Classification of deadlift biomechanics with wearable inertial measurement units

O’Reilly, Martin; Whelan, Darragh; Ward, Tomás E.; Delahunt, Eamonn; Caulfield, Brian

doi:10.1016/j.jbiomech.2017.04.028

Cited by 45 publications

(51 citation statements)

References 13 publications

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“…Eleven studies investigated the utilisation of wearable IMU systems for quantifying exercise technique [3,19,23,64,[69][70][71][72][73][74][75]. Table 10 summarises the sensing set-ups, movement measure which was classified, methodology and performance metrics for each system identified in this area.…”

Section: Movement Classificationmentioning

confidence: 99%

“…Future work may also benefit from utilising deep learning techniques for classification such as the convolutional neural networks approach demonstrated by Veiga et al [68]. Such classification methodologies have recently been shown to have many benefits when compared to traditional machine learning classification techniques when analysing timeseries data, including reducing the risk of overfitting and improving system accuracy [77,78] (Table 10) [3,19,23,64,[70][71][72]74]. There is therefore potential to investigate movement classification with larger data sets and across a range of other exercises.…”

Section: Exercise Detection Systemsmentioning

confidence: 99%

“…Some recent studies also compare the effectiveness of global and personalised classification systems [73,74], whereby a personalised classification system is one which is trained from data from an individual and developed specifically for this individual and a global classification system is trained with data from many individuals and can be used by individuals not included in the training data. Analysis has shown that the personalised systems are more computationally efficient and accurate than global ones [73][74][75], which has enabled movement classification systems to be developed with single sensing unit rather than multiple sensing units. However, the time required to collect data from and train a classification system for every individual who requires a movement classification system is a significant practical limitation which may hinder the uptake of movement classification systems in clinical practice.…”

Section: Exercise Detection Systemsmentioning

confidence: 99%

“…STROBE = STrengthening the Reporting of OBservational studies in Epidemiology. [69], [60] Single sensor units (n=19) [14], [52], [58], [24], [53], [7] [67], [70], [71], [72], [73], [74] Charlton et al…”

Section: Inclusion Criteriamentioning

confidence: 99%

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Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

et al. 2018

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BackgroundAnalysis of lower limb exercises is traditionally completed with four distinct methods (i) 3D motion capture; (ii) depth-camera based systems (iii) visual analysis from a qualified exercise professional; (iv) self-assessment. Each method is associated with a number of limitations. ObjectiveThe aim of this systematic review is to synthesize and evaluate studies which have investigated the capacity for inertial measurement unit (IMU) technologies to assess movement quality in lower limb exercises. Data SourcesA systematic review of PubMed, ScienceDirect and Scopus was conducted. Study Eligibility CriteriaArticles written in English and published in the last 10 years which contained an IMU system for the analysis of repetition-based targeted lower limb exercises were included. Study Appraisal and Synthesis MethodsThe quality of included studies was measured using an adapted version of the STROBE assessment criteria for cross-sectional studies. The studies were categorised in to three groupings: exercise detection, movement classification or measurement validation. Each study was then qualitatively summarised. ResultsFrom the 2452 articles that were identified with the search strategies, 47 papers are included in this review. ConclusionsWearable inertial sensor systems for analysing lower limb exercises are a rapidly growing technology. Research over the past ten years has predominantly focused on validating measurements that the systems produce and classifying users' exercise quality. There have been very few user evaluation studies and no clinical trials in this field to date. Key PointsInertial measurement unit (IMU) systems have been extensively validated to successfully measure joint angle and temporal features during lower limb exercises.It is less understood if IMU systems can validly compute kinetic measures pertaining to lower limb exercises.IMU systems, which incorporate machine learning in to their data analysis pathways, have also been found to be effective in automated exercise detection and in classifying movement quality across a range of lower limb exercises.3

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Section: Movement Classificationmentioning

confidence: 99%

Section: Exercise Detection Systemsmentioning

confidence: 99%

Section: Exercise Detection Systemsmentioning

confidence: 99%

Section: Inclusion Criteriamentioning

confidence: 99%

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Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

et al. 2018

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“…10 Studies are now exploring the feasibility of using kinematic data to augment PT through virtual coaching of exercise form and activity tracking during home exercise. [11][12][13][14][15][16][17] Early implementations of biomechanical analysis during home rehabilitation show positive signs of user experience and rehabilitation outcomes. 11,18,19 However, presenting biomechanical data to nonprofessionals poses a major challenge.…”

Section: Introductionmentioning

confidence: 99%

Design of a Low-Cost, Wearable Device for Kinematic Analysis in Physical Therapy Settings

et al. 2020

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Unsupervised home exercise is a major component of physical therapy (PT). This study proposes an inexpensive, inertial measurement unit-based wearable device to capture kinematic data to facilitate exercise. However, conveying and interpreting kinematic data to non-experts poses a challenge due to the complexity and background knowledge required that most patients lack. Objectives The objectives of this study were to identify key user interface and user experience features that would likely improve device adoption and assess participant receptiveness toward the device. Methods Fifty participants were recruited to perform nine upper extremity exercises while wearing the device. Prior to exercise, participants completed an orientation of the device, which included examples of software graphics with exercise data. Surveys that measured receptiveness toward the device, software graphics, and ergonomics were given before and after exercise. Results Participants were highly receptive to the device with 90% of the participants likely to use the device during PT. Participants understood how the simple kinematic data could be used to aid exercise, but the data could be difficult to comprehend with more complex movements. Devices should incorporate wireless sensors and emphasize ease of wear. Conclusion Device-guided home physical rehabilitation can allow for individualized treatment protocols and improve exercise self-efficacy through kinematic analysis. Future studies should implement clinical testing to evaluate the impact a wearable device can have on rehabilitation outcomes.

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Interpretable Classification of Human Exercise Videos Through Pose Estimation and Multivariate Time Series Analysis

Singh

Nguyen

et al. 2022

Studies in Computational Intelligence

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Classification of deadlift biomechanics with wearable inertial measurement units

Cited by 45 publications

References 13 publications

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

Design of a Low-Cost, Wearable Device for Kinematic Analysis in Physical Therapy Settings

Interpretable Classification of Human Exercise Videos Through Pose Estimation and Multivariate Time Series Analysis

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