• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.749-755
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• 2011

Objective: The purpose of this study is to assess the reducing effect of workload on developed electro-motion scissors. Methods: To achieve this, we measured the pressure distribution, Joint angle of fingers and JSI(Job Strain Index) for electro-motion scissors and hand-operated scissor in objective assessment and surveyed the uncomfortable degree in subjective assessment. Results: As a result, The peak of pressure in the electro-motion scissors was generally lower than the hand-operated scissors. JSI and overall joint angle of fingers for the electro-motion scissors were remarkably lower than the hand-operated scissors. Also, the subjective uncomfortable degree showed that the uncomfortable point of electro-motion scissors were generally lower than the hand operated scissors. Conclusion: The impact of reducing the work load as well as distributing the pressure around the hand by using electro-motion scissors during grapes pinching was confirmed.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.517-521
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• 2008

Hand motion classification problem is considered as basis for sign or gesture recognition. We promote optical flow as main feature extracted from images sequences to simultaneously segment the motion's area by its magnitude and characterize the motion' s directions by its orientation. We manage the flow orientation histogram as motion descriptor. A motion is encoded by concatenating the flow orientation histogram from several frames. We utilize simple histogram matching to classify the motion sequences. Attempted experiments show the feasibility of our method for hand motion localization and classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1172-1178
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• 2018

A system that control computer presentations by using the hand motion recognition and identification is proposed. The system recognizes and identifies various types of motion in hand motion, controlls the presentation without additional control devices. To recognize hand movements, it performs a face and hand region detection. Facial area is detected using Haar classifier and hand region is extracted according to skin color information on HSV color model. The face area is used to determine the beginning and end of hand gestures, the size and direction of motion. It recognizes various hand gestures and uses them to control computer presentations according to the hand motion identification rules that are proposed and set horizontal and vertical axes from the face area. It is confirmed that 97.2% recognition rate is obtained in about 1200 hand motion recognition experiments and the proposed algorithm is valid in presentation control.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.62-69
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• 2008

A specialized anthropomorphic robot hand which can be attached to the biped humanoid robot MAHRU-R in KIST, has been developed. This built-in type hand consists of three fingers and a thumb with total four DOF(Degrees of Freedom) where the finger mechanism is well designed for grasping typical objects stably in human's daily activities such as sphere and cylinder shaped objects. The restriction of possible motions and the limitation of grasping objects arising from the reduction of DOF can be overcome by reflecting a typical human finger's motion profile to the design procedure. As a result, the developed hand can imitate not only human hand's shape but also its motion in a compact and efficient manner. Also this novel robot hand can perform various human hand gestures naturally and grasp normal objects with both power and precision grasping capability.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.229-237
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• 2022

Motion recognition is very useful for implementing an intuitive HMI (Human-Machine Interface). In particular, hands are the body parts that can move most precisely with relatively small portion of energy. Thus hand motion has been used as an efficient communication interface with other persons or machines. In this paper, we design and implement a light-weight ANN (Artificial Neural Network)-based hand motion recognition using a state-of-the-art flex sensor. The proposed design consists of data collection from a wearable flex sensor, preprocessing filters, and a light-weight NN (Neural Network) classifier. For verifying the performance and functionality of the proposed design, we implement it on a low-end embedded device. Finally, our experiments and prototype implementation demonstrate that the accuracy of the proposed hand motion recognition achieves up to 98.7%.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.263-266
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• 2007

We introduce a vision-based hand gesture recognition fer understanding musical time and patterns without extra special devices. We suggest a simple and reliable vision-based hand gesture recognition having two features First, the motion-direction code is proposed, which is a quantized code for motion directions. Second, the conducting feature point (CFP) where the point of sudden motion changes is also proposed. The proposed hand gesture recognition system extracts the human hand region by segmenting the depth information generated by stereo matching of image sequences. And then, it follows the motion of the center of the gravity(COG) of the extracted hand region and generates the gesture features such as CFP and the direction-code finally, we obtain the current timing pattern of beat and tempo of the playing music. The experimental results on the test data set show that the musical time pattern and tempo recognition rate is over 86.42% for the motion histogram matching, and 79.75% fer the CFP tracking only.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.30-37
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• 2011

This paper proposes a language set of hand motions for enhancing the performance of vision-based hand signal recognizer. Based on the statistical analysis of the angular tendency of hand movements in sign language and the hand motions in practical use, we construct four motion primitives as building blocks for basic hand motions. By combining these motion primitives, we design a discernable 'fundamental hand motion set' toward increasing the hand signal recognition. To demonstrate the validity of proposed designing method, we develop a 'fundamental hand motion set' recognizer based on hidden Markov model (HMM). The recognition system showed 99.01% recognition rate on the proposed language set. This result validates that the proposed language set enhances discernaility among the hand motions such that the performance of hand signal recognizer is improved.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.324-329
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• 2009

Hand Motion Modeling and Recognition (HMR) are a fundamental technology in the field of proactive computing for designing a human computer interaction system. In this paper, we present a 3D HMR system including data glove based on 3-axis accelerometer sensor and 3D Hand Modeling. Data glove as a device is capable of transmitting the motion signal to PC through wireless communication. We have implemented a 3D hand model using kinematic chain theory. We finally utilized the rule based algorithm to recognize hand gestures namely, scissor, rock and papers using the 3-D hand model.

• Journal of the Ergonomics Society of Korea
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• v.24 no.4
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• pp.1-5
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• 2005

A glove design which reflects the changes in hand surface by hand motion can reduce the undesirable effects of use of gloves on hand performance. The present study examined changes in hand length dimensions due to hand motion and identified significant factors affecting the length changes. Recruiting 120 males and females in their 20s and 30s having various hand lengths, this study measured 10 hand length dimensions, defined at 2 hand areas(phalangeal and metacarpal areas) for 5 digits, when the hand is stretched and in fist, and then calculated the percentage of length increase for each dimension. ANOVA and simple-effect analyses showed the length change percentages were mainly different depending on digit and hand area: 111-127% at the phalangeal area and 112-116% at the metacarpal area. The length change percentages of the index, middle, ring, and little fingers in the phalangeal area ascended in order and showed a high correlation(r = 0.94)with the ranges of motion of the fingers.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.109-116
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• 2011

In this paper, we introduce non-touch style interface system technology without any touch style controlling mechanism, which is called as "Air-interface". To develop this system, we used the full reflection principle of infrared (IR) light and then user's hand is separated from the background with the obtained image at every frame. The segmented hand region at every frame is used as input data for an hand-motion recognition module, and the hand-motion recognition module performs a suitable control event that has been mapped into the specified hand-motion through verifying the hand-motion. In this paper, we introduce some developed and suggested methods for image processing and hand-motion recognition. The developed air-touch technology will be very useful for advertizement panel, entertainment presentation system, kiosk system and so many applications.