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.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-4553 |
| Date | 01 June 2024 |
| Creators | Dodd, Nathan |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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