform, analyzed the signals by frequency bandwidth. Then compared the analyzed results with existing frequency analyzing method using AR model techniques. The suggested wavelet coefficient from power spectrum component in the study shows a similar tendency with the results from FFT or AR model technique. Therefore, it can be found that power spectrum analyzing method by wavelet coefficient is a useful as a tool for analyzing autonomous nervous system activities using HRV signals. Since the suggested method able to clearly depict the progression of change in time zone, which was once impossible with the existing methods, it is presumed that it will be useful in other physiological signals.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of Korean Dental Science
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• v.10 no.2
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• pp.45-52
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• 2017

Calcium has versatile roles in diverse physiological functions. Among these functions, intracellular $Ca^{2+}$ plays a key role during the secretion of salivary glands. In this review, we introduce the diverse cellular components involved in the saliva secretion and related dynamic intracellular $Ca^{2+}$ signals. Calcium acts as a critical second messenger for channel activation, protein translocation, and volume regulation, which are essential events for achieving the salivary secretion. In the secretory process, $Ca^{2+}$ activates $K^+$ and $Cl^-$ channels to transport water and electrolyte constituting whole saliva. We also focus on the $Ca^{2+}$ signals from intracellular stores with discussion about detailed molecular mechanism underlying the generation of characteristic $Ca^{2+}$ patterns. In particular, inositol triphosphate signal is a main trigger for inducing $Ca^{2+}$ signals required for the salivary gland functions. The biphasic response of inositol triphosphate receptor and $Ca^{2+}$ pumps generate a self-limiting pattern of $Ca^{2+}$ efflux, resulting in $Ca^{2+}$ oscillations. The regenerative $Ca^{2+}$ oscillations have been detected in salivary gland cells, but the exact mechanism and function of the signals need to be elucidated. In future, we expect that further investigations will be performed toward better understanding of the spatiotemporal role of $Ca^{2+}$ signals in regulating salivary secretion.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.46-50
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• 2001

Voice is one of the most efficient communication media and it includes several kinds of factors about speaker, context emotion and so on. Human emotion is expressed in the speech, the gesture, the physiological phenomena (the breath, the beating of the pulse, etc). In this paper, the method to have cognizance of emotion from anyone's voice signals is presented and simulated by using neuro-fuzzy model.