• Journal of Communications and Networks
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• v.13 no.2
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• pp.125-131
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• 2011

Rapid strides are being made to bring to reality the technology of wearable sensors for monitoring patients' physiological data.We study the problem of automatically detecting anomalies in themeasured blood glucose levels. The normal daily measurements of the patient are used to train a hidden Markov model (HMM). The structure of the HMM-its states and output symbols-are selected to accurately model the typical transitions in blood glucose levels throughout a 24-hour period. The learning of the HMM is done using historic data of normal measurements. The HMM can then be used to detect anomalies in blood glucose levels being measured, if the inferred likelihood of the observed data is low in the world described by the HMM. Our simulation results show that our technique is accurate in detecting anomalies in glucose levels and is robust (i.e., no false positives) in the presence of reasonable changes in the patient's daily routine.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1247-1248
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• 2022

Due to rapid urbanization and population growth, it has become crucial to analyze the various volumes and characteristics of pedestrian pathways to understand the capacity and level of service (LOS) for pathways to promote a better walking environment. Different indicators have been developed to measure pedestrian volume. The pedestrian level of service (PLOS), tailored to analyze pedestrian pathways based on the concept of the LOS in transportation in the Highway Capacity Manual, has been widely used. PLOS is a measurement concept used to assess the quality of pedestrian facilities, from grade A (best condition) to grade F (worst condition), based on the flow rate, average speed, occupied space, and other parameters. Since the original PLOS approach has been criticized for producing idealistic results, several modified versions of PLOS have also been developed. One of these modified versions is perceived PLOS, which measures the LOS for pathways by considering pedestrians' awareness levels. However, this method relies on survey-based measurements, making it difficult to continuously deploy the technique to all the pathways. To measure PLOS more quantitatively and continuously, researchers have adopted computer vision technologies to automatically assess pedestrian flows and PLOS from CCTV videos. However, there are drawbacks even with this method because CCTVs cannot be installed everywhere, e.g., in alleyways. Recently, a technique to monitor bio-signals, such as electrodermal activity (EDA), through wearable sensors that can measure physiological responses to external stimuli (e.g., when another pedestrian passes), has gained popularity. It has the potential to continuously measure perceived PLOS. In their previous experiment, the authors of this study found that there were many significant EDA responses in crowded places when other pedestrians acting as external stimuli passed by. Therefore, we hypothesized that the EDA responses would be significantly higher in places where relatively more dynamic objects pass, i.e., in crowded areas with low PLOS levels (e.g., level F). To this end, the authors conducted an experiment to confirm the validity of EDA in inferring the perceived PLOS. The EDA of the subjects was measured and analyzed while watching both the real-world and virtually created videos with different pedestrian volumes in a laboratory environment. The results showed the possibility of inferring the amount of pedestrian volume on the pathways by measuring the physiological reactions of pedestrians. Through further validation, the research outcome is expected to be used for EDA-based continuous measurement of perceived PLOS at the alley level, which will facilitate modifying the existing walking environments, e.g., constructing pathways with appropriate effective width based on pedestrian volume. Future research will examine the validity of the integrated use of EDA and acceleration signals to increase the accuracy of inferring the perceived PLOS by capturing both physiological and behavioral reactions when walking in a crowded area.

• Journal of Sensor Science and Technology
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• v.17 no.2
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• pp.133-142
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• 2008

Respiration is induced by muscular contraction of the chest and abdomen, resulting in the abdominal volume change. Thus, continuous measurement of the abdominal dimension enables to monitor breathing activity. Conductive rubber cord has been previously introduced and tested to develop wearable application for respiratory measurements. The present study implemented wireless wearable respiratory monitoring system with the conductive rubber cord in the patient's pants. Signal extraction circuitry was developed to obtain the abdominal circumference changes reflecting the lung volume variation caused by respiratory activity. Wireless transmission was followed based on the zigbee communication protocol in a size of 65mm${\times}$105mm easily put in pocket. Successful wireless monitoring of respiration was performed in that breathing frequency was accurately estimated as well as different breathing patterns were easily recognized from the abdominal signal. $CO_2$ inhalation experiment was additionally performed in purpose of quantitative estimation of tidal volume. Air mixed with $0{\sim}5%\;CO_2$was inhaled by 4 normal males and the respiratory air flow rate, abdominal dimension change, and end tidal $CO_2$ concentration were simultaneously measured in steady state. $CO_2$ inhalation increased the tidal volume in normal physiological state with a correlation coefficient of 0.90 between the tidal volume and the end tidal $CO_2$ concentration. The tidal volume estimated from the abdominal signal linearly correlated with the accurate tidal volume measured by pneumotachometer with a correlation coefficient of 0.88 with mean relative error of approximately 8%. Therefore, the tidal volume was accurately estimated by measuring the abdominal dimension change.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.352-356
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• 2023

The integration of bioelectronic devices with the skin is a promising strategy for personalized healthcare monitoring and diagnostics. On-skin bioelectrodes hold great potential for the real-time tracking of physiological parameters. However, persistent challenges of stability and reliability have instigated exploration beyond conventional noble metals. This study focuses on the ionic passivation and oxidation dynamics of copper-based on-skin thin-film bioelectrodes. Through parylene chemical vapor deposition, we harness a controlled thin film of parylene insulation to counter the intrinsic susceptibility of copper to oxidation in the ionic environment. The results represent the relationship among the parylene insulation thickness, copper oxidation, and electrode impedance over temporal intervals. Comparative analyses indicate that the short-term stability of the copper electrode is comparable to that of the gold electrode. Therefore, we propose a cost-effective strategy for fabricating copper-based on-skin bioelectrodes by introducing enhanced ionic stability within a discernible operational timeframe. This study enriches our understanding of on-skin bioelectronics and affordable material choices for practical use in wearable healthcare devices.

• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.49-56
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• 2021

In this study, we investigated the necessary mechanical properties of conductive multifilament yarns for fabricating the electrodes of biosignal measurement pressure and stretch textile sensors using embroidery. When electrodes and circuits for smart wearable products are produced through the embroidery process using conductive multifilament yarns, unnecessary material loss is minimized, and complex electrode shapes or circuit designs can be produced without additional processes using a computer embroidering machine. However, because ordinary missionary threads cannot overcome the stress in the embroidery process and yarn cutting occurs, herein, we analyzed the S-S curve, thickness, and twist structure, which are three types of silver-coated multifilament yarns, and measured the stress in the thread of the embroidery simultaneously. Thus, the required mechanical properties of the yarns in the embroidery process were analyzed. In the actual sample production, cutting occurred in silver-coated multifilament rather than silver-coated polyamide/polyester, which showed the lowest S-S curve. In the embroidery process, the twist was unwound through repetitive vertical movement. Further, we fabricated a piezoresistive pressure/tension sensor to measure gauge factor, which is an index for measuring biological signals. We confirmed that the sensor can be applied to the fabrication of embroidery electrodes, which is an important process in the mass production of smart wearable products.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.