Surface Feature Recognition and Grasped Object Slip Prevention with a Liquid Metal Tactile Sensor for a Prosthetic Hand

Moaed A. Abd; Mostapha Al-Saidi; Maohua Lin; Genevieve Liddle; Kunal Mondal; Erik D. Engeberg

doi:10.1109/BioRob49111.2020.9224294

Surface Feature Recognition and Grasped Object Slip Prevention with a Liquid Metal Tactile Sensor for a Prosthetic Hand

Moaed A. Abd, Mostapha Al-Saidi, Maohua Lin, Genevieve Liddle, Kunal Mondal, Erik D. Engeberg

Nuclear Materials Packaging, Transportat

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

17 Scopus citations

Abstract

There is a need to gather rich, real-time tactile information to enhance prosthetic hand performance during object manipulation. To that end, a highly stretchable liquid metal tactile sensor was designed, manufactured, and integrated into the fingertip of an i-limb Ultra prosthetic hand. With this novel tactile sensor, the feasibility of real-time slip detection and prevention of a grasped object was demonstrated. Furthermore, this liquid metal tactile sensor was used to distinguish between five different surface patterns with high accuracy using three different classification algorithms: K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF). The K-nearest neighbors (KNN) classifier produced the highest classification accuracy of 98% to distinguish between five different surface textures. Taken together, this novel prosthetic fingertip tactile sensor has the potential to improve grasp control and object manipulation operations for upper limb amputees. Additionally, this paper documents the first time that a liquid metal tactile sensor has been used to distinguish between different surface features, to the best knowledge of the authors.

Original language	English
Title of host publication	2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
Publisher	IEEE Computer Society
Pages	1174-1179
Number of pages	6
ISBN (Electronic)	9781728159072
DOIs	https://doi.org/10.1109/BioRob49111.2020.9224294
State	Published - Nov 2020
Event	8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020 - New York City, United States Duration: Nov 29 2020 → Dec 1 2020

Publication series

Name	Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Volume	2020-November
ISSN (Print)	2155-1774

Conference

Conference	8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
Country/Territory	United States
City	New York City
Period	11/29/20 → 12/1/20

Funding

* This research was supported in part by NIH 1R01EB025819 and NSF awards 1317952, 1536136, and 1659484, Dept. of Energy Contract TOA#0000403076, and by I-SENSE at FAU Moaed A. Abd, Mostapha Al-Saidi, Dr. Maohua Lin, and Dr. Erik Engeberg, are with the Ocean and Mechanical Engineering Department, Florida Atlantic University, Boca Raton, FL 33431 USA (email: mabd2015, This research was supported in part by NIH 1R01EB025819 and NSF awards 131795.

Access to Document

10.1109/BioRob49111.2020.9224294

Cite this

Abd, M. A., Al-Saidi, M., Lin, M., Liddle, G., Mondal, K., & Engeberg, E. D. (2020). Surface Feature Recognition and Grasped Object Slip Prevention with a Liquid Metal Tactile Sensor for a Prosthetic Hand. In 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020 (pp. 1174-1179). Article 9224294 (Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics; Vol. 2020-November). IEEE Computer Society. https://doi.org/10.1109/BioRob49111.2020.9224294

Abd, Moaed A. ; Al-Saidi, Mostapha ; Lin, Maohua et al. / Surface Feature Recognition and Grasped Object Slip Prevention with a Liquid Metal Tactile Sensor for a Prosthetic Hand. 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020. IEEE Computer Society, 2020. pp. 1174-1179 (Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics).

@inproceedings{7e9e2cfdea724641b6c4e790782d1114,

title = "Surface Feature Recognition and Grasped Object Slip Prevention with a Liquid Metal Tactile Sensor for a Prosthetic Hand",

abstract = "There is a need to gather rich, real-time tactile information to enhance prosthetic hand performance during object manipulation. To that end, a highly stretchable liquid metal tactile sensor was designed, manufactured, and integrated into the fingertip of an i-limb Ultra prosthetic hand. With this novel tactile sensor, the feasibility of real-time slip detection and prevention of a grasped object was demonstrated. Furthermore, this liquid metal tactile sensor was used to distinguish between five different surface patterns with high accuracy using three different classification algorithms: K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF). The K-nearest neighbors (KNN) classifier produced the highest classification accuracy of 98% to distinguish between five different surface textures. Taken together, this novel prosthetic fingertip tactile sensor has the potential to improve grasp control and object manipulation operations for upper limb amputees. Additionally, this paper documents the first time that a liquid metal tactile sensor has been used to distinguish between different surface features, to the best knowledge of the authors.",

author = "Abd, {Moaed A.} and Mostapha Al-Saidi and Maohua Lin and Genevieve Liddle and Kunal Mondal and Engeberg, {Erik D.}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020 ; Conference date: 29-11-2020 Through 01-12-2020",

year = "2020",

month = nov,

doi = "10.1109/BioRob49111.2020.9224294",

language = "English",

series = "Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics",

publisher = "IEEE Computer Society",

pages = "1174--1179",

booktitle = "2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020",

}

Abd, MA, Al-Saidi, M, Lin, M, Liddle, G, Mondal, K & Engeberg, ED 2020, Surface Feature Recognition and Grasped Object Slip Prevention with a Liquid Metal Tactile Sensor for a Prosthetic Hand. in 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020., 9224294, Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, vol. 2020-November, IEEE Computer Society, pp. 1174-1179, 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020, New York City, United States, 11/29/20. https://doi.org/10.1109/BioRob49111.2020.9224294

Surface Feature Recognition and Grasped Object Slip Prevention with a Liquid Metal Tactile Sensor for a Prosthetic Hand. / Abd, Moaed A.; Al-Saidi, Mostapha; Lin, Maohua et al.
2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020. IEEE Computer Society, 2020. p. 1174-1179 9224294 (Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics; Vol. 2020-November).

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

TY - GEN

T1 - Surface Feature Recognition and Grasped Object Slip Prevention with a Liquid Metal Tactile Sensor for a Prosthetic Hand

AU - Abd, Moaed A.

AU - Al-Saidi, Mostapha

AU - Lin, Maohua

AU - Liddle, Genevieve

AU - Mondal, Kunal

AU - Engeberg, Erik D.

PY - 2020/11

Y1 - 2020/11

N2 - There is a need to gather rich, real-time tactile information to enhance prosthetic hand performance during object manipulation. To that end, a highly stretchable liquid metal tactile sensor was designed, manufactured, and integrated into the fingertip of an i-limb Ultra prosthetic hand. With this novel tactile sensor, the feasibility of real-time slip detection and prevention of a grasped object was demonstrated. Furthermore, this liquid metal tactile sensor was used to distinguish between five different surface patterns with high accuracy using three different classification algorithms: K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF). The K-nearest neighbors (KNN) classifier produced the highest classification accuracy of 98% to distinguish between five different surface textures. Taken together, this novel prosthetic fingertip tactile sensor has the potential to improve grasp control and object manipulation operations for upper limb amputees. Additionally, this paper documents the first time that a liquid metal tactile sensor has been used to distinguish between different surface features, to the best knowledge of the authors.

AB - There is a need to gather rich, real-time tactile information to enhance prosthetic hand performance during object manipulation. To that end, a highly stretchable liquid metal tactile sensor was designed, manufactured, and integrated into the fingertip of an i-limb Ultra prosthetic hand. With this novel tactile sensor, the feasibility of real-time slip detection and prevention of a grasped object was demonstrated. Furthermore, this liquid metal tactile sensor was used to distinguish between five different surface patterns with high accuracy using three different classification algorithms: K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF). The K-nearest neighbors (KNN) classifier produced the highest classification accuracy of 98% to distinguish between five different surface textures. Taken together, this novel prosthetic fingertip tactile sensor has the potential to improve grasp control and object manipulation operations for upper limb amputees. Additionally, this paper documents the first time that a liquid metal tactile sensor has been used to distinguish between different surface features, to the best knowledge of the authors.

UR - http://www.scopus.com/inward/record.url?scp=85095573758&partnerID=8YFLogxK

U2 - 10.1109/BioRob49111.2020.9224294

DO - 10.1109/BioRob49111.2020.9224294

M3 - Conference contribution

AN - SCOPUS:85095573758

T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics

SP - 1174

EP - 1179

BT - 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020

PB - IEEE Computer Society

T2 - 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020

Y2 - 29 November 2020 through 1 December 2020

ER -

Abd MA, Al-Saidi M, Lin M, Liddle G, Mondal K, Engeberg ED. Surface Feature Recognition and Grasped Object Slip Prevention with a Liquid Metal Tactile Sensor for a Prosthetic Hand. In 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020. IEEE Computer Society. 2020. p. 1174-1179. 9224294. (Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics). doi: 10.1109/BioRob49111.2020.9224294

Surface Feature Recognition and Grasped Object Slip Prevention with a Liquid Metal Tactile Sensor for a Prosthetic Hand

Abstract

Publication series

Conference

Funding

Access to Document

Other files and links

Fingerprint

Cite this