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Multimodal myocontrol for bimanual manipulation

Engel, Sarah (2017) Multimodal myocontrol for bimanual manipulation. Master's. DLR-Interner Bericht. DLR-IB-RM-OP-2017-77, 97 S.

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Abstract

Human hands provide multiple functions like powerful and precise grasping and are part of interpersonal communication. Thus the loss of an upper limb affects everyday life. Intuitively controllable prostheses have the aim to replace the missing functionality. Reliability of the myocontrol system is the key criterion to develop user-oriented prostheses. Simultaneous and proportional control using muscle signals realizes intuitive control. However reliability is still missing. One solution to solve this issue is incremental learning. This approach takes care of unreliable performance by allowing model updates on-demand, for instance whenever the user wants to teach the system or whenever a failure occurred. Thus current incremental learning methods lead to interactive myocontrol; their drawback, however, is the need to stop the prediction and update the model manually. An automatic detection offailures, as reliable as possible, is desirable. By developing a new method called observer model we seek to automatically identify failures of the performance of the myocontrol system. The observer model is fed with multimodal data and according observer’s labels. An offline analysis on training data includes extraction of time- and frequency domain features and classification according to their class affiliation. This classifier is used to detect poor performance of the myocontrol system. Recognition rate is used as a measure for the performance of the classifier; it gives a percentage of concurrence of actual and predicted labels. An experiment modeling daily-life activities was conducted for 11 subjects. We see an average outcome of a 60.3% recognition rate as a proof for the possibility to detect failures of the myocontrol system that leaves space for further improvement. It opens the door to further investigations towards automatic failure detection to aim reliable myocontrol.

Item URL in elib:https://elib.dlr.de/113025/
Document Type:Monograph (DLR-Interner Bericht, Master's)
Title:Multimodal myocontrol for bimanual manipulation
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Engel, SarahSarah.Engel (at) dlr.deUNSPECIFIED
Date:2017
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:97
Status:Published
Keywords:surface electromyography, prosthetics, rehabilitation robotics, machine learning
Institution:ulm university
Department:Faculty of Engineering, Computer Science and Psychology
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Intelligente Mobilität
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Cognitive Robotics
Deposited By: Nowak, Markus
Deposited On:17 Jul 2017 13:10
Last Modified:17 Jul 2017 13:10

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