Northwestern University

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Dimensionality Reduction for Prosthetic Hand Control

Description:

In this document, I demonstrate that: 1) Linear basis functions cannot outperform nonlinear ones to represent hand kinematics 2) Nonlinear autoencoders outperform PCA on the dimensionality reduction of hand kinematics, 3) Nonlinear autoencoders outperform PCA in human gait representation and recurrent nonlinear autoencoders can seamlessly express the temporal dynamics, 4)...

Keyword:

kinematics, prosthetics, machine learning, gait, dimensionality reduction, and hand

Subject:

Engineering

Creator:

Portnova-Fahreeva, Alexandra

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

09/22/2022

Date Created:

2022-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:16176 and etdadmin_upload_920776