NDLI: A novel predictive approach to prosthetic control for digit amputations using grip classification

Content Provider	IEEE Xplore Digital Library
Author	Ganesh, A.C. Vale, V.
Copyright Year	2013
Description	Author affiliation: Online High Sch., Pre-Coll. Studies, Stanford Univ., Stanford, CA, USA (Ganesh, A.C.; Vale, V.)
Abstract	In this paper, the authors present a novel method of prosthetic control for amputees who are missing a subset of their digits. The core of the system is a personalized set of basic configurations of the human hand to facilitate given tasks, referred to as use-patterns. In the concept, the system first senses the positions of the remaining, healthy digits using resistive flex sensors mounted onto a glove. Then, an algorithm using a Z-score metric compares the flex sensor readings to a large set of training data to determine which of several use-patterns the current situation corresponds to. A control system can then move a bionic digit into the intended position corresponding to the use-pattern detected, completing the grip. MATLAB simulations were run to determine the accuracy of this concept on five exemplary use-patterns. The success rate with one amputated digit was 96.5% and the success rate with two amputated digits was 92.5%. This demonstrates the feasibility of a simple, intuitive, and low-cost prosthetic control system based on this premise that could deliver even higher success rates when personalized with self-learning algorithms.
Starting Page	724
Ending Page	729
File Size	1441596
Page Count	6
File Format	PDF
ISSN	23250690
e-ISBN	9781479925193
DOI	10.1109/ICAMechS.2013.6681737
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Z Score Digit Resistive Flex Sensors Vectors Hand Standards Robotics Grip Predictive Classification Amputation Electromyography Sensors Robots Prosthetics
Content Type	Text
Resource Type	Article

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