Classification of deadlift biomechanics with wearable inertial measurement units |
| |
Institution: | 1. Insight Centre for Data Analytics, University College Dublin, Ireland;2. School of Public Health, Physiotherapy and Sports Science, University College Dublin, Ireland;3. Insight Centre for Data Analytics, Maynooth University, Ireland;1. Faculty of Medicine and Health Sciences, Universitat Internacional de Catalunya. Carrer de Josep Trueta, 08195, Sant Cugat del Vallès, Barcelona, Spain;2. ACTIUM Anatomy Group. Carrer de Josep Trueta, 08195, Sant Cugat del Vallès, Barcelona, Spain;3. Physiotherapy Department, Faculty of Health Sciences, European University of Gasteiz - EUNEIZ, C/ La biosfera Ibilbidea, 6, 01013, Gasteiz, Álava, Spain;4. Fundació Institut Universitari per a la recerca a l''Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain;5. Movement Lab BCN, Barcelona, Spain;1. Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran;2. Division of Applied Mechanics, Department of Mechanical Engineering, Polytechnique Montréal, Québec, Canada;1. Charles Darwin University, Physiolytics Laboratory, School of Psychological and Clinical Sciences, PO Box 40146, Casuarina, NT 0811, Australia;2. SABEL Labs, Griffith School of Engineering, Nathan Campus, Griffith University, 170 Kessels Road, Nathan, Brisbane, Queensland 4111, Australia;1. Sport and Exercise Sciences Research Unit, University of Palermo, Italy;2. Environmental Ergonomics Research Centre, Loughborough University, UK;3. Department of Biomedical Sciences, Physiological Laboratory, University of Padua, Italy |
| |
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 51.2 Hz, 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. |
| |
Keywords: | Wearable sensors Biomedical technology Lower extremity Inertial measurement units |
本文献已被 ScienceDirect 等数据库收录! |
|