Online Fault Detection Approach of Unpredictable Inputs: Application to Handwriting System
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-03
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Many investigators are interested in improving the control strategies of hand prosthesis to make it functional and more convenient to use.
The most used control approach is based on the forearm muscles activities, named ‘ElectroMyoGraphic’ (EMG) signal.
However, these biological signals are very sensitive to many disturbances and are generally unpredictable in time, type, and level.
This leads to inaccurate identification of user intent and threatens the prosthesis control reliability.
This paper proposed a real-time fault detection and localization approach applied to handwriting device on the plane.
This approach allows connecting inputs (IEMG signals)/outputs (pen tip coordinates) data as a parametric model for Multi-Inputs Multi-Outputs (MIMO) system.
The proposed approach is considered as a model-independent abrupt or intermittent fault detection method and as an alternative solution to the unpredictable input observer based techniques, without any observability requirements.
This approach allows detecting, in real time, several types of faults in one or two inputs signals and in the same or different instants.
Our study is appropriate for many rapidly expanding fields and practices, including biomedical engineering, robotics, and biofeedback therapy or even military applications.
American Psychological Association (APA)
Chihi, Ines& Benrejeb, Mohamed. 2018. Online Fault Detection Approach of Unpredictable Inputs: Application to Handwriting System. Complexity،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1136936
Modern Language Association (MLA)
Chihi, Ines& Benrejeb, Mohamed. Online Fault Detection Approach of Unpredictable Inputs: Application to Handwriting System. Complexity No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1136936
American Medical Association (AMA)
Chihi, Ines& Benrejeb, Mohamed. Online Fault Detection Approach of Unpredictable Inputs: Application to Handwriting System. Complexity. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1136936
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136936