Simultaneous Multi-Joint Myoelectric Control of Transradial Prostheses

As the development of dexterous prosthetic hand and wrist units continues, there is a need for command interfaces that will enable a user to operate these multi-joint devices in a natural, coordinated manner. Commercially available myoelectric control algorithms are unintuitive, allow only a single degree of freedom to be operated at a time, and typically require some type of a mode switch to transition between operating the various functions of the system. Pattern recognition-based control algorithms take advantage of distinct patterns of activity related to the intuitive movements of the missing limb, thus reducing the cognitive burden associated with traditional control methods. To date, however, the majority of these advanced approaches still limit users to sequential operation of the various prosthesis functions. This project aimed to develop an intuitive myoelectric command interface that would allow multiple joints (forearm pronation-supination, wrist flexion-extension, and hand opening-closing) of a transradial prosthesis to be controlled in a highly coordinated fashion. We demonstrated than a time-delayed artificial neural network controller could be trained to accurately predict motions of several key hand and wrist motions using myoelectric signals recorded from muscles in the forearm offline. The controller was then evaluated in real-time during simulations of several tasks in a virtual reality environment. The motions of the “virtual prosthesis” are controlled by the user’s myoelectric signals, and the novel functional task simulator incorporates kinematic tracking of residual limb motions, dynamic simulations of prosthesis motions, and force-based interactions with objects in the virtual environment. The implemented control scheme showed that it is possible to simultaneously control multiple joints, reducing movement time and improving several subjects’ ability to perform tasks in the virtual environment when compared to a traditional control approach.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Biomedical Engineering

Grantor dc:publisher

Case Western Reserve University School of Graduate Studies

Year dc:date

2013

Author dc:creator

• Pulliam, Christopher Lee

Contributors dc:contributor

• Kirsch, Robert

• Biomedical Engineering

Statement dc:rights

• unrestricted
• This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.

Language dc:language

English

Repository record dc:identifier

http://rave.ohiolink.edu/etdc/view?acc_num=case1365001042

OAI identifier oai:identifier

oai:etd.ohiolink.edu:case1365001042