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Classification of deadlift biomechanics with wearable inertial measurement units

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 achiev... Full description

Journal Title: Journal of biomechanics 2017, Vol.58, p.155-161
Main Author: O'Reilly, Martin A
Other Authors: Whelan, Darragh F , Ward, Tomas E , Delahunt, Eamonn , Caulfield, Brian M
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: United States: Elsevier Ltd
ID: ISSN: 0021-9290
Link: https://www.ncbi.nlm.nih.gov/pubmed/28545824
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recordid: cdi_proquest_miscellaneous_1903170579
title: Classification of deadlift biomechanics with wearable inertial measurement units
format: Article
creator:
  • O'Reilly, Martin A
  • Whelan, Darragh F
  • Ward, Tomas E
  • Delahunt, Eamonn
  • Caulfield, Brian M
subjects:
  • Aberration
  • Acceptability
  • Adult
  • Analysis
  • Biomechanical Phenomena
  • Biomechanics
  • Biomedical technology
  • Classification
  • Classification systems
  • Classifiers
  • Collection
  • Data sets
  • Datasets
  • Errors
  • Exercise - physiology
  • Experiments
  • Female
  • Forest management
  • Health risks
  • Humans
  • Inertial measurement units
  • Injuries
  • Injury prevention
  • International conferences
  • Lower extremity
  • Lumbar Vertebrae - physiology
  • Male
  • Measurement
  • Monitoring, Physiologic - instrumentation
  • Monitoring, Physiologic - methods
  • Motion capture
  • Physical Medicine and Rehabilitation
  • Physical therapy
  • Physical training
  • Recording
  • Rehabilitation
  • Resistance training
  • Risk assessment
  • Risk reduction
  • Sensitivity
  • Sensitivity analysis
  • Sensors
  • Signal processing
  • Spine
  • Spine (lumbar)
  • Sports sciences
  • Systems analysis
  • Therapeutics, Physiological
  • Training
  • Wearable sensors
  • Wearable technology
  • Weight training
  • Young Adult
ispartof: Journal of biomechanics, 2017, Vol.58, p.155-161
description: 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.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0021-9290
fulltext: fulltext
issn:
  • 0021-9290
  • 1873-2380
url: Link


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descriptionAbstract 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.
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subjectAberration ; Acceptability ; Adult ; Analysis ; Biomechanical Phenomena ; Biomechanics ; Biomedical technology ; Classification ; Classification systems ; Classifiers ; Collection ; Data sets ; Datasets ; Errors ; Exercise - physiology ; Experiments ; Female ; Forest management ; Health risks ; Humans ; Inertial measurement units ; Injuries ; Injury prevention ; International conferences ; Lower extremity ; Lumbar Vertebrae - physiology ; Male ; Measurement ; Monitoring, Physiologic - instrumentation ; Monitoring, Physiologic - methods ; Motion capture ; Physical Medicine and Rehabilitation ; Physical therapy ; Physical training ; Recording ; Rehabilitation ; Resistance training ; Risk assessment ; Risk reduction ; Sensitivity ; Sensitivity analysis ; Sensors ; Signal processing ; Spine ; Spine (lumbar) ; Sports sciences ; Systems analysis ; Therapeutics, Physiological ; Training ; Wearable sensors ; Wearable technology ; Weight training ; Young Adult
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descriptionAbstract 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.
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28Monitoring, Physiologic - instrumentation
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30Motion capture
31Physical Medicine and Rehabilitation
32Physical therapy
33Physical training
34Recording
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36Resistance training
37Risk assessment
38Risk reduction
39Sensitivity
40Sensitivity analysis
41Sensors
42Signal processing
43Spine
44Spine (lumbar)
45Sports sciences
46Systems analysis
47Therapeutics, Physiological
48Training
49Wearable sensors
50Wearable technology
51Weight training
52Young Adult
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11Data sets
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abstractAbstract 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.
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pmid28545824
doi10.1016/j.jbiomech.2017.04.028
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