Seokhee Jeon’s research while affiliated with Kyung Hee University and other places

Haptic affection plays a crucial role in user experience, particularly in the automotive industry where the tactile quality of components can influence customer satisfaction. This study aims to accurately predict the affective property of a car door by only watching the force or torque profile of it when opening. To this end, a deep learning model is designed to capture the underlying relationships between force profiles and user-defined adjective ratings, providing insights into the door-opening experience. The dataset employed in this research includes force profiles and user adjective ratings collected from six distinct car models, reflecting a diverse set of door-opening characteristics and tactile feedback. The model's performance is assessed using Leave-One-Out Cross-Validation, a method that measures its generalization capability on unseen data. The results demonstrate that the proposed model achieves a high level of prediction accuracy, indicating its potential in various applications related to haptic affection and design optimization in the automotive industry.

Multi-mode haptic feedback is essential to achieve high realism and immersion in virtual environments. This paper proposed a novel silicone fingertip actuator integrated with a hot thermal fabric finger sleeve to render pressure, vibration, and hot thermal feedback simultaneously. The actuator is pneumatically actuated to render a realistic and effective tactile experience in accordance with hot thermal sensation. The silicone actuator, with two air chambers controlled by pneumatic valves connected to compressed air tanks. Simultaneously, a PWM signal from a microcontroller regulates the temperature of the thermal fabric sleeve, enhancing overall system functionality. The lower chamber of the silicone actuator is responsible for pressure feedback, whereas the upper chamber is devoted to vibrotactile feedback. The conductive yarn or thread was utilized to spread the thermal feedback actuation points on the thermal fabric's surface. To demonstrate the actuator's capability, a VR environment consisting of a bowl of liquid and a stove with fire was designed. Based on different functionalities the scenario can simulate the tactile perception of pressure, vibration, and temperature simultaneously or consecutively.

Our system introduces a modularized pneumatic actuating unit capable of delivering vibration, pressure, and impact feedback. Designed for adaptability, these modular tactile actuating units can be rapidly customized and reconfigured to suit a wide range of virtual reality (VR) scenarios, with a particular emphasis on safety training applications. This flexibility is demonstrated through scenarios such as using construction tools in a virtual environment and simulating safety protocols against falling objects. Innovative mounting solutions securely attach the actuators to various body sites, ensuring both comfort and stability during use. Our approach enables seamless integration into diverse VR safety training programs, enhancing the realism and effectiveness of simulations with precise and reliable haptic feedback.

Haptic feedback increases the realism of virtual environments. This paper proposes a wearable haptic device that renders torque feedback to the user's wrist from any angle. The device comprises a control part and a handle part. The control part consists of three DC gear motors and a microcontroller, while the handle part securely holds the Oculus Quest 2 right controller. The control part manages string tension to deliver the sensation of torque feedback during interactions with virtual tools or objects. The three points of the handle part are connected to the three motors of the control part via strings, which pull the handle part to render precise 360-degree (yaw and pitch) torque feedback to the user's wrist. Finally, to show the effectiveness of the proposed device, two VR demos were implemented- Shooting Game and Shielding Experience.

This paper presents a methodology for enhancing actuator performance in older devices or retrofitting devices with haptic feedback actuators. The approach is versatile, accommodating various actuator and mounting positions. Through a frequency sweep analysis, the system's characteristics are captured, enabling the creation of location-specific transfer functions to accurately transform input signals into command signals for a precise output at the target location. This method offers fast and simple collection of the system properties and generation of location-specific signals.

In safety training simulators, realistic haptic feedback is essential to make people construct accurate situation awareness through experiencing. In this regard, this paper presents a new and innovative system that provides the haptic experience of falling objects on user’s head during an earthquake. Special focus was on the accurate reproduction of impact feedback when various objects fall on the head. To this end, we propose a novel data-driven approach. This approach first collects 3-axis acceleration signals during real collision under several impact velocities. Afterward, 3D acceleration data is abstracted to a 1D acceleration profile using our novel max–min extraction approach. The impact signal for an arbitrary velocity is interpolated using a deep convolutional bidirectional long short-term memory encoder–decoder model. Rendering hardware is also implemented using high performance voice-coil vibrotactile actuator. Numerical and subjective evaluations are carried out to evaluate the performance of the proposed approach.Kindly check and confirm the edit made in the title.I confirm the edit is okay.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Authors Given name: [Joolekha Bibi] Last name: [Joolee], Given name: [Mohammad Shadman] Last name: [Hashem]. Also, kindly confirm the details in the metadata are correct.Yes, the author names are presented accurately and in the correct sequence.

This paper introduces a wearable pneumatic actuator, designed for providing multiple types of tactile feedback using a single end-effector. To this end, the actuator combines a 3D-printed framework consisting of five 0.5 DOF soft silicon air cells with a pneumatic system to deliver a range of tactile sensations through a single end-effector. The actuator is capable of producing diverse haptic feedback, including vibration, pressure, impact, and lateral force, controlled by an array of solenoid valves. The design’s focus on multimodality in a compact and lightweight form factor makes it highly suitable for wearable applications. It can produce a maximum static force of 8.3 N, vibrations with an acceleration of up to 3.15 g, and lateral forces of up to 3.3 N. The efficacy of the actuator is demonstrated through two distinct user studies: one focusing on perception, where users differentiated between lateral cues and vibration frequencies, and another within a first-person shooter gaming scenario, revealing enhanced user engagement and experience. The actuator’s adaptability to body sites and rich multimodal haptic feedback enables it to find applications in virtual reality, gaming, training simulations, and more.

This paper introduces a framework to predict multi-dimensional haptic attribute values that humans use to recognize the material by using the physical tactile signals (acceleration) generated when a textured surface is stroked. To this end, two spaces are established: a haptic attribute space and a physical signal space. A five-dimensional haptic attribute space is established through human adjective rating experiments with the 25 real texture samples. The physical space is constructed using tool-based interaction data from the same 25 samples. A mapping is modeled between the aforementioned spaces using a newly designed CNN-LSTM deep learning network. Finally, a prediction algorithm is implemented that takes acceleration data and returns coordinates in the haptic attribute space. A quantitative evaluation was conducted to inspect the reliability of the algorithm on unseen textures, showing that the model outperformed other similar models.

The current study strives to provide a haptic attribute space where texture surfaces are located based on their haptic attributes. The main aim of the haptic attribute space is to come up with a standardized model for representing and identifying haptic textures analogous to the RGB model for colors. To this end, a four dimensional haptic attribute space is established by conducting a psychophysical experiment where human participants rate 100 real-life texture surfaces according to their haptic attributes. The four dimensions of the haptic attribute space are rough-smooth, flat-bumpy, sticky-slippery, and hard-soft. The generalization and scalability of the haptic attribute space is achieved by training a 1D-CNN model for predicting attributes of haptic textures. The 1D-CNN is trained using the attribute data from psychophysical experiments and image features collected from the images of real textures. The prediction power granted by the 1D-CNN renders scalability to the haptic attribute space. The prediction accuracy of the proposed 1D-CNN model is compared against other machine learning and deep learning algorithms. The results show that the proposed method outperforms the other models on MAE and RMSE metrics.