• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.104-109
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• 2017

We propose a new machine learning algorithm which analyzes daily activity patterns of users from multi-modal wearable sensor data. The proposed model learns and extracts activity patterns using input from wearable devices in real-time. Inspired by cue integration of human's property, we constructed gated multi-modal neural networks which integrate wearable sensor input data selectively by using gate modules. For the experiments, sensory data were collected by using multiple wearable devices in restaurant situations. As an experimental result, we first show that the proposed model performs well in terms of prediction accuracy. Then, the possibility to construct a knowledge schema automatically by analyzing the activation patterns in the middle layer of our proposed model is explained.

• 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.

• Physical Therapy Rehabilitation Science
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• v.8 no.3
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• pp.146-151
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• 2019

Objective: The purpose of this study was to investigate the test-retest reliability and concurrent validity of the joint angle of the lower extremities during sit-to-stand movements with wearable sensors based on a portable gait analysis system (PGAS), and the results were compared with a analysis system (MAS) to predict the clinical potential of it. Design: Cross-sectional study. Methods: Sixteen persons with stroke (9 males, 7 females) participated in this study. All subjects had the MAS and designed PGS applied simultaneously and eight sensor units of designed PGAS were placed in a position to avoid overlap with the reflexive markers from MAS. The initial position of the subjects was 90º of hip, knee, and ankle joint flexion while sitting on a chair that was armless and backless. The height of the chair was adjusted to each individual. After each trial, the test administrator checked the quality of data from both systems that measured sit-to-stand for test-retest reliability and concurrent validity. Results: As a result, wearable sensor based designed PGAS and MAS demonstrated reasonable test-retest reliability for the assessment of joint angle in the lower extremities during sit-to-stand performance. The intra-class correlation coefficients (ICCs) for wearable sensor based designed PGAS showed an acceptable test-retest reliability, with ICCs ranging from 0.759 to 0.959. In contrast, the MAS showed good to excellent test-retest reliability, with ICCS ranging from 0.811 to 0.950. In concurrent validity, a significant positive relationship was observed between PGAS and MAS for variation of joint angle during sit-to-stand movements (p<0.01). A moderate to high relationship was found in the affected hip (r=0.665), unaffected hip (r=0.767), affected knee (r=0.876), unaffected knee (r=0.886), affected ankle (r=0.943) and unaffected ankle (r=0.823) respectively. Conclusions: The results of this study indicated that wearable sensor based designed PGAS showed acceptable test-retest reliability and concurrent validity in persons with stroke for sit-to-stand movements and wearable sensors based on developed PGAS may be a useful tool for clinical assessment of functional movement.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2138-2142
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• 2005

A wearable sensor system is proposed as a man-machine interface to control a device for walking assistance. The sensor system is composed of small sensors to detect the information about the user's body motion such as the activity level of skeletal muscles and the acceleration of each body parts. Each sensor includes a microcomputer and all the sensors are connected into a network by using the serial communication function of the microcomputer. The whole network is integrated into a belt made of soft fabric, thus, users can put on/off very easily. The sensor system is very reliable because of its decentralized network configuration. The body information obtained from the sensor system is used for controlling the assisting device to achieve a comfortable and an effective walking training.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1732-1739
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• 2018

With the explosion of digital devices, interaction technologies between human and devices are required more than ever. Especially, hand gesture recognition is advantageous in that it can be easily used. It is divided into the two groups: the contact sensor and the non-contact sensor. Compared with non-contact gesture recognition, the advantage of contact gesture recognition is that it is able to classify gestures that disappear from the sensor's sight. Also, since there is direct contacted with the user, relatively accurate information can be acquired. Electromyography (EMG) and force-sensitive resistors (FSRs) are the typical methods used for contact gesture recognition based on muscle activities. The sensors, however, are generally too sensitive to environmental disturbances such as electrical noises, electromagnetic signals and so on. In this paper, we propose a novel contact gesture recognition method based on Flexible Epidermal Tactile Sensor Array (FETSA) that is used to measure electrical signals according to movements of the wrist. To recognize gestures using FETSA, we extracted feature sets, and the gestures were subsequently classified using the support vector machine. The performance of the proposed gesture recognition method is very promising in comparison with two previous non-contact and contact gesture recognition studies.

• Journal of IKEEE
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• v.23 no.3
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• pp.793-799
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• 2019

In this study, the diffusion process and the thermal energy distribution gradient of the sensor were confirmed by using the finite element analysis program (COMSOL) of the mesh method to analyze the thermal diffusion in the wearable fabric (Nylon) + MWCNT gas sensor. To analyze the diffusion process of thermal energy, the structure of the gas sensor was modeled in a two dimension plane. The proposed modeling was presented with the characteristic value for the component of the sensor, and the gas sensor designed using the mesh finite element method (FEM) was proposed and analyzed by suggesting the one-way partial differential equation in the governing equation to know the degree of thermal energy diffusion and the thermal energy gradient. In addition, the temperature gradient 10[K/mm] of the anode-cathode electrode layer and the gas detection unit was investigated by suggesting the heat velocity transfer equation.

• Journal of Fashion Business
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• v.25 no.5
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• pp.99-113
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• 2021

This study aimed to produce sensors for measuring thigh motor skills. A textile stretch sensor was manufactured using a CNT(Carbon Nano Tube) 0.1 wt% water SWCNT(Single-Walled Carbon Nano Tube) solution, and different designs were applied to increase the sensitivity of the sensor, and different GF(Gauge Factor) values were compared using UTM devices. The same design was applied to fabrics and weaves to observe changes in performance according to fibrous tissue, and the suitability of sensors was determined based on tensile strength, elongation, and the elongation recovery rate. Sensitivity was found to vary depending upon the design. Thus the manufactured sensor was attached to a pair of fitness pants as a prototype, divided into lunge position and squat position testing, and the stretch sensor was used to measure thigh movements. It was shown that stretch sensors used to measure thigh motor skills should have light and flexible features and that elongation recovery rates and tensile strength should be considered together. The manufactured stretch sensor may be applicable to various sports fields that use lower limb muscles, wearable healthcare products, and medical products for measuring athletic ability.

• Journal of Biomedical Engineering Research
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• v.37 no.3
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• pp.112-118
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• 2016

The neck pain is fairly common occurance. Forward head posture and text neck are poor postures which may be related with neck pain but the evidence is not enough. We developed the wearable sensor which can assess the head & neck posture in daily life. Microprocessor, Bluetooth low energy, and 3-axis accelerometer, rechargeable battery and vibratior for reminding are used to implement the wearable sensor. Real-time algorithm to parameterize the posture for one epoch is implemented which classifies the posture in the epoch into three classed; dynamic, static_good posture, and static_poor posture. Also the algorithm makes reminding to its wearer to give them the prolonged poor posture is detected. The mean error of measurement was 1.2 degree. The correlation coefficient between neck angle and craniovertebral angle was 0.9 or higher in all cases. With the pilot study on text neck syndrome was also quatified. Average of neck angle were 74.3 degree during the listening in the classroom and 57.8 degree during the smartphoning. Using the wearable sensor suggested, the poor postures of forward head posture and neck neck can be detected in real-time which can remind the wearer according to his/her setting.

• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.47-51
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• 2017

The purpose of this study was to investigate the effect of wearable sensor wear on the muscle contraction of cervical erector spinae and upper trapezius causing the forward head posture induction in order to reduce the stress induced by the use of smartphone. This study was to investigate the muscle activity of healthy adults in the 20th to 30th generations by dividing them into the control group using the smartphone, the non-wearing group conscious the posture of the head posture, and the wearing group wearing the wearable sensor. There were no differences in muscle activity between cervical erector spinae and upper trapezius compared to the control, non - wearing, and wearing groups. In addition, the changes in muscle activity of cervical erector spinae muscles were increased in all groups, but the muscle activity of upper trapezius muscles were in the wear group compared to the non-wear group and the control group, but there was no statistical significance. That is, wear of the wearable sensor may be effective in controlling the conscious posture, but it may cause the compensation of another part.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.318-330
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• 2015

In wearable sensor systems (WSSs), sensor nodes are deployed around human body parts such as the arms, the legs, the stomach, and the back. These sensors have limited lifetimes because they are battery-operated. Thus, transmission power control (TPC) is needed to save the energy of sensor nodes. The TPC should control the transmission power level (TPL) of sensor nodes based on current channel conditions. However, previous TPC algorithms did not precisely estimate the channel conditions. Therefore, we propose a new TPC algorithm that uses an accelerometer to directly measure the current channel condition. Based on the directly measured channel condition, the proposed algorithm adaptively adjusts the transmission interval of control packets for updating TPL. The proposed algorithm is efficient because the power consumption of the accelerometer is much lower than that of control packet transmissions. To evaluate the effectiveness of our approach, we implemented the proposed algorithm in real sensor devices and compared its performance against diverse TPC algorithms. Through the experimental results, we proved that the proposed TPC algorithm outperformed other TPC algorithms in all channel environments.