Donghyun Sim
Donghyun Sim
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Donghyun Sim has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- A. S. M. Sharifuzzaman Sagar
The University of Western Australia
1 shared publication - Hyung Seok Kim
Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea
1 shared publication - Minjeong Cho
Department of Information and Communication Engineering, Sejong University, Seoul 05006, Korea
1 shared publication - Sunghoon Park
Department of Semiconductor System Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea
1 shared publication - Yoonchul Baek
Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
1 shared publication
Latest Publications
- Open Access
Low-Latency Haptic Open Glove for Immersive Virtual Reality Interaction
Recent advancements in telecommunications and the tactile Internet have paved the way for studying human senses through haptic technology. Haptic technology enables tactile sensations and control using virtual reality (VR) over a network. Researchers are developing various haptic devices to allow for real-time tactile sensation, which can be used in various industries, telesurgery, and other mission-critical operations. One of the main criteria of such devices is extremely low latency, as low as 1 ms. Although researchers are attempting to develop haptic devices with low latency, there remains a need to improve latency and robustness to hand sizes. In this paper, a low-latency haptic open glove (LLHOG) based on a rotary position sensor and min-max scaling (MMS) filter is proposed to realize immersive VR interaction. The proposed device detects finger flexion/extension and adduction/abduction motions using two position sensors located in the metacarpophalangeal (MCP) joint. The sensor data are processed using an MMS filter to enable low latency and ensure high accuracy. Moreover, the MMS filter is used to process object handling control data to enable hand motion-tracking. Its performance is evaluated in terms of accuracy, latency, and robustness to finger length variations. We achieved a very low processing delay of 145.37 μs per finger and overall hand motion-tracking latency of 4 ms. Moreover, we tested the proposed glove with 10 subjects and achieved an average mean absolute error (MAE) of for flexion/extension, and for adduction/abduction. The proposed method is therefore superior to the existing methods in terms of the above factors for immersive VR interaction.Open AccessLow-Latency Haptic Open Glove for Immersive Virtual Reality Interaction
Recent advancements in telecommunications and the tactile Internet have paved the way for studying human senses through haptic technology. Haptic technology enables tactile sensations and control using virtual reality (VR) over a network. Researchers are developing various haptic devices to allow for real-time tactile sensation, which can be used in various industries, telesurgery, and other mission-critical operations. One of the main criteria of such devices is extremely low latency, as low as 1 ms. Although researchers are attempting to develop haptic devices with low latency, there remains a need to improve latency and robustness to hand sizes. In this paper, a low-latency haptic open glove (LLHOG) based on a rotary position sensor and min-max scaling (MMS) filter is proposed to realize immersive VR interaction. The proposed device detects finger flexion/extension and adduction/abduction motions using two position sensors located in the metacarpophalangeal (MCP) joint. The sensor data are processed using an MMS filter to enable low latency and ensure high accuracy. Moreover, the MMS filter is used to process object handling control data to enable hand motion-tracking. Its performance is evaluated in terms of accuracy, latency, and robustness to finger length variations. We achieved a very low processing delay of 145.37 μs per finger and overall hand motion-tracking latency of 4 ms. Moreover, we tested the proposed glove with 10 subjects and achieved an average mean absolute error (MAE) of for flexion/extension, and for adduction/abduction. The proposed method is therefore superior to the existing methods in terms of the above factors for immersive VR interaction.