Date of Award

6-2024

Degree Name

MS in Mechanical Engineering

Department/Program

Mechanical Engineering

College

College of Engineering

Advisor

Eric Espinoza-Wade

Advisor Department

Mechanical Engineering

Advisor College

College of Engineering

Abstract

Stroke is a chronic illness which often impairs survivors for extended periods of time,

leaving the individual limited in motor function. The ability to perform daily activities

(ADL) is closely linked to motor recovery following a stroke. The objective of

this work is to employ surface electromyography (sEMG) gathered through a novel,

wearable armband sensor to monitor and quantify ADL performance. The first contribution

of this work seeks to develop a relationship between sEMG and and grip

aperture, a metric tied to the success of post-stroke individuals’ functional independence.

The second contribution of this work aims to develop a deep learning model

to classify RTG movements in the home setting using continuous EMG and acceleration

data. In contribution one, ten non-disabled participants (10M, 22.5 0.5 years)

were recruited. We performed a correlation analysis between aperture and peak EMG

value, as well as a one-way non parametric analysis to determine cylinder diameter

effect on aperture. In contribution two, one non-disabled participant is instructed to

wash a set of dishes. The EMG and acceleration data collected is input into a recurrent

neural network (RNN) machine learning model to classify movement patterns.

The first contribution’s analysis demonstrated a strong positive correlation between

aperture and peak EMG value, as well as a statistically significant effect of diameter

(p < 0.001). The RNN model built in contribution two demonstrated high capability

at classifying movement at 94% accuracy and an F1-score of 86%. These results

demonstrate promising feasibility for long-term, in-home classification of daily tasks.

Future applications of this approach should consider extending the procedure to

include post-stroke individuals, as this could offer valuable insight into motor recovery

within the home setting.

Award received:

Presentation at IEEE 46th Annual EMBC Conference

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