Predicting Perceptual Haptic Attributes of Textured Surface from Tactile Data Based on Deep CNN-LSTM Network

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Predicting Perceptual Haptic Attributes of Textured Surface from Tactile Data Based on Deep CNN-LSTM Network. / Awan, Mudassir Ibrahim; Hassan, Waseem; Jeon, Seokhee.

VRST 2023 - 29th ACM Symposium on Virtual Reality Software and Technology. Association for Computing Machinery, Inc., 2023. 33 (Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Awan, MI, Hassan, W & Jeon, S 2023, Predicting Perceptual Haptic Attributes of Textured Surface from Tactile Data Based on Deep CNN-LSTM Network. in VRST 2023 - 29th ACM Symposium on Virtual Reality Software and Technology., 33, Association for Computing Machinery, Inc., Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST, 29th ACM Symposium on Virtual Reality Software and Technology, VRST 2023, Christchurch, New Zealand, 09/10/2023. https://doi.org/10.1145/3611659.3615714

APA

Awan, M. I., Hassan, W., & Jeon, S. (2023). Predicting Perceptual Haptic Attributes of Textured Surface from Tactile Data Based on Deep CNN-LSTM Network. In VRST 2023 - 29th ACM Symposium on Virtual Reality Software and Technology [33] Association for Computing Machinery, Inc.. Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST https://doi.org/10.1145/3611659.3615714

Vancouver

Awan MI, Hassan W, Jeon S. Predicting Perceptual Haptic Attributes of Textured Surface from Tactile Data Based on Deep CNN-LSTM Network. In VRST 2023 - 29th ACM Symposium on Virtual Reality Software and Technology. Association for Computing Machinery, Inc. 2023. 33. (Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST). https://doi.org/10.1145/3611659.3615714

Author

Awan, Mudassir Ibrahim ; Hassan, Waseem ; Jeon, Seokhee. / Predicting Perceptual Haptic Attributes of Textured Surface from Tactile Data Based on Deep CNN-LSTM Network. VRST 2023 - 29th ACM Symposium on Virtual Reality Software and Technology. Association for Computing Machinery, Inc., 2023. (Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST).

Bibtex

@inproceedings{56934e15c02646faa66c909303070420,
title = "Predicting Perceptual Haptic Attributes of Textured Surface from Tactile Data Based on Deep CNN-LSTM Network",
abstract = "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.",
keywords = "Haptic texture classification, neural network, psychophysics",
author = "Awan, {Mudassir Ibrahim} and Waseem Hassan and Seokhee Jeon",
note = "Funding Information: This research was supported by the Ministry of Science and ICT Korea under the ITRC support program (IITP-2023-RS-2022-00156354), under the IITP program (2022-0-01005), both supervised by IITP, and under the Mid-Researcher Program (2022R1A2C1008483) supervised by the NRF Korea. Publisher Copyright: {\textcopyright} 2023 ACM.; 29th ACM Symposium on Virtual Reality Software and Technology, VRST 2023 ; Conference date: 09-10-2023 Through 11-10-2023",
year = "2023",
month = oct,
day = "9",
doi = "10.1145/3611659.3615714",
language = "English",
series = "Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST",
booktitle = "VRST 2023 - 29th ACM Symposium on Virtual Reality Software and Technology",
publisher = "Association for Computing Machinery, Inc.",

}

RIS

TY - GEN

T1 - Predicting Perceptual Haptic Attributes of Textured Surface from Tactile Data Based on Deep CNN-LSTM Network

AU - Awan, Mudassir Ibrahim

AU - Hassan, Waseem

AU - Jeon, Seokhee

N1 - Funding Information: This research was supported by the Ministry of Science and ICT Korea under the ITRC support program (IITP-2023-RS-2022-00156354), under the IITP program (2022-0-01005), both supervised by IITP, and under the Mid-Researcher Program (2022R1A2C1008483) supervised by the NRF Korea. Publisher Copyright: © 2023 ACM.

PY - 2023/10/9

Y1 - 2023/10/9

N2 - 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.

AB - 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.

KW - Haptic texture classification

KW - neural network

KW - psychophysics

U2 - 10.1145/3611659.3615714

DO - 10.1145/3611659.3615714

M3 - Article in proceedings

AN - SCOPUS:85175260496

T3 - Proceedings of the ACM Symposium on Virtual Reality Software and Technology, VRST

BT - VRST 2023 - 29th ACM Symposium on Virtual Reality Software and Technology

PB - Association for Computing Machinery, Inc.

T2 - 29th ACM Symposium on Virtual Reality Software and Technology, VRST 2023

Y2 - 9 October 2023 through 11 October 2023

ER -

ID: 388954624