Page 65 - THEi Student Applied Research Presentations 2024

PREDICT IMPORTANT PARAMETERS OF
GROUND REACTION FORCES AND RUNNING

INJURY PATTERNS IN BIOMECHANICAL

ANALYSIS USING MOTIONMETRIX AND
MACHINE LEARNING

ABSTRACT

RESEARCH BACKGROUND
Running, while highly beneficial to health, is typically linked to a significant risk of
injury. Each year, a significant percentage of runners sustain injuries as a result of
rigorous training programs and repetitive stress. However, there is new technology MAK KA HO
developments for more effective injury prevention and management strategies.
BSocSc (Hons) in Sports and
METHODOLOGY Recreation Management
The study involved 65 marathon runners, with the project aims, protocols, and Department of Sport and Recreation
procedures explained to them. Participants were invited to participate in Run Lap.
Data were collected by questionnaire, MotionMetrix and clinical test. Data were then
analysed and the Machine Learning Model was built.

FINDINGS OBJECTIVES

The Random Forest Classifier identified 8 features related to injury patterns related to
marathon runners. Initially, a set of four features related to ground reaction force was The objectives of the research project
evaluated: 'Vertical Force', 'Lateral Force', 'Contact Time', and 'Braking Force', across are:
three machine learning models. Subsequently, four additional features were included: • Identify the injury patterns which
'Weekly Distance ', 'Hip Abduction Strength', 'Knee Alignment', and 'Knee-to-Wall affect marathon runners
Distance'. When predicting the cause of injury, the 4 ground reaction force factors has • To what extent can ground reaction
lower accuracy than that of the other 4 features. The important features identified by force (GRF) be used as a predictive
the machine learning models are knee alignment, hip abduction strength, and weekly parameter for running injuries via
distance history. machine learning

ABOUT THE INVESTIGATOR

My name is MAK Ka Ho.
My supervisor is Mr Indy HO Man Kit.

Student Applied Research Presentations 2024 Student Applied Research Presentations 2024 65