Abstract
Recent data science advances have provided powerful methods
to utilise biomechanical data sets through machine learning
(ML) techniques. This includes classification of various
populations from discriminating kinematic, kinetic, and
electromyography (EMG) measures, particularly using support
vector machines (SVM) [1]. Despite this, there is limited SVM
literature on athletic characteristics. Since muscle fibre type is
linked to exercise-specific adaptations [2], SVM classification
of athlete types may provide insight into these adaptations. Our
previous work used a forward selection approach in SVM
models to distinguish between strength- and endurance-trained
athletes, identifying the tibialis anterior (TA) as the most
discriminatory muscle with 82.83% accuracy in the best performing
model using only TA EMG features [3]. Building
upon and extending these previous findings, this study used
Principal Component Analysis (PCA) to examine differences in
TA EMG stance phase patterns between these same strengthcand
endurance-trained athletes.
to utilise biomechanical data sets through machine learning
(ML) techniques. This includes classification of various
populations from discriminating kinematic, kinetic, and
electromyography (EMG) measures, particularly using support
vector machines (SVM) [1]. Despite this, there is limited SVM
literature on athletic characteristics. Since muscle fibre type is
linked to exercise-specific adaptations [2], SVM classification
of athlete types may provide insight into these adaptations. Our
previous work used a forward selection approach in SVM
models to distinguish between strength- and endurance-trained
athletes, identifying the tibialis anterior (TA) as the most
discriminatory muscle with 82.83% accuracy in the best performing
model using only TA EMG features [3]. Building
upon and extending these previous findings, this study used
Principal Component Analysis (PCA) to examine differences in
TA EMG stance phase patterns between these same strengthcand
endurance-trained athletes.
| Original language | English |
|---|---|
| Publication status | Published - 02 Dec 2024 |
| Event | Combined Scientific Meetings of the Australian and New Zealand Society of Biomechanics (ANZSB) & the Australian and New Zealand Orthopaedic Research Society (ANZORS): Biomechanics meets Biology in Orthopaedics - Swinburne University of Technology, Melbourne, Australia Duration: 01 Dec 2024 → 04 Dec 2024 Conference number: 14 https://abc14.com.au/ |
Conference
| Conference | Combined Scientific Meetings of the Australian and New Zealand Society of Biomechanics (ANZSB) & the Australian and New Zealand Orthopaedic Research Society (ANZORS) |
|---|---|
| Abbreviated title | Technology and clinical innovation in Biomechanics and Orthopaedics |
| Country/Territory | Australia |
| City | Melbourne |
| Period | 01/12/24 → 04/12/24 |
| Internet address |