Type

Journal Article

Authors

Brian M Caulfield
Eamonn Delahunt
Tomas E Ward
Darragh F Whelan
Martin A O'Reilly

Subjects

Physiotherapy & Sport

Topics
sensitivity and specificity wearable resistance training biomechanics binary classification classification lumbar spine inertial measurement

Classification of deadlift biomechanics with wearable inertial measurement units. (2016)

Abstract The deadlift is a compound full-body exercise that is fundamental in resistance training, rehabilitation programs and powerlifting competitions. Accurate quantification of deadlift biomechanics is important to reduce the risk of injury and ensure training and rehabilitation goals are achieved. This study sought to develop and evaluate deadlift exercise technique classification systems utilising Inertial Measurement Units (IMUs), recording at 51.2Hz, worn on the lumbar spine, both thighs and both shanks. It also sought to compare classification quality when these IMUs are worn in combination and in isolation. Two datasets of IMU deadlift data were collected. Eighty participants first completed deadlifts with acceptable technique and 5 distinct, deliberately induced deviations from acceptable form. Fifty-five members of this group also completed a fatiguing protocol (3-Repition Maximum test) to enable the collection of natural deadlift deviations. For both datasets, universal and personalised random-forests classifiers were developed and evaluated. Personalised classifiers outperformed universal classifiers in accuracy, sensitivity and specificity in the binary classification of acceptable or aberrant technique and in the multi-label classification of specific deadlift deviations. Whilst recent research has favoured universal classifiers due to the reduced overhead in setting them up for new system users, this work demonstrates that such techniques may not be appropriate for classifying deadlift technique due to the poor accuracy achieved. However, personalised classifiers perform very well in assessing deadlift technique, even when using data derived from a single lumbar-worn IMU to detect specific naturally occurring technique mistakes.
Collections Ireland -> Maynooth University -> PubMed

Full list of authors on original publication

Brian M Caulfield, Eamonn Delahunt, Tomas E Ward, Darragh F Whelan, Martin A O'Reilly

Experts in our system

1
Brian Caulfield
University College Dublin
Total Publications: 269
 
2
Eamonn Delahunt
University College Dublin
Total Publications: 114
 
3
Tomas Ward
Maynooth University
Total Publications: 165
 
4
Darragh F Whelan
Maynooth University
Total Publications: 13
 
5
Martin O'Reilly
University College Dublin
Total Publications: 16