Mild traumatic brain injury (mTBI), or concussion, results from sudden acceleration
or deceleration of the brain and subsequent complex tissue propagation of shock waves
that disrupt structure and function. Concussions can cause many symptoms including
headache, dizziness, and difficulty concentrating. These can be detrimental to children,
a ecting their participation in school, sport, and social activities. Therefore,
return to school (RTS) and return to activity (RTA) protocols have been developed to
help safely return children to these activities without risking further injury. The goal
of this study was to develop machine learning (ML) algorithms to predict RTA and
RTS stages, that can easily be incorporated into a smartphone application (APP).
Ideally this would assist children in tracking and determining their RTA and RTS
progression leading them to a safe and timely return.
Support vector machine classi er (SVC) and random forest (RF) algorithms were
developed to predict RTA/RTS stages. Both were modeled on previously acquired
data, and on newly acquired data, and results were compared. Models were trained
and tested using accelerometry and symptom data from pediatric concussion patients.
A sliding window technique and feature extraction were performed on raw acceleration
data to extract suitable features, which were combined with yes/no symptom
recordings as ML inputs. The dataset consisted of 67 participants aged 10 to 18, 42
female and 25 male, with a total of 844408 samples.
The best results for RTS prediction showed average accuracy of 83% for RF and
66% for SVC. For RTA predictions, the best results had average accuracy of 60% for
RF and 58% for SVC. For new data, RTS predictions showed an accuracy of 45%
for RF and 41% for SVC. RTA predictions had an accuracy of 35% for RF and 30%
for SVC. RF models had superior performance on all data. These results show that
predicting RTA/RTS is possible with ML. However, improvements to these models
can be made by training on more data prior to APP implementation. More data is
needed, as recruitment during this study was limited due to Covid-19 restrictions. / Thesis / Master of Applied Science (MASc) / Concussions are recorded in approximately 300,000 athletes annually and are estimated
to a ect up to 3.8 million individuals per year in the United States alone.
Understanding when its safe to return to normal routine after an injury is important
but challenging. Therefore, a series of stages have been developed to lead children
through a safe and timely return to sport and activity after concussion. The goal
of this study was to develop machine learning (ML) algorithms which predict these
return stages using symptom recordings and gross body movement data. Algorithms
could be incorporated into a smartphone application (APP) to provide accessible return
guidelines for children with concussions. Algorithms were created and model
performance was tested using symptom and body movement data collected from children
after a concussive injury. The results of this study show that it is possible to
predict return to school and return to activity stages with ML, and with improvements,
can be used to facilitate return from injury
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27339 |
| Date | January 2021 |
| Creators | Anderson, Lauren |
| Contributors | Noseworthy, Michael, Biomedical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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