Page 73 - THEi Student Applied Research Presentations 2024

BIOMECHANICAL ANALYSIS IN LOWER LIMB
ALIGNMENT IN PREDICTING RUNNING-RELATED

INJURIES VIA MACHINE LEARNING

ABSTRACT Poster

RESEARCH BACKGROUND

The global popularity in running has resulted in an increase in running-related injuries.
These injuries affect a significant portion of the running community and highlight the
need for advanced biomechanical analysis in injury prevention and management.

METHODOLOGY
Employing MotionMetrix technology, the study collects detailed biomechanical data YAM HO PONG AVERY
on the lower limb alignment of the runners. Machine learning models—specifically
RandomForestClassifier, XGBClassifier, and SVM—are applied to the collected data BSocSc (Hons) in Sports and
and identify patterns and predictors of injury risk. There were 59 participants in this Recreation Management
study, 44 males and 15 females aged 27-53, with at least 10km running experience. Department of Sport and Recreation

FINDINGS
Model Findings through Training and Prediction Process: Among the machine learning
models used, the XGBClassifier emerged as the most accurate, with a mean accuracy of
0.6844 and precision of 0.7371; thus, XGBClassifier is effective in predicting accurate OBJECTIVES
true positives against false positives and negatives.
Important Features and their Insights: Right-side hip abduction strength and right-side Utilizing MotionMetrix technology for
knee alignment were identified as significant features across all models, highlighting non-invasive biomechanical data collection
their pivotal role in predicting running-related injuries. The results proves that these and machine learning for data analysis,
biomechanical factors are crucial for understanding injury risk among runners. this study seeks to identify key predictors
Their significance likely stems from their impact on running mechanics and stress of injury risk, with a particular focus on
distribution in the musculoskeletal system. Features that show a strong correlation lower limb alignment.
with the outcome across various scenarios within the training data tend to be weighted
more heavily. For example, hip abduction strength and knee alignment might directly
influence running posture, leading to a higher injury risk, hence their significance in
the models.

ABOUT THE INVESTIGATOR

As a sports therapist, I would like to offer tailored rehabilitation services to athletes,
focusing on their rapid recovery and return to peak performance. Ambitiously, I aspire
to establish a sports therapy clinic in Hong Kong dedicated to providing top-tier
services accessible to everyone, aiming to break barriers and ensure that high-quality
sports therapy is available to individuals from all walks of life. My supervisor is Mr. HO
Man Kit, Indy.

Student Applied Research Presentations 2024 Student Applied Research Presentations 2024 73