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

• Journal of the Korean Society for Precision Engineering
• /
• v.17 no.7
• /
• pp.209-216
• /
• 2000

Recently, Wearable Computers have been applied to medical equipments, inspection system, military and various fields of industries. To support the various application of wearable computer, many researches into the input device for wearable computer have been executed. This paper describes the glove-like hand input system for wearable computer. the characteristics of sensed values, and coupling effects between each sensor. Using these characteristics and coupling effects, the general relation between flexion angles of joints and the values from sensors are proposed as exponential functions. Also, the error range of sensed values is proposed and the glove-like hand input system is calibrated as well by the experiments.

• Journal of Biomedical Engineering Research
• /
• v.28 no.2
• /
• pp.280-286
• /
• 2007

The wearable sensor glove was developed using EDA sensors and conducting fabric. EDA(Electro-dermal Activity) signal is an electric response of human skin. There are SIL(Skin Impedance Level) and SIR(Skin Impedance Response) in EDA. SIL consists mostly of a DC component while SIR consists of an AC component. The relationship between drowsiness and the EDA signal is utilized. EDA sensors were made using a conducting fabric instead of AgCl electrodes, for a more suitable, more wearable device. The EDA signal acquisition module was made by connecting the EDA sensor gloves through conductive fabric lines. Also, the EDA signal acquisition module can be connected to a PC that shows the results of the EDA signal processing analysis and gives proper feedback to the user. This system can be used in various applications to detect drowsiness and prevent accidents from drowsiness for automobile drivers.

• The Journal of Korea Robotics Society
• /
• v.15 no.3
• /
• pp.240-247
• /
• 2020

This paper presents a novel knitted data glove system for pattern classification of hand posture. Several experiments were conducted to confirm the performance of the knitted data glove. To find better sensor materials, the knitted data glove was fabricated with stainless-steel yarn and silver-plated yarn as representative conductive yarns, respectively. The result showed that the signal of the knitted data glove made of silver-plated yarn was more stable than that of stainless-steel yarn according as the measurement distance becomes longer. Also, the pattern classification was conducted for the performance verification of the data glove knitted using the silver-plated yarn. The average classification reached at 100% except for the pointing finger posture, and the overall classification accuracy of the knitted data glove was 98.3%. With these results, we expect that the knitted data glove is applied to various robot fields including the human-machine interface.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.741-744
• /
• 2009

In this paper, We introduce the glove mouse using a Gyroscope, acceleration sensor, Pin-type Viboratctile Display Device and USB HID. The device recognize a user's wrist by Gyroscope and acceleration sensor in the glove and transmit the data to USB dongle which is recognized the manufactured mouse by Blutooth. Also, using a special application, We transmit the tactile information to user through the Pin-type Vibrotactile Display. We implement wearable system in the glove except USB device. If user want to use general spatial mouse, we recognize mouse USB dongle only without another application. If user want to feel the tactile sensationn, we can use by connecting PC serial communication port to USB dongle.

• 전자공학회논문지 IE
• /
• v.46 no.3
• /
• pp.55-61
• /
• 2009

This paper describes an assistant system for elders who live alone. The developed system is composed of a wearable RFID system, a gateway system, and server system. The wearable RFID system is installed in glove. The wearable RFID system can be considered as a wireless sensor network which has a sink node and sensor node with a RFID reader. The sensor node can read RFID tags on the various objects used in daily living such as furniture, medicines, sugar and salt bottles, and ok. The sensor node transmits wireless packets to the sink node. The sink node sends the received packet immediately to a server system via a gateway system. The gateway provides users with audio-visual information of objects. The server system is composed of a database server and a web server. The data from each wearable RFID system is collected into a database, and then the data are processed to visualize the measurement of daily living activities of users. The processed data can be provided for someone who wants to know about user's daily living patterns in house such as family, caregivers, and medical crew.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.10
• /
• pp.1014-1020
• /
• 2011

This presents a motion and force estimation system of human fingers by using an Electromyography (EMG) sensor module and a data glove system to be proposed in this paper. Both EMG sensor module and data glove system are developed in such a way to minimize the number of hardware filters in acquiring the signals as well as to reduce their sizes for the wearable. Since the onset of EMG precedes the onset of actual finger movement by dozens to hundreds milliseconds, we show that it is possible to predict the pattern of finger movement before actual movement by using the suggested system. Also, we are to suggest how to estimate the grasping force of hand based on the relationship between RMS taken EMG signal and the applied load. Finally we show the effectiveness of the suggested estimation system through several experiments.

• PNF and Movement
• /
• v.21 no.2
• /
• pp.171-183
• /
• 2023

Purpose: The purpose of this study was to compare universal goniometry (UG), which is commonly used in clinical practice to measure the range of motion (ROM) of finger joints with a wearable soft sensor glove, and to analyze the reliability to determine its usefulness. Methods: Ten healthy adults (6 males, 4 females) participated in this study. The metacarpophalangeal joint (MCP), interphalangeal joint (IP), and proximal interphalangeal joint (PIP) of both hands were measured using UG and Mollisen HAND soft sensor gloves during active flexion, according to the American Society for Hand Therapists' measurement criteria. Measurements were taken in triplicate and averaged. The mean and standard deviation of the two methods were calculated, and the 95% limits of agreement (LOA) of the measurements were calculated using the intraclass correlation coefficient (ICC) and Bland-Altman plot to examine the reliability and discrepancies between the measurements. Results: The results of the mean values of the flexion angles for the active range of motion (AROM) of the finger joints showed large angular differences in the finger joints, except for the MCP of the thumb. In the inter-rater reliability analysis according to the measurement method, the ICC (2, 1) value showed a low level close to 0, and the mean difference by the Bland-Altman plot showed a value greater than 0, showing a pattern of discrepancy. The 95% LOA had a wide range of differences. Conclusion: This study is a preliminary study investigating the usefulness of the soft sensor glove, and the reliability analysis showed a low level of reliability and inconsistency. However, if future studies can overcome the limitations of this study and the technical problems of the soft sensor glove in the development stage, it is suggested that the measurement instrument can show more accurate measurement and higher reliability when measuring ROM with UG.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.2
• /
• pp.379-386
• /
• 2023

When patients suffer from finger injuries, their finger joints can become stiff and inflexible due to decreased ability to exercise the finger tendons. This can lead to a loss of strength and difficulty using their hands. To address this, it is important to provide patients with consistent rehabilitation treatment that can help restore finger flexibility and strength simultaneously. In this study, we propose wearable gloves that use FSRs (force sensitive resistors) for finger strength training. The glove is designed to be adjustable using rubber bands and a custom PCB is designed for signal acquisition. For the evaluation of finger strength training, the result was analyzed in four cases. We suggest a vector that represents the center of five finger forces, and the result shows that the vector can indicate the level of force balance.

• Journal of Magnetics
• /
• v.22 no.1
• /
• pp.150-154
• /
• 2017

A novel high precision spatial positioning method utilizing the electromagnetic momentum, i.e., Electromagnetic Momentum Positioning (EMP), is proposed in this paper. By measuring the momentum of the electromagnetic field around the small current loop, the relative position between the sensor and the current loop is calculated. This method is particularly suitable for the application of close-range and high-precision positioning, e.g., data gloves and medical devices in personal healthcare, etc. The simulation results show that EMP method can give a high accuracy with the positioning error less than 1 mm, which is better than the traditional magnetic positioning devices with the error greater than 1 cm. This method lays the foundation for the application of data gloves to meet the accurate positioning requirement, such as the high precision interaction in Virtual Reality (VR), Augmented Reality (AR) and personal wearable devices network.