• Physical Therapy Korea
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• v.16 no.4
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• pp.8-15
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• 2009

Grip strength is an objective indicator for evaluating the functional movement of upper extremities. Therapists have been using it for a long time as an excellent barometer for evaluating the therapy process, therapeutic effects and prognosis of patients with injuries in upper extremities. This study investigated the effects of extensor pattern position and elastic taping of non-dominant hand on the grip strength of dominant hand among general adults. The subjects of this study were 23 males and 7 females from physical therapy departments of 3 Universities located in Busan who agreed to participate in the experiment and the resultant data were analyzed using SPSS version 12.0. The results of the study were as follows. First, there was a significant difference between the grip strength of dominant hand when the non-dominant hand was at the neutral position and that when the non-dominant hand was at the extensor pattern position and both hands were at the maximum strength simultaneously (Bonferroni-corrected p<.001). Second, there was a significant difference between the grip strength of dominant hand when the non-dominant hand was at the neutral position and that when the elastic taping of non-dominant hand was applied (Bonferroni-corrected p<.001). Third, there was no significant difference between the grip strength of dominant hand when the non-dominant hand was at the extensor pattern position and both hands were at the maximum strength simultaneously and that when the elastic taping of non-dominant hand was applied. The irradiation effects through the extensor pattern position of non-dominant hand and application of the elastic taping to non-dominant hand showed significant results in improving the maximum grip strength of dominant hand. This finding could be suggested as the probability for the indirect treatment of the upper extremities of hemiplegia and orthopedic patients due to the long-term fixing of upper extremities.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.4
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• pp.46-53
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• 2002

Push-pull strength has been found to be associated with various work factors such as height, distance, repetition, duration, posture and individual factors. Therefore, this study was performed to investigate the effect of various work factors on push-pull strength and muscle recruitment pattern. Work factors were consisted of grip height(elbow, shoulder), grip distance(100%, 50%, and 25% of maximum grip distance) and shoulder angle(neutral($90^{\cire}$), and abduction($45^{\cire}$, $0^{\cire}$)) during sitting work. The results showed that the normalized strength and EMG value were higher at the elbow height than the shoulder height, and increased with grip distance and shoulder adduction. The results of ANOVA showed that there was significant difference on muscle recruitment patterns among the task conditions. Therefore, it is necessary to consider work factors as well as strength to prevent workers from work-related injuries.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.30-36
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• 2016

Recently, digital doorlock systems have been employing biometric recognition and smartphone as personal authentification means. The performance of a digital doorlock system is determined by the two conflicting indices such as security and convenience. This paper proposes and implements one grip based doorpull shaped doorlock systems using fingerprint and touch sensor grip pattern. The proposed system sequentially performs fingerprint recognition and grip pattern identification when a user grips the doorpull in order to open the door. This method so called 'One Grip' is considered to enhance security while maintaining users' convenience. We expect the proposed method can solve the phone missing problem encountered in developing smart doorlock systems based on smartphones.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.245-248
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• 2005

This paper presents a novel user interface system which aims at easy controlling of mobile devices. The fundamental concept of the proposed interface is to launch an appropriate function of the device by sensing and recognizing the grip-pattern when the user tries to use the mobile device. To this end, we develop a prototype system which employs capacitive touch sensors covering the housing of the system and a recognition algorithm for offering the appropriate function which suitable for the sensed grip-pattern. The effectiveness and feasibility of the proposed method is evaluated through the test of recognition rate with the collected grip-pattern database.

• Korean Journal of Applied Biomechanics
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• v.17 no.3
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• pp.125-131
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• 2007

The purpose of this study was to evaluate the difference in grip force and angular kinematic variables between elite (handicap${\leq}2$) and novice golfers. Three-dimensional motion analysis system with synchronized grip force measurement system was used. The participants consisted of two groups based on their playing ability: 10 elite golfers and 10 novice golfers. Each subject performed 5 putting strokes at the distance of 1, 3, and 5m with randomly selected order. During entire putting phase, elite group showed relatively constant grip force but novice group showed continuously increasing grip force pattern. There existed a clear difference in the trajectory of shoulder line between two groups. As for novice group the rotational center did not converge into one point, for elite group the rotational center converged into precise single point. And there was a clear difference pattern in anterior-posterior directional movement at shoulder between two groups. These difference might be helpful for improving consistent putting skills.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.29-34
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• 2015

Brain-computer interface (BCI) is being studied for convenient life in various application fields. The purpose of this study is to investigate a changing electroencephalography (EEG) for precise motion of a robot or an artificial arm. Three subjects who participated in this experiment performed three-task: Grip, Move, Relax. Acquired EEG data was extracted feature data using two feature extraction algorithm (power spectrum analysis and multi-common spatial pattern). Support vector machine (SVM) were applied the extracted feature data for classification. The classification accuracy was the highest at Grip class of two subjects. The results of this research are expected to be useful for patients required prosthetic limb using EEG.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.304-313
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• 2006

This paper describes a novel way of applying capacitive sensing technology to a mobile user interface. The key idea is to use grip-pattern, which is naturally produced when a user tries to use the mobile device, as a clue to determine an application to be launched. To this end, a capacitive touch sensing system is carefully designed and installed underneath the housing of the mobile device to capture the information of the user's grip-pattern. The captured data is then recognized by dedicated recognition algorithms. The feasibility of the proposed user interface system is thoroughly evaluated with various recognition tests.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.277-282
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• 2015

Braincomputer interface (BCI) technology is making advances in the field of humancomputer interaction (HCI). To improve the BCI technology, we study the changes in the electroencephalogram (EEG) signals for six levels of grip strength: 10%, 20%, 40%, 50%, 70%, and 80% of the maximum voluntary contraction (MVC). The measured EEG data are categorized into three classes: Weak, Medium, and Strong. Features are then extracted using power spectrum analysis and multiclass-common spatial pattern (multiclass-CSP). Feature datasets are classified using a support vector machine (SVM). The accuracy rate is higher for the Strong class than the other classes.

• Physical Therapy Korea
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• v.20 no.2
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• pp.28-37
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• 2013

This study examined differences in the activity of upper limb muscles according to how an ultrasound head is gripped. Twenty-two adult males were participated in the study. Each participant was asked to apply ultrasound treatment on to a lump of pork meat by two different ultrasound head grip patterns: spherical and cylindrical grips. Muscle activity was measured in the extensor carpi radialis longus (ECRL), flexor carpi ulnaris (FCU), and pronator teres (PT), triceps brachii (TB), middle deltoid (MD), and upper trapezius (UT) muscles. There were no significant differences in the EMG signals of any muscle according to the ultrasound head grip pattern (p>.05). There were significant differences in the EMG signal of each type of muscle (p<.05). The EMG signal of UT was the lowest and that of TB was lower than ECRL and FCU. There were interactions between ECRL and FCU, between ECRL and PT, between FCU and ECRL, and between FCU and MD. The EMG signal of ECRL using the cylindrical head was low and that of FCU with the cylindrical head was high, while the opposite was the case with the spherical head ($p_{adj}$ <.05/15). The results of this study indicate that the wrist muscles worked actively when the participants applied ultrasound therapy using both spherical and cylindrical heads. A spherical head might induce imbalanced muscle activity among the wrist muscles, leading to deviation of the wrist joint. Therefore, the cylindrical head is recommended for ultrasound therapy because it produced a constant, repeated force.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.678-683
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• 2006

A novel and intuitive way of accessing applications of mobile devices is presented. The key idea is to use grip-pattern, which is naturally produced when a user tries to use the mobile device, as a clue to determine an application to be launched. To this end, a capacitive touch sensor system is carefully designed and installed underneath the housing of the mobile terminal to capture the image of the user's grip-pattern. The captured data is then recognized by a recognizer with dedicated preprocessing and postprocessing algorithms. The recognition test is performed to validate the feasibility of the proposed user interface system.