Page 69 - THEi Student Applied Research Presentations 2024

THE ESTIMATION OF RUNNING ECONOMY AND
MOTION ANALYSIS OF RUNNERS USING MACHINE

LEARNING ON MOTIONMETRIX DATA

ABSTRACT Poster

RESEARCH BACKGROUND

This study investigates whether there is a particular pattern among runners in achieving
efficient running economy and running performance. Could there be a relation between
the running biomechanisms and what would be the potential intrinsic factors affecting
the running economy?

METHODOLOGY THAPA CELCIA
A total of 59 athletes, aged 21 to 53 years, participated in this study conducted at
RunLap Research Centre. Subjects were asked to perform the following tasks: (1) Foot BSocSc (Hons) in Sports and
posture index, (2) Ankle dorsiflexion range of motion (ROM), (3) Reactive strength Recreation Management
index (RSI), (4) Single-leg squat, (5) Hip abduction strength, and (6) Running gait Department of Sport and Recreation
assessment (MotionMetrix).

FINDINGS

Out of the three machine learning models (XGB, SVM, ANN), the study demonstrated
that XGB is the most accurate for predicting running performance. Moreover, the OBJECTIVES
calculated cross-validated mean absolute error (MAE) of 3.757 and MAE of 4.371 fits
under the 10% potential error acceptance range in predicting the running duration. The To use MotionMetrix to analyse the
most significant features of importance were training pace, weekly distance, right knee running motion through kinematics,
alignment, and overstride. such as joint loading, gait symmetry,
and stride length. Furthermore, with
ABOUT THE INVESTIGATOR the data collected, this research aims
to identify the influential factors or
I’m THAPA Celcia. As an active sportsperson, I enjoy the thrill and all the other emotions patterns among runners on optimal
sports give me. Hence, I genuinely like helping people, whether at the clinic or during running performance and the economy
on-field support. Moreover, when working with sports teams, the atmosphere is one of running.
of the reasons that drives me to push myself further as a sports therapist. To my
supervisor, Mr Indy HO, thank you for broadening my horizons by introducing machine
learning to me through this research project.

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