Classification of texture and frictional condition at initial contact by tactile afferent responses

Heba Khamis; Stephen J. Redmond; Vaughan Macefield; Ingvars Birznieks

doi:10.1007/978-3-662-44193-0_58

Classification of texture and frictional condition at initial contact by tactile afferent responses

Heba Khamis, Stephen J. Redmond, Vaughan Macefield, Ingvars Birznieks

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

14 Citations (Scopus)

Abstract

Adjustments to friction are crucial for precision object handling in both humans and robotic manipulators. The aim of this work was to investigate the ability of machine learning to disentangle concurrent stimulus parameters, such as normal force ramp rate, texture and friction, from the responses of tactile afferents at the point of initial contact with the human finger pad. Three textured stimulation surfaces were tested under two frictional conditions each, with a 4 N normal force applied at three ramp rates. During stimulation, the responses of fourteen afferents (5 SA-I, 2 SA-II, 5 FA-I, 2 FA-II) were recorded. A Parzen window classifier was used to classify ramp rate, texture and frictional condition using spike count, first spike latency or peak frequency from each afferent. This is the first study to show that ramp rate, texture and frictional condition could be classified concurrently with over 90% accuracy using a small number of tactile sensory units.

Original language	English
Title of host publication	Haptics: Neuroscience, Devices, Modeling, and Applications
Subtitle of host publication	9th International Conference, EuroHaptics 2014
Editors	Christian Duriez, Christian Duriez, Malika Auvray, Malika Auvray
Place of Publication	Germany
Publisher	Springer
Pages	460-468
Number of pages	9
ISBN (Electronic)	9783662441930
ISBN (Print)	9783662441923
DOIs	https://doi.org/10.1007/978-3-662-44193-0_58
Publication status	Published - 2014
Externally published	Yes
Event	International Conference on Haptics: Neuroscience, Devices, Modeling, and Applications, EuroHaptics 2014 - Versailles, France Duration: 24 Jun 2014 → 26 Jun 2014 Conference number: 9th

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8618
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Haptics: Neuroscience, Devices, Modeling, and Applications, EuroHaptics 2014
Abbreviated title	EuroHaptics 2014
Country/Territory	France
City	Versailles
Period	24/06/14 → 26/06/14

Keywords

Friction
Microneurography
Parzen classifier
Texture

Access to Document

10.1007/978-3-662-44193-0_58

Cite this

Khamis, H., Redmond, S. J., Macefield, V., & Birznieks, I. (2014). Classification of texture and frictional condition at initial contact by tactile afferent responses. In C. Duriez, C. Duriez, M. Auvray, & M. Auvray (Eds.), Haptics: Neuroscience, Devices, Modeling, and Applications: 9th International Conference, EuroHaptics 2014 (pp. 460-468). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8618). Springer. https://doi.org/10.1007/978-3-662-44193-0_58

Khamis, Heba ; Redmond, Stephen J. ; Macefield, Vaughan et al. / Classification of texture and frictional condition at initial contact by tactile afferent responses. Haptics: Neuroscience, Devices, Modeling, and Applications: 9th International Conference, EuroHaptics 2014. editor / Christian Duriez ; Christian Duriez ; Malika Auvray ; Malika Auvray. Germany : Springer, 2014. pp. 460-468 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Adjustments to friction are crucial for precision object handling in both humans and robotic manipulators. The aim of this work was to investigate the ability of machine learning to disentangle concurrent stimulus parameters, such as normal force ramp rate, texture and friction, from the responses of tactile afferents at the point of initial contact with the human finger pad. Three textured stimulation surfaces were tested under two frictional conditions each, with a 4 N normal force applied at three ramp rates. During stimulation, the responses of fourteen afferents (5 SA-I, 2 SA-II, 5 FA-I, 2 FA-II) were recorded. A Parzen window classifier was used to classify ramp rate, texture and frictional condition using spike count, first spike latency or peak frequency from each afferent. This is the first study to show that ramp rate, texture and frictional condition could be classified concurrently with over 90% accuracy using a small number of tactile sensory units.",

keywords = "Friction, Microneurography, Parzen classifier, Texture",

author = "Heba Khamis and Redmond, {Stephen J.} and Vaughan Macefield and Ingvars Birznieks",

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doi = "10.1007/978-3-662-44193-0_58",

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Khamis, H, Redmond, SJ, Macefield, V & Birznieks, I 2014, Classification of texture and frictional condition at initial contact by tactile afferent responses. in C Duriez, C Duriez, M Auvray & M Auvray (eds), Haptics: Neuroscience, Devices, Modeling, and Applications: 9th International Conference, EuroHaptics 2014. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8618, Springer, Germany, pp. 460-468, International Conference on Haptics: Neuroscience, Devices, Modeling, and Applications, EuroHaptics 2014, Versailles, France, 24/06/14. https://doi.org/10.1007/978-3-662-44193-0_58

Classification of texture and frictional condition at initial contact by tactile afferent responses. / Khamis, Heba; Redmond, Stephen J.; Macefield, Vaughan et al.
Haptics: Neuroscience, Devices, Modeling, and Applications: 9th International Conference, EuroHaptics 2014. ed. / Christian Duriez; Christian Duriez; Malika Auvray; Malika Auvray. Germany: Springer, 2014. p. 460-468 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8618).

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

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AU - Redmond, Stephen J.

AU - Macefield, Vaughan

AU - Birznieks, Ingvars

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N2 - Adjustments to friction are crucial for precision object handling in both humans and robotic manipulators. The aim of this work was to investigate the ability of machine learning to disentangle concurrent stimulus parameters, such as normal force ramp rate, texture and friction, from the responses of tactile afferents at the point of initial contact with the human finger pad. Three textured stimulation surfaces were tested under two frictional conditions each, with a 4 N normal force applied at three ramp rates. During stimulation, the responses of fourteen afferents (5 SA-I, 2 SA-II, 5 FA-I, 2 FA-II) were recorded. A Parzen window classifier was used to classify ramp rate, texture and frictional condition using spike count, first spike latency or peak frequency from each afferent. This is the first study to show that ramp rate, texture and frictional condition could be classified concurrently with over 90% accuracy using a small number of tactile sensory units.

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Khamis H, Redmond SJ, Macefield V, Birznieks I. Classification of texture and frictional condition at initial contact by tactile afferent responses. In Duriez C, Duriez C, Auvray M, Auvray M, editors, Haptics: Neuroscience, Devices, Modeling, and Applications: 9th International Conference, EuroHaptics 2014. Germany: Springer. 2014. p. 460-468. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-662-44193-0_58

Classification of texture and frictional condition at initial contact by tactile afferent responses

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