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Article

Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

1
Ocean and Mechanical Engineering Department, Florida Atlantic University, Boca Raton, FL 33431, USA
2
Applied Research Center, Florida International University, Miami, FL 33174, USA
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Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
4
The Center for Complex Systems & Brain Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
*
Author to whom correspondence should be addressed.
This manuscript is an extended version of the conference paper: Abd M.A., Al-Saidi M., Lin M., Liddle G., Mondal K., Engeberg E.D. Surface Feature Recognition and Grasped Object Slip Prevention with a Liquid Metal Tactile Sensor for a Prosthetic Hand presented at the 2020 8th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA, 29 November–1 December 2020.
Academic Editor: Salvatore Pirozzi
Sensors 2021, 21(13), 4324; https://doi.org/10.3390/s21134324
Received: 18 May 2021 / Revised: 15 June 2021 / Accepted: 22 June 2021 / Published: 24 June 2021
(This article belongs to the Section Sensors and Robotics)
Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands. View Full-Text
Keywords: liquid metal; tactile sensor; surface feature recognition; prosthetic hand; robotic hand liquid metal; tactile sensor; surface feature recognition; prosthetic hand; robotic hand
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MDPI and ACS Style

Abd, M.A.; Paul, R.; Aravelli, A.; Bai, O.; Lagos, L.; Lin, M.; Engeberg, E.D. Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition. Sensors 2021, 21, 4324. https://doi.org/10.3390/s21134324

AMA Style

Abd MA, Paul R, Aravelli A, Bai O, Lagos L, Lin M, Engeberg ED. Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition. Sensors. 2021; 21(13):4324. https://doi.org/10.3390/s21134324

Chicago/Turabian Style

Abd, Moaed A., Rudy Paul, Aparna Aravelli, Ou Bai, Leonel Lagos, Maohua Lin, and Erik D. Engeberg 2021. "Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition" Sensors 21, no. 13: 4324. https://doi.org/10.3390/s21134324

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