• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.150-156
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• 2015

In this paper, a marionette control system using wrist and finger gestures through an IMU sensor is studied. The signals from the sensor device are conditioned and recognized, then the commands are sent to the 8 motors of the marionette via Bluetooth (5 motors control the motion of the marionette, and 3 motors control the location of the marionette). It is revealed that the degree of freedom of fingers are not independent from each other, therefore, some gestures are hardly made. Gesture mode changes for difficult postures of the fingers in cases of a lack of finger DOF are proposed. Therefore, the gesture mode change switches the assignment of gesture as required. Experimental results show that gesture mode change is successful for appropriate postures of a marionette.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.551-556
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• 2012

Objective: This study presents the effect of visual feedback on one-hand gesture performance in vision-based gesture recognition system when people use gestures to control a screen device remotely. Backgroud: gesture interaction receives growing attention because it uses advanced sensor technology and it allows users natural interaction using their own body motion. In generating motion, visual feedback has been to considered critical factor affect speed and accuracy. Method: three types of visual feedback(arrow, star, and animation) were selected and 20 gestures were listed. 12 participants perform each 20 gestures while given 3 types of visual feedback in turn. Results: People made longer hand trace and take longer time to make a gesture when they were given arrow shape feedback than star-shape feedback. The animation type feedback was most preferred. Conclusion: The type of visual feedback showed statistically significant effect on the length of hand trace, elapsed time, and speed of motion in performing a gesture. Application: This study could be applied to any device that needs visual feedback for device control. A big feedback generate shorter length of motion trace, less time, faster than smaller one when people performs gestures to control a device. So the big size of visual feedback would be recommended for a situation requiring fast actions. On the other hand, the smaller visual feedback would be recommended for a situation requiring elaborated actions.

• Smart Media Journal
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• v.8 no.3
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• pp.47-52
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• 2019

In this paper, we designed HW / SW interfaces for processing the signals of capacitive sensors like Electric Potential Sensor (EPS) to detect the surrounding electric field disturbance as feature signals in motion recognition systems. We implemented a smart light control system with those interfaces. In the system, the on/off switch and brightness adjustment are controlled by hand gestures using the designed and fabricated interface circuits. PWM (Pulse Width Modulation) signals of the controller with a driver IC are used to drive the LED and to control the brightness and on/off operation. Using the hand-gesture signals obtained through EPS sensors and the interface HW/SW, we can not only construct a gesture instructing system but also accomplish the faster recognition speed by developing dedicated interface hardware including control circuitry. Finally, using the proposed hand-gesture recognition and signal processing methods, the light control module was also designed and implemented. The experimental result shows that the smart light control system can control the LED module properly by accurate motion detection and gesture classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.616-619
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• 2004

As interests Un virtual reality being increased, the importance of HCI(Human computer interaction) field using gesture is also increased. However, in the preceding gesture recognition, the requirement of high-cost peripheral equipments limits users right. In this paper we suggest that through using low cost of USB PC-camera users are allowed to have more flexibly and cost down so that it can be adopted much commonly.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.525-531
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• 2012

Objective: The aim of this study is to investigate learnability of gestures-commands pair when people use gestures to control a device. Background: In vision-based gesture recognition system, selecting gesture-command pairing is critical for its usability in learning. Subjective preference and its agreement score, used in previous study(Lim et al., 2012) was used to group four gesture-command pairings. To quantify the learnability, two learning models, average time model and marginal time model, were used. Method: Two sets of eight gestures, total sixteen gestures were listed by agreement score and preference data. Fourteen participants divided into two groups, memorized each set of gesture-command pair and performed gesture. For a given command, time to recall the paired gesture was collected. Results: The average recall time for initial trials were differed by preference and agreement score as well as the learning rate R driven by the two learning models. Conclusion: Preference rate agreement score showed influence on learning of gesture-command pairs. Application: This study could be applied to any device considered to adopt gesture interaction system for device control.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.1
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• pp.1-7
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• 2012

This paper presents a system used to automatically recognize the road traffic control gestures of police officers. In this approach,the control gestures of traffic police officers are captured in the form of depth images.A human skeleton is then constructed using a kinematic model. The feature vector describing a traffic control gesture is built from the relative angles found amongst the joints of the constructed human skeleton. We utilize Support Vector Machines (SVMs) to perform the gesture recognition. Experiments show that our proposed method is robust and efficient and is suitable for real-time application. We also present a testbed system based on the SVMs trained data for real-time traffic gesture recognition.

• Journal of the Ergonomics Society of Korea
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• v.36 no.2
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• pp.109-121
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• 2017

Objective: This study aims to find suitable types and styles for gesture interaction as remote control on smart TVs. Background: Smart TV is being developed rapidly in the world, and gesture interaction has a wide range of research areas, especially based on vision techniques. However, most studies are focused on the gesture recognition technology. Also, not many previous studies of gestures types and styles on smart TVs were carried out. Therefore, it is necessary to check what users prefer in terms of gesture types and styles for each operation command. Method: We conducted an experiment to extract the target user manipulation commands required for smart TVs and select the corresponding gestures. To do this, we looked at gesture styles people use for every operation command, and checked whether there are any gesture styles they prefer over others. Through these results, this study was carried out with a process selecting smart TV operation commands and gestures. Results: Eighteen TV commands have been used in this study. With agreement level as a basis, we compared the six types of gestures and five styles of gestures for each command. As for gesture type, participants generally preferred a gesture of Path-Moving type. In the case of Pan and Scroll commands, the highest agreement level (1.00) of 18 commands was shown. As for gesture styles, the participants preferred a manipulative style in 11 commands (Next, Previous, Volume up, Volume down, Play, Stop, Zoom in, Zoom out, Pan, Rotate, Scroll). Conclusion: By conducting an analysis on user-preferred gestures, nine gesture commands are proposed for gesture control on smart TVs. Most participants preferred Path-Moving type and Manipulative style gestures based on the actual operations. Application: The results can be applied to a more advanced form of the gestures in the 3D environment, such as a study on VR. The method used in this study will be utilized in various domains.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.599-606
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• 2002

A new method to govern the navigation of a mobile robot using hand gesture recognition is proposed based on the following two procedures. One is to achieve vision information by using a 2-DOF camera as a communicating medium between a man and a mobile robot and the other is to analyze and to control the mobile robot according to the recognized hand gesture commands. In the previous researches, mobile robots are passively to move through landmarks, beacons, etc. In this paper, to incorporate various changes of situation, a new control system that manages the dynamical navigation of mobile robot is proposed. Moreover, without any generally used expensive equipments or complex algorithms for hand gesture recognition, a reliable hand gesture recognition system is efficiently implemented to convey the human commands to the mobile robot with a few constraints.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.461-469
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• 2015

The aim of this study is to find out the user's needs of a 3D hand gesture interface in the smart home environment. To find out the users' needs, we investigated which object the users want to use with a 3D hand gesture interface and why they want to use a 3D hand gesture interface. 3D hand gesture interfaces are studied to be applied to various devices in the smart environment. 3D hand gesture interfaces enable the users to control the smart environment with natural and intuitive hand gestures. With these advantages, finding out the user's needs of a 3D hand gesture interface would improve the user experience of a product. This study was conducted using a diary method to find out the user's needs with 20 participants. They wrote the needs of a 3D hand gesture interface during one week filling in the forms of a diary. The form of the diary is comprised of who, when, where, what and how to use a 3D hand gesture interface with each consisting of a usefulness score. A total of 322 data (209 normal data and 113 error data) were collected from users. There were some common objects which the users wanted to control with a 3D hand gesture interface and reasons why they want to use a 3D hand gesture interface. Among them, the users wanted to use a 3D hand gesture interface mostly to control the light, and to use a 3D hand gesture interface mostly to overcome hand restrictions. The results of this study would help develop effective and efficient studies of a 3D hand gesture interface giving valuable insights for the researchers and designers. In addition, this could be used for creating guidelines for 3D hand gesture interfaces.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1034-1037
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• 2015

In recent years in the field of robotics, various methods have been developed to create an intimate relationship between people and robots. These methods include speech, vision, and biometrics recognition as well as gesture-based interaction. These recognition technologies are used in various wearable devices, smartphones and other electric devices for convenience. Among these technologies, gesture recognition is the most commonly used and appropriate technology for wearable devices. Gesture recognition can be classified as contact or noncontact gesture recognition. This paper proposes contact gesture recognition with IMU and EMG sensors by using the hidden Markov model (HMM) twice. Several simple behaviors make main gestures through the one-stage HMM. It is equal to the Hidden Markov model process, which is well known for pattern recognition. Additionally, the sequence of the main gestures, which comes from the one-stage HMM, creates some higher-order gestures through the two-stage HMM. In this way, more natural and intelligent gestures can be implemented through simple gestures. This advanced process can play a larger role in gesture recognition-based UX for many wearable and smart devices.