Authoring New Haptic Textures Based on Interpolation of Real Textures in Affective Space

Hassan, Waseem; Abdulali, Arsen; Jeon, Seokhee

doi:10.1109/tie.2019.2914572

Cited by 17 publications

(8 citation statements)

References 23 publications

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“…In addition, they proposed a recursive constraint projection (RCP) algorithm for data segmentation, which also proves effective for processing streaming data. Hassan et al 30 presented the concept of Haptic Authoring, which involves the creation of novel virtual textures through the interpolation of pre-existing texture models, guided by their correlation with descriptive affective attributes. Vibrotactile signals are produced using Generative Adversarial Network (GAN) in 11 .…”

Section: Related Workmentioning

confidence: 99%

A multimodal multitask deep learning framework for vibrotactile feedback and sound rendering

Joolee,

Uddin

2024

Sci Rep

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Data-driven approaches are often utilized to model and generate vibrotactile feedback and sounds for rigid stylus-based interaction. Nevertheless, in prior research, these two modalities were typically addressed separately due to challenges related to synchronization and design complexity. To this end, we introduce a novel multimodal multitask deep learning framework. In this paper, we developed a comprehensive end-to-end data-driven system that encompasses the capture of contact acceleration signals and sound data from various texture surfaces. This framework introduces novel encoder-decoder networks for modeling and rendering vibrotactile feedback through an actuator while routing sound to headphones. The proposed encoder-decoder networks incorporate stacked transformers with convolutional layers to capture both local variability and overall trends within the data. To the best of our knowledge, this is the first attempt to apply transformer-based data-driven approach for modeling and rendering of vibrotactile signals as well as sounds during tool-surface interactions. In numerical evaluations, the proposed framework demonstrates a lower RMS error compared to state-of-the-art models for both vibrotactile signals and sound data. Additionally, subjective similarity evaluation also confirm the superiority of proposed method over state-of-the-art.

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Section: Related Workmentioning

confidence: 99%

A multimodal multitask deep learning framework for vibrotactile feedback and sound rendering

Joolee,

Uddin

2024

Sci Rep

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“…The studies in 16 , 17 used the SynTouch BioTac sensor to collect physical interaction signals from objects and use them for predicting haptic attributes of textures. Another study attempted to establish one such space (authoring space) where textures were located based on their haptic attributes 18 . The space was created from a combination of physical interaction signals (contact acceleration patterns) with texture and haptic attributes (hardness, roughness) of texture.…”

Section: Introductionmentioning

confidence: 99%

“…In certain studies the whole space would have to be remade in case of a new entry. Third, the authoring space in 18 was intended to create virtual textures inheriting properties of real textures, while our current goal is to establish a space which can accommodate and identify new real textures based on their haptic attributes. Last, all of the aforementioned studies needed physical interaction with a surface in order to incorporate them into the space.…”

Section: Introductionmentioning

confidence: 99%

Establishing haptic texture attribute space and predicting haptic attributes from image features using 1D-CNN

Hassan

Joolee

Jeon

2023

Sci Rep

Self Cite

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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.

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“…Texture tactile perception refers to tactile experiences and feeling which is gained by touching subtle profile changes of real object surface through the force tactile sensing channel of human [1,2,3]. The texture tactile reproduction is to simulate and replicate the tactile touching when human skin interacts with the objects through some technological means, so that operator can perceive tactile information of the objects in the virtual world [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Friction-varying tactile display with large working space for texture reproduction

Zeng

Liu

Zhang

et al. 2021

IEICE Electron. Express

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As the latest touch-based input equipment of consumer electronics, touch screen is a convenient and natural human-machine interaction vehicle. However, it has not separated from the scope of "interactive input". Tactile display adds the tactile output along with the interactive input and helps the operator to perceive tactile feedback of texture, profile, etc., which increases the immersion of the operator significantly. In this study, a friction-varying texture tactile display which is composed of resonance module, positioning module and vision display module was designed by using the squeeze film effect. In this device, a piece of 194×100×2 mm transparent glass was used as the touch tablet, which can provide a large working area and is easy to be integrated with common consumer electronics. The feasibility and precision of the designed friction-varying tactile display in simulation of texture were further verified by a psychophysical experiment.

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Authoring New Haptic Textures Based on Interpolation of Real Textures in Affective Space

Cited by 17 publications

References 23 publications

A multimodal multitask deep learning framework for vibrotactile feedback and sound rendering

A multimodal multitask deep learning framework for vibrotactile feedback and sound rendering

Establishing haptic texture attribute space and predicting haptic attributes from image features using 1D-CNN

Friction-varying tactile display with large working space for texture reproduction

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