TY - GEN
T1 - Texture Discrimination using a Flexible Tactile Sensor Array on a Soft Biomimetic Finger
AU - Sankar, Sriramana
AU - Brown, Alisa
AU - Balamurugan, Darshini
AU - Nguyen, Harrison
AU - Iskarous, Mark
AU - Simcox, Talya
AU - Kumar, Deepesh
AU - Nakagawa, Andrei
AU - Thakor, Nitish
N1 - Funding Information:
ACKNOWLEDGMENT This research was funded by the NSF award 1830444: Scalable, Customizable Sensory Solutions for Dexterous Robotic Hands.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Soft robotic fingers provide enhanced flexibility and dexterity when interacting with the environment. The capability of soft fingers can be further improved by integrating them with tactile sensors to discriminate various textured surfaces. In this work, a flexible 3×3 fabric-based tactile sensor array was integrated with a soft, biomimetic finger for a texture discrimination task. The finger palpated seven different textured plates and the corresponding tactile response was converted into neuromorphic spiking patterns, mimicking the firing pattern of mechanoreceptors in the skin. Spike-based feature metrics were used to classify different textures using the support vector machine (SVM) classifier. The sensor was able to achieve an accuracy of 99.21% when two features, mean spike rate and average inter-spike interval, from each taxel were used as inputs into the classifier. The experiment showed that an inexpensive, soft, biomimetic finger combined with the flexible tactile sensor array can potentially help users perceive their environment better.
AB - Soft robotic fingers provide enhanced flexibility and dexterity when interacting with the environment. The capability of soft fingers can be further improved by integrating them with tactile sensors to discriminate various textured surfaces. In this work, a flexible 3×3 fabric-based tactile sensor array was integrated with a soft, biomimetic finger for a texture discrimination task. The finger palpated seven different textured plates and the corresponding tactile response was converted into neuromorphic spiking patterns, mimicking the firing pattern of mechanoreceptors in the skin. Spike-based feature metrics were used to classify different textures using the support vector machine (SVM) classifier. The sensor was able to achieve an accuracy of 99.21% when two features, mean spike rate and average inter-spike interval, from each taxel were used as inputs into the classifier. The experiment showed that an inexpensive, soft, biomimetic finger combined with the flexible tactile sensor array can potentially help users perceive their environment better.
KW - Flexible tactile sensor array
KW - Neuromorphic model
KW - Soft biomimetic finger
KW - Supervised learning
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U2 - 10.1109/SENSORS43011.2019.8956704
DO - 10.1109/SENSORS43011.2019.8956704
M3 - Conference contribution
AN - SCOPUS:85078694825
T3 - Proceedings of IEEE Sensors
BT - 2019 IEEE Sensors, SENSORS 2019 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Sensors, SENSORS 2019
Y2 - 27 October 2019 through 30 October 2019
ER -