Page 63 - THEi Student Applied Research Presentations 2024

MACHINE LEARNING-BASED PREDICTION OF
RUNNING INJURIES: POOLED ANALYSIS OF

MOTIONMETRIX DATA AND SCREENING TESTS

ABSTRACT Poster

RESEARCH BACKGROUND

Running-related injuries, particularly overuse injuries, are significant concerns for
runners. Relying solely on traditional measures like running volume oversimplifies
the assessment of training stress, often neglecting important factors such as ground
reaction force and foot-strike pattern. Instead of the linear and unidirectional causality
view of sports injury etiology, the complex system perspective proposed a multifactorial
nature of injuries, emphasizing unknown interactions and varying weights among LI MEI HUNG
features.
BSocSc (Hons) in Sports and
METHODOLOGY Recreation Management
A total of 59 well-trained long-distance runners were recruited. Demographic Department of Sport and Recreation
information, running behavior, and injury history were collected through the survey.
Participants completed a 1-minute MotionMetrix running assessment at 12 km/h with
0% inclination, and three screening tests, including the knee-to-wall lunge test, hip
abduction strength test, and single-leg squat test. One hot encoding and oversampling
techniques were employed for data preprocessing. Machine learning models, including OBJECTIVES
random forest, support vector machine, and extreme gradient boost, were implemented
in Python. The dataset was split into 80% for training and 20% for testing, and k-fold The current study integrated survey
cross-validation was utilized. data, screening tests, and the joint
kinematics and kinetics results from
FINDINGS the MotionMetrix markerless running
The XGBoost model exhibited the highest evaluation of accuracy (0.717), F-1 score assessment system, to identify the
(0.759), and AUC (0.735), which suggested its great accuracy and superior predictive high-importance features, and develop
and discriminative capabilities in identifying runners at risk of injury. The most a machine learning-based predictive
important features included weekly running distance, hip abduction strength, and model for assessing running injury risk.
running experience.

ABOUT THE INVESTIGATOR

Hey, I'm LI Mei Hung Parker, a Year-4 sports therapy student at SRM. Studying can be
enjoyable, but if it's not your thing, it's important to find something that truly excites
you. Lately, I've been embracing traveling and exploring life’s unpredictability. One of
my goals is to become a skilled and knowledgeable sports therapist. I'm passionate
about helping people have a blast while staying active and living their best, healthiest
lives.
Here's my super supervisor: Mr. Indy HO!

Student Applied Research Presentations 2024 Student Applied Research Presentations 2024 63