• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3423-3438
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• 2014

In recent years, wearable sensor devices are reshaping the way people live, work, and play. A wearable sensor device is a computer that is subsumed into the personal space of the user, and is always on, and always accessible. Therefore, among the most salient aspects of a wearable sensor device should be a small form factor, long battery lifetime, and real-time characteristics. Thereby, sophisticated applications of a wearable sensor device use real-time operating systems to guarantee real-time deadlines. The deterministic multi-dimensional task scheduling algorithms are implemented on ARC (Actual Remote Control) with relatively limited hardware resources. ARC is a wearable wristwatch-type remote controller; it can also serve as a universal remote control, for various wearable sensor devices. In the proposed algorithms, there is no limit on the maximum number of task priorities, and the memory requirement can be dramatically reduced. Furthermore, regardless of the number of tasks, the complexity of the time and space of the proposed algorithms is O(1). A valuable contribution of this work is to guarantee real-time deadlines for wearable sensor devices.

• Journal of Sensor Science and Technology
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• v.26 no.5
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• pp.301-305
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• 2017

This paper presents a wearable human health-monitoring band. The band consists of a body temperature detector (BTD) and a hear rate detector (HRD). The BTD and HRD are realized using an inkjet-printed flexible temperature sensor and a commercial heart rate sensor module, respectively. The sensitivity of the fabricated BTD was found to be $-31/^{\circ}C$ with a linearity of 99.82%. The HRD using the commercial heart rate sensor module has a good performance with a standard deviation of 0.85 between the data of a commercial smart watch and the fabricated HRD.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.9-13
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• 2019

Recently, as the sleep disorder problem of modern people deepens, the interest towards quality of sleep is increasing. To increase the quality of modern people's sleep. This paper has suggested an LED lighting control system according to the sleep stage using PPG sensors of wearable devices. The pulse of the wrist radial artery was measured using a wearable device mounted with PPG sensor, which enables heart rate-measuring, and by using the point that heart rate lowers during stable sleep than non-sleeping, the LED lighting of indoors was controlled, which is the disturbing element when sleeping. For the performance evaluation, a 10-Fold cross analysis was conducted for performance evaluation, and a result of an average accuracy 87.02% was obtained as a result. Therefore, the LED lighting control system according to the sleep stage using a wearable device of this paper is expected to contribute to raise the quality of the user's life.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.154-160
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• 2019

In this paper, we propose the miniaturization size of wearable Range of Motion(ROM) and a system that can be connected with smart devices in real-time to measure the joint movement range dynamically. Currently, the ROM of the joint is directly measured by a person using a goniometer. Conventional methods are different depending on the measurement method and location of the measurement person, which makes it difficult to measure consistently and may cause errors. Also, it is impossible to measure the ROM of joints in real-life situations. Therefore, the wearable sensor is attached to the joint to be measured to develop a miniaturize size ROM device that can measure the range of motion of the joint in real-time. The sensor measured the resistance value changed according to the movement of the joint using a load cell. Also, the sensed analog values were converted to digital values using an Analog to Digital Converter(ADC). The converted amount can be transmitted wireless to the smart device through the wearable sensor node. As a result, the developed device can be measured more consistently than the measurement using the goniometer, communication with IoT-based smart devices, and wearable enables dynamic observation. The developed wearable sensor node will be able to monitor the dynamic state of rehabilitation patients in real-time and improve the rapid change of treatment method and customized treatment.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.71-78
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• 2022

Wearable devices have the potential to revolutionize future medical diagnostics and personal healthcare. The integration of biosensors into scalable form factors allow continuous and noninvasive monitoring of key biomarkers and various physiological indicators. However, conventional wearable devices have critical limitations owing to their rigid and obtrusive interfaces. Recent developments in functional biocompatible materials, micro/nanofabrication methods, multimodal sensor mechanisms, and device integration technologies have provided the foundation for novel skin-interfaced bioelectronics for advanced and user-friendly wearable devices. Nonetheless, it is a great challenge to satisfy a wide range of design parameters in fabricating an authentic skin-interfaced device while maintaining its edge over conventional devices. This review highlights recent advances in skin-compatible materials, biosensor performance, and energy-harvesting methods that shed light on the future of wearable devices for digital health and personalized medicine.

• Journal of Fashion Business
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• v.18 no.4
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• pp.1-14
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• 2014

Currently smart textile market is rapidly expanding and the demand is increasing integration of an electronic fiber circuit. The garments are an attractive platform for wearable device. This is one of the integration techniques, which consists of is the selective introduction of conductive yarns into the fabric through knitting, weaving or embroidering. The aim of this work is to develop a golf bend driven prototype design for an attachable Arduino that can be used to assess elbow motion. The process begins with the development of a wearable device technique that uses conductive yarn and flex sensor for measurement of elbow bending movements. Also this paper describes and discusses resistance value of zigzag embroidery of the conductive yarns on the tensile properties of the fabrics. Furthermore, by forming a circuit using an Arduino and flex sensor the prototype was created with an assistance band for golf posture training. This study provides valuable information to those interested in the future directions of the smart fashion industry.

• PNF and Movement
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• v.18 no.2
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• pp.287-296
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• 2020

Purpose: The study aims were to develop a wearable inertial sensor-based gait analysis device that uses machine learning algorithms, and to validate this novel device using temporal gait parameters. Methods: Thirty-four healthy young participants (22 male, 12 female, aged 25.76 years) with no musculoskeletal disorders were asked to walk at three different speeds. As they walked, data were simultaneously collected by a motion capture system and inertial measurement units (Reseed®). The data were sent to a machine learning algorithm adapted to the wearable inertial sensor-based gait analysis device. The validity of the newly developed instrument was assessed by comparing it to data from the motion capture system. Results: At normal speeds, intra-class correlation coefficients (ICC) for the temporal gait parameters were excellent (ICC [2, 1], 0.99~0.99), and coefficient of variation (CV) error values were insignificant for all gait parameters (0.31~1.08%). At slow speeds, ICCs for the temporal gait parameters were excellent (ICC [2, 1], 0.98~0.99), and CV error values were very small for all gait parameters (0.33~1.24%). At the fastest speeds, ICCs for temporal gait parameters were excellent (ICC [2, 1], 0.86~0.99) but less impressive than for the other speeds. CV error values were small for all gait parameters (0.17~5.58%). Conclusion: These results confirm that both the wearable inertial sensor-based gait analysis device and the machine learning algorithms have strong concurrent validity for temporal variables. On that basis, this novel wearable device is likely to prove useful for establishing temporal gait parameters while assessing gait.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.18-23
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• 2021

There has been demand for many magnetic sensor applications, and to develop low-cost devices, it is critical to accurately understand the behavior of the magnetic field and the characteristics of magnetic sensors and target devices during initial development phase. The magnetic field has been known to have very complicated nonlinear data to calculate, so it has required expensive computing machines or research to accurately calculate the magnetic sensor values. However, this paper introduces a characteristic of a magnetic sensor called the giant magnetoresistance (GMR) and proposes simple and sufficient approaches to develop a wearable joystick device using a magnetic sensor. Particularly, this paper introduces the design factors for how to properly develop a low-cost wearable joystick device using magnetic sensors after carefully considering the mechanism of a real joystick and the characteristics of magnetic sensors. As a result, user test results are provided to show how users can operate this new wearable joystick device.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.159-172
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• 2024

The number of healthcare products available for pets has increased in recent times, which has prompted active research into wearable devices for pets. However, the data collected through such devices are limited by outliers and missing values owing to the anomalous and irregular characteristics of pets. Hence, we propose pet behavior recognition based on a hybrid one-dimensional convolutional neural network (CNN) and long short- term memory (LSTM) model using pet wearable devices. An Arduino-based pet wearable device was first fabricated to collect data for behavior recognition, where gyroscope and accelerometer values were collected using the device. Then, data augmentation was performed after replacing any missing values and outliers via preprocessing. At this time, the behaviors were classified into five types. To prevent bias from specific actions in the data augmentation, the number of datasets was compared and balanced, and CNN-LSTM-based deep learning was performed. The five subdivided behaviors and overall performance were then evaluated, and the overall accuracy of behavior recognition was found to be about 88.76%.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.205-209
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• 2007

Today, there are research trends about the wearable sensor device that measures various bio-signals and provides healthcare services to user using e-Health technology. This study describes the wearable sensor glove using pulse-wave sensor, conducting fabric and embedded system. This wearable sensor glove is based on the pulse-wave measurement system which is able to measure the pulse wave signal in much use of oriental medicine on the basis of a research trend of e-Health system.