Classification of squat quality with inertial measurement units in the single leg squat mobility test

Kianifar, Rezvan; Lee, Alex; Raina, Sachin; Kulić, Dana

doi:10.1109/embc.2016.7592162

Cited by 20 publications

(23 citation statements)

References 12 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%

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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%

“…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%

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

et al. 2018

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“…In the pilot data analysis [14] , we found the ankle IR features to be the best predictors of the DKV, which led us to suggest that it is possible to use only one sensor on the tibia (saving time and simplifying the test protocol) and still have good classification accuracy. To confirm this hypothesis with the larger datasets, we used feature selection on only ankle extracted features (90 out of 210 features) and found that ankle IR velocity, angle and acceleration, as well as ankle Add velocity features are the best predictors in the absence of hip or knee information.…”

Section: Resultsmentioning

confidence: 89%

“…In our pilot study [14] , we classified SLS performance as “poor,” “moderate” or “good” based on clinically understandable features. In the study, 3 IMUs were attached to the shank, thigh and low back of 7 healthy volunteers who performed 5 consecutive repetitions of SLS.…”

Section: Related Workmentioning

confidence: 99%

Automated Assessment of Dynamic Knee Valgus and Risk of Knee Injury During the Single Leg Squat

Kianifar

Lee²,

Raina³

et al. 2017

IEEE J. Transl. Eng. Health Med.

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Many clinical assessment protocols of the lower limb rely on the evaluation of functional movement tests such as the single leg squat (SLS), which are often assessed visually. Visual assessment is subjective and depends on the experience of the clinician. In this paper, an inertial measurement unit (IMU)-based method for automated assessment of squat quality is proposed to provide clinicians with a quantitative measure of SLS performance. A set of three IMUs was used to estimate the joint angles, velocities, and accelerations of the squatting leg. Statistical time domain features were generated from these measurements. The most informative features were used for classifier training. A data set of SLS performed by healthy participants was collected and labeled by three expert clinical raters using two different labeling criteria: “observed amount of knee valgus” and “overall risk of injury”. The results showed that both flexion at the hip and knee, as well as hip and ankle internal rotation are discriminative features, and that participants with “poor” squats bend the hip and knee less than those with better squat performance. Furthermore, improved classification performance is achieved for females by training separate classifiers stratified by gender. Classification results showed excellent accuracy, 95.7 % for classifying squat quality as “poor” or “good” and 94.6% for differentiating between high and no risk of injury.

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“…Research in this field has shown the ability of multiple body-worn IMUs to evaluate exercise quality for a variety of exercises [ 11 - 14 ]. These range from early-stage rehabilitation exercises such as heel slides and straight leg raises [ 15 ] to more complex late-stage rehabilitation exercises or S&C exercises such as bodyweight squats [ 16 ], lunges [ 17 ], and single-leg squats [ 18 - 20 ]. More cost-effective and practical systems using a single body-worn IMU have also been shown to be effective in the analysis of exercise technique [ 17 , 18 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation

O’Reilly¹,

Duffin²,

Ward³

et al. 2017

JMIR Rehabil Assist Technol

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BackgroundBiofeedback systems that use inertial measurement units (IMUs) have been shown recently to have the ability to objectively assess exercise technique. However, there are a number of challenges in developing such systems; vast amounts of IMU exercise datasets must be collected and manually labeled for each exercise variation, and naturally occurring technique deviations may not be well detected. One method of combatting these issues is through the development of personalized exercise technique classifiers.ObjectiveWe aimed to create a tablet app for physiotherapists and personal trainers that would automate the development of personalized multiple and single IMU-based exercise biofeedback systems for their clients. We also sought to complete a preliminary investigation of the accuracy of such individualized systems in a real-world evaluation.MethodsA tablet app was developed that automates the key steps in exercise technique classifier creation through synchronizing video and IMU data collection, automatic signal processing, data segmentation, data labeling of segmented videos by an exercise professional, automatic feature computation, and classifier creation. Using a personalized single IMU-based classification system, 15 volunteers (12 males, 3 females, age: 23.8 [standard deviation, SD 1.8] years, height: 1.79 [SD 0.07] m, body mass: 78.4 [SD 9.6] kg) then completed 4 lower limb compound exercises. The real-world accuracy of the systems was evaluated.ResultsThe tablet app successfully automated the process of creating individualized exercise biofeedback systems. The personalized systems achieved 89.50% (1074/1200) accuracy, with 90.00% (540/600) sensitivity and 89.00% (534/600) specificity for assessing aberrant and acceptable technique with a single IMU positioned on the left thigh.ConclusionsA tablet app was developed that automates the process required to create a personalized exercise technique classification system. This tool can be applied to any cyclical, repetitive exercise. The personalized classification model displayed excellent system accuracy even when assessing acute deviations in compound exercises with a single IMU.

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Classification of squat quality with inertial measurement units in the single leg squat mobility test

Cited by 20 publications

References 12 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

Automated Assessment of Dynamic Knee Valgus and Risk of Knee Injury During the Single Leg Squat

Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation

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