Invariant neuromorphic representations of tactile stimuli improve robustness of a real-time texture classification system Article Swipe
Mark M. Iskarous
,
Zan Chaudhry
,
Fangjie Li
,
Samuel Bello
,
Sriramana Sankar
,
Ariel Slepyan
,
Natasha Chugh
,
Christopher L. Hunt
,
Rebecca J. Greene
,
Nitish V. Thakor
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.17060
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.17060
Humans have an exquisite sense of touch which robotic and prosthetic systems aim to recreate. We developed algorithms to create neuron-like (neuromorphic) spiking representations of texture that are invariant to the scanning speed and contact force applied in the sensing process. The spiking representations are based on mimicking activity from mechanoreceptors in human skin and further processing up to the brain. The neuromorphic encoding process transforms analog sensor readings into speed and force invariant spiking representations in three sequential stages: the force invariance module (in the analog domain), the spiking activity encoding module (transforms from analog to spiking domain), and the speed invariance module (in the spiking domain). The algorithms were tested on a tactile texture dataset collected in 15 speed-force conditions. An offline texture classification system built on the invariant representations has higher classification accuracy, improved computational efficiency, and increased capability to identify textures explored in novel speed-force conditions. The speed invariance algorithm was adapted to a real-time human-operated texture classification system. Similarly, the invariant representations improved classification accuracy, computational efficiency, and capability to identify textures explored in novel conditions. The invariant representation is even more crucial in this context due to human imprecision which seems to the classification system as a novel condition. These results demonstrate that invariant neuromorphic representations enable better performing neurorobotic tactile sensing systems. Furthermore, because the neuromorphic representations are based on biological processing, this work can be used in the future as the basis for naturalistic sensory feedback for upper limb amputees.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.17060
- https://arxiv.org/pdf/2411.17060
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404988558
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404988558Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.17060Digital Object Identifier
- Title
-
Invariant neuromorphic representations of tactile stimuli improve robustness of a real-time texture classification systemWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-11-26Full publication date if available
- Authors
-
Mark M. Iskarous, Zan Chaudhry, Fangjie Li, Samuel Bello, Sriramana Sankar, Ariel Slepyan, Natasha Chugh, Christopher L. Hunt, Rebecca J. Greene, Nitish V. ThakorList of authors in order
- Landing page
-
https://arxiv.org/abs/2411.17060Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2411.17060Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2411.17060Direct OA link when available
- Concepts
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Neuromorphic engineering, Invariant (physics), Robustness (evolution), Artificial intelligence, Computer science, Pattern recognition (psychology), Computer vision, Mathematics, Artificial neural network, Mathematical physics, Chemistry, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.system. | 162 |
| abstract_inverted_index.systems | 11 |
| abstract_inverted_index.tactile | 114, 216 |
| abstract_inverted_index.texture | 25, 115, 124, 160 |
| abstract_inverted_index.activity | 48, 90 |
| abstract_inverted_index.domain), | 87, 98 |
| abstract_inverted_index.domain). | 107 |
| abstract_inverted_index.encoding | 63, 91 |
| abstract_inverted_index.explored | 145, 177 |
| abstract_inverted_index.feedback | 243 |
| abstract_inverted_index.identify | 143, 175 |
| abstract_inverted_index.improved | 136, 167 |
| abstract_inverted_index.process. | 40 |
| abstract_inverted_index.readings | 68 |
| abstract_inverted_index.scanning | 31 |
| abstract_inverted_index.systems. | 218 |
| abstract_inverted_index.textures | 144, 176 |
| abstract_inverted_index.accuracy, | 135, 169 |
| abstract_inverted_index.algorithm | 153 |
| abstract_inverted_index.amputees. | 247 |
| abstract_inverted_index.collected | 117 |
| abstract_inverted_index.developed | 16 |
| abstract_inverted_index.exquisite | 3 |
| abstract_inverted_index.increased | 140 |
| abstract_inverted_index.invariant | 28, 73, 130, 165, 182, 209 |
| abstract_inverted_index.mimicking | 47 |
| abstract_inverted_index.real-time | 158 |
| abstract_inverted_index.recreate. | 14 |
| abstract_inverted_index.Similarly, | 163 |
| abstract_inverted_index.algorithms | 17, 109 |
| abstract_inverted_index.biological | 227 |
| abstract_inverted_index.capability | 141, 173 |
| abstract_inverted_index.condition. | 204 |
| abstract_inverted_index.invariance | 82, 102, 152 |
| abstract_inverted_index.performing | 214 |
| abstract_inverted_index.processing | 56 |
| abstract_inverted_index.prosthetic | 10 |
| abstract_inverted_index.sequential | 78 |
| abstract_inverted_index.transforms | 65 |
| abstract_inverted_index.(transforms | 93 |
| abstract_inverted_index.conditions. | 121, 149, 180 |
| abstract_inverted_index.demonstrate | 207 |
| abstract_inverted_index.efficiency, | 138, 171 |
| abstract_inverted_index.imprecision | 194 |
| abstract_inverted_index.neuron-like | 20 |
| abstract_inverted_index.processing, | 228 |
| abstract_inverted_index.speed-force | 120, 148 |
| abstract_inverted_index.Furthermore, | 219 |
| abstract_inverted_index.naturalistic | 241 |
| abstract_inverted_index.neuromorphic | 62, 210, 222 |
| abstract_inverted_index.neurorobotic | 215 |
| abstract_inverted_index.computational | 137, 170 |
| abstract_inverted_index.(neuromorphic) | 21 |
| abstract_inverted_index.classification | 125, 134, 161, 168, 199 |
| abstract_inverted_index.human-operated | 159 |
| abstract_inverted_index.representation | 183 |
| abstract_inverted_index.representations | 23, 43, 75, 131, 166, 211, 223 |
| abstract_inverted_index.mechanoreceptors | 50 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.24722153 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |