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

Abd, Moaed A.; Paul, Rudy; Aravelli, Aparna; Bai, Ou; Lagos, Leonel; Lin, Maohua; Engeberg, Erik D.

doi:10.3390/s21134324

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessArticle

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

¹,

²,

³,

²,

and

^1,4,*

Ocean and Mechanical Engineering Department, Florida Atlantic University, Boca Raton, FL 33431, USA

Applied Research Center, Florida International University, Miami, FL 33174, USA

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)

View Full-Text Download PDF

Browse Figures

Review Reports Citation Export

Abstract

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

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Supplementary Material

Supplementary File 1:
ZIP-Document (ZIP, 4059 KiB)

Share and Cite

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

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Access Map by Country/Region

Author Biographies

Moaed A. Abd

Moaed A. Abd received the B.Sc degree in Mechatronics Engineering from the University of Baghdad, Iraq, in 2007 and the M.Sc degree in Mechanical Engineering from Duisberg-Essen University, Germany in 2011. Currently, he is doing his PhD degree and working as a graduate research assistant in the Ocean and Mechanical Engineering Department at Florida Atlantic University, USA. His current research interests include soft robotics, sensors and actuators, upper-limb prosthetics, machine learning and adaptive control.

Rudy Paul

Rudy Paul is a research assistant in the BioRobotics Lab at FAU. He is currently an undergraduate student in the Department of Ocean and Mechanical Engineering at FAU. He is expected to graduate with a B.S. in the spring of 2022 and a M.S. in the spring of 2023, both in mechanical engineering. His topics of study include fabrication of soft sensors and application to soft robotics as well as cervical spine research using FEA and experimental methods.

Aparna Aravelli

Dr. Aravelli is a Research Specialist at the Applied Research Center (ARC) and a faculty in the Department of Mechanical and Materials Engineering (MME) at FIU. She received Ph.D. and M.S degrees in mechanical engineering from the University of Miami and West Virginia University respectively. Dr. Aravelli’ s experience span the broad areas of engineering including sensors, robotics, engine emissions and optimization. Dr. Aravelli has published a book titled “Real-Time Measurement of Oxides of Nitrogen from Heavy-Duty Diesel Engines” and authored several publications. Currently, she is involved in DOE and NSF projects related to nuclear and microfluidics applications.

Ou Bai

Dr. Bai received his B.S. degree in Electronic Engineering from Tsinghua University, China and received his Ph.D. Degree in Advanced Systems Control Engineering from Saga University, Japan. Dr. Bai received post-doctoral research training in the National Institutes of Health. Dr. Bai currently serves as the Director of Human Cyber-Physical Systems Laboratory and Associate Professor in the Department of Electrical and Computer Engineering, Florida International University. Dr. Bai has published 150 peer-reviewed Journal papers, book chapters and conference proceeding papers. Dr. Bai’s research is highly interdisciplinary with collaborations from academia, industry, medical institutes and government Laboratories.

Leonel Lagos

Dr. Lagos is the Director of Research at the Applied Research Center (ARC) and adjunct faculty in the Department of Biomedical Engineering at FIU. He received Ph.D. in Civil/Environmental engineering and M.S. in mechanical engineering from the FIU. Dr. Lagos’ research and industrial experience span the broad areas of engineering including sensors, robotics, artificial intelligence, and machine learning. Dr. Lagos has published several peer-reviewed journal publications and conference publications. Currently, he is the Principal Investigator for Department of Energy, Department of Defense, and National Science Foundation projects in the area of sensors/robotics, AI, environmental remediation and workforce development programs.

Maohua Lin

Dr. Maohua Lin received his B.S. and M.S. in material science and technology from Fuzhou University and Nanchang University, respectively, and the Ph.D. degree in mechanical engineering from Florida Atlantic University. He is currently a research scientist in the Department of Ocean & Mechanical Engineering at Florida Atlantic University. His research interests include sensor design, soft robotics, medical device, 3D printing, simulation and modeling. His studies have resulted in several awards and over 30 journal articles and conference papers.

Erik D. Engeberg

Dr. Erik D. Engeberg earned his Ph.D. from the University of Utah in 2008. He is currently a Professor in the Ocean and Mechanical Engineering Department at Florida Atlantic University. His research interests include sensors, actuators, robotics, and electrophysiology.

Article Menu

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

Abstract

Supplementary Material

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Author Biographies

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Article Menu

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

Abstract

Supplementary Material

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Author Biographies

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

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