|Table of Contents|

[1] Huang Gewen, He Cong, Wu Juan, Wang Fei, et al. Metrics analysis of tactile perceptual space basedon improved NMDS for leather textures [J]. Journal of Southeast University (English Edition), 2022, 38 (1): 49-55. [doi:10.3969/j.issn.1003-7985.2022.01.008]
Copy

Metrics analysis of tactile perceptual space basedon improved NMDS for leather textures()
Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
38
Issue:
2022 1
Page:
49-55
Research Field:
Computer Science and Engineering
Publishing date:
2022-03-20

Info

Title:
Metrics analysis of tactile perceptual space basedon improved NMDS for leather textures
Author(s):
Huang Gewen;  He Cong;  Wu Juan;  Wang Fei
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Keywords:
haptic;  tactile perception;  perceptual space;  non-metric multidimensional scaling
PACS:
TP391
DOI:
10.3969/j.issn.1003-7985.2022.01.008
Abstract:
To solve the fuzzy and unstable tactile similarity relationship between some sample points in the perception experiment, an improved non-metric multidimensional scaling(INMDS)is proposed in this paper. In view of the inconsistency of each sample’s contribution, the maximum marginal decision when constructing the perception space to describe the tactile perception characteristics is also proposed. The corresponding constraints are set according to the degree of similarity, and controlling the relaxation variable factor is proposed to optimize the perception dimension and coordinate measurement. The effectiveness of the INMDS algorithm is verified by two perception experiments. The results show that compared with the metric multidimensional scaling(MDS)and non-metric multidimensional scaling(NMDS)algorithms, the perceptual space constructed by INMDS can more accurately reflect the difference relationship between different leather sample points perceived by people. Moreover, the relative position of sample points in the perceptual space is more consistent with subjective perception results.

References:

[1] Fishel J A, Loeb G E. Bayesian exploration for intelligent identification of textures [J]. Front Neurorobot, 2012, 6: 1-20. DOI:10.3389/fnbot.2012.00004.
[2] Ozioko O, Navaraj W, Hersh M, et al. Tacsac: A wearable haptic device with capacitive touch-sensing capability for tactile display [J]. Sensors, 2020, 20(17): 4780. DOI:10.3390/s20174780.
[3] Martolini C, Cappagli G, Signorini S, et al. Effects of increasing stimulated area in spatiotemporally congruent unisensory and multisensory conditions [J]. Brain Sciences, 2021, 11(3): 343. DOI:10.3390/brainsci11030343.
[4] Mun S, Lee H, Choi S. Perceptual space of regular homogeneous haptic textures rendered using electrovibration [C]// Proceedings of the 2019 IEEE World Haptics Conference(WHC). Waikoloa Village, HI, USA, 2019: 7-12. DOI:10.1109/WHC.2019.8816143.
[5] Rodríguez J L, Velázquez R, Del-Valle-Soto C, et al. Active and passive haptic perception of shape: Passive haptics can support navigation [J]. Electronics, 2019, 8(3): 355. DOI:10.3390/electronics8030355.
[6] Hashim I H M, Kumamoto S, Takemura K, et al. Tactile evaluation feedback system for multi-layered structure inspired by human tactile perception mechanism [J]. Sensors, 2017, 17(11): 2601. DOI:10.3390/s17112601.
[7] Culbertson H, Kuchenbecker K J. Importance of matching physical friction, hardness, and texture in creating realistic haptic virtual surfaces [J]. IEEE Transactions on Haptics, 2017, 10(1): 63-74. DOI:10.1109/TOH.2016.2598751.
[8] Klatzky R L, Pawluk D, Peer A. Haptic perception of material properties and implications for applications [J]. Proceedings of the IEEE, 2013, 101(9): 2081-2092. DOI:10.1109/Jproc.2013.2248691.
[9] Massalim Y, Kappassov Z, Varol H A. Deep vibro-tactile perception for simultaneous texture identification, slip detection, and speed estimation [J]. Sensors, 2020, 20(15): 4121. DOI:10.3390/s20154121.
[10] Abdulali A, Rakhmatov R, Ogay T, et al. Data-driven modeling and rendering of force responses from elastic tool deformation [J]. Sensors, 2018, 18(1): 237. DOI:10.3390/s18010237.
[11] Shao Z Y, Wu J, Ouyang Q Q, et al. Multi-layered perceptual model for haptic perception of compliance [J]. Electronics, 2019, 8(12): 1497. DOI:10.3390/electronics8121497.
[12] Li W, Jelfs B, Kealy A, et al. Cooperative localization using distance measurements for mobile nodes [J]. Sensors, 2021, 21(4): 1507. DOI:10.3390/s21041507.
[13] Wei M, Aragues R, Sagues C, et al. Noisy range network localization based on distributed multidimensional scaling [J]. IEEE Sensors Journal, 2014, 15(3): 1. DOI:10.1109/jsen.2014.2366035.
[14] Entezami A, Sarmadi H, Behkamal B, et al. Health monitoring of large-scale civil structures: An approach based on data partitioning and classical multidimensional scaling [J]. Sensors, 2021, 21(5): 1646. DOI:10.3390/s21051646.
[15] Tenreiro Machado J, Lopes A, Galhano A. Multidimensional scaling visualization using parametric similarity indices [J]. Entropy, 2015, 17(4): 1775-1794. DOI:10.3390/e17041775.
[16] Li B, Cui W, Wang B. Arobust wireless sensor network localization algorithm in mixed LOS/NLOS scenario [J]. Sensors, 2015, 15(9): 23536-23553. DOI:10.3390/s150923536.
[17] Yoshioka T, Bensma�EF;a S J, Craig J C, et al. Texture perception through direct and indirect touch: An analysis of perceptual space for tactile textures in two modes of exploration [J]. Somatosensory & Motor Research, 2007, 24(1/2): 53-70. DOI:10.1080/08990220701318163.
[18] Wu J, Li N, Liu W, et al. Experimental study on the perception characteristics of haptic texture by multidimensional scaling [J]. IEEE Transactions on Haptics, 2015, 8(4): 410-420. DOI:10.1109/TOH.2015.2438866.
[19] Yoshioka T, Zhou J. Factors involved in tactile texture perception through probes [J]. Advanced Robotics, 2009, 23(6): 747-766. DOI:10.1163/156855309X431703.
[20] Neumann A, Müller L, Falk B, et al. Experimental study on haptic perception of rough surfaces using MDS [J]. Procedia CIRP, 2016, 50: 577-582. DOI:10.1016/j.procir.2016.05.087.
[21] Piovari M, Levin D I W, Rebello J, et al. An interaction-aware, perceptual model for non-linear elastic objects [J]. ACM Transactions on Graphics, 2016, 35(4): 1-13. DOI:10.1145/2897824.2925885.
[22] Wills J, Agarwal S, Kriegman D, et al. Toward a perceptual space for gloss [J]. ACM Transactions on Graphics, 2009, 28(4): 1-15. DOI:10.1145/1559755.1559760.
[23] Woods C T, Robertson S, Sinclair W H, et al. Non-metric multidimensional performance indicator scaling reveals seasonal and team dissimilarity within the National Rugby League [J].Journal of Science and Medicine in Sport, 2018, 21(4): 410-415. DOI:10.1016/j.jsams.2017.06.014.
[24] Su Z Q, Song B L, Liu M, et al. Feature-based association rules for the evaluation of the English network considering non-metric multidimensional scale [J].International Journal of Electrical Engineering Education, 2019, 56(4): 1-18. DOI:10.1177/0020720919884245.
[25] Maaten L, Hinton G. Visualizing non-metric similarities in multiple maps [J]. Machine Learning, 2012, 87(1): 33-55. DOI:10.1007/s10994-011-5273-4.
[26] Sameer A, Josh W, Lawrence C, et al. Generalized non-metric multidimensional scaling [C]// Proceedings of the Artificial Intelligence and Statistics. San Juan, Puerto Rico, 2007: 11-18.

Memo

Memo:
Biographies: Huang Gewen(1998—), female, graduate; Wu Juan(corresponding author), female, doctor, professor, juanwuseu@seu.edu.cn.
Foundation items: The National Key R&D Program of China(No.2018AAA0103001), the National Natural Science Foundation of China(No.62073073).
Citation: Huang Gewen, He Cong, Wu Juan, et al. Metrics analysis of tactile perceptual space based on improved NMDS for leather textures[J].Journal of Southeast University(English Edition), 2022, 38(1):49-55.DOI:10.3969/j.issn.1003-7985.2022.01.008.
Last Update: 2022-03-20