Estimating output torque via amplitude estimation and neural drive : a high-density sEMG study

The scope and relevance of wearable robotics spans across a number of research fields with a variety of applications. One such application is the augmentation of healthy individuals for improved performance. A challenge within this field is improving user-interface control. An established approach for improving user-interface control is neural control interfaces derived from surface electromyography (sEMG). This thesis presents an exploration of output joint torque estimation using high density surface electromyography (HDsEMG). The specific aims of this thesis were to implement a well-established amplitude estimation method for standard multi-electrode sEMG collection with an HDsEMG grid, and to take an existing blind source separation algorithm for HDsEMG decomposition and modify it in order to decompose a nonisometric contraction.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Leahy, Logan Patrick.

Advisor dc:contributor.advisor

• Neville Hogan.

• Mechanical Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127134

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127134