Leveraging IMU data for accurate exercise performance classification and musculoskeletal injury risk screening

Whelan, Darragh; O’Reilly, Martin; Huang, B. Q.; Giggins, Oonagh M.; Kechadi, Tahar; Caulfield, Brian

doi:10.1109/embc.2016.7590788

Cited by 27 publications

(14 citation statements)

References 14 publications

(24 reference statements)

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“…The general data analysis approach for all studies appears to be first completing signal pre-processing, signal segmentation, computing features from the signals and placing them in feature vectors which will be used to train and evaluate different classification algorithms [79]. 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.…”

Section: Exercise Detection Systemsmentioning

confidence: 99%

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: Exercise Detection Systemsmentioning

confidence: 99%

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

et al. 2018

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“…In exercise classification with IMUs, there exist a number of universal steps that allow for the development of exercise biofeedback systems [ 28 ]. First, IMU data must be collected from participants as they exercise.…”

Section: Methodsmentioning

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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“…This classification will give the best results if it is based on objective measures. With the increasing availability of cheap and reliable measuring devices based on inertial measurement units (IMU) and the advancement of health informatics, more professionals start to rely on objective evaluations [19]. Still, there is a lack of a clear consensus on which main variables from neck movement should be addressed on a basis [16,[20][21][22][23][24][25] and which is their association to the blunt trauma suffered [26].…”

Section: Introductionmentioning

confidence: 99%

Improving the staging of neck injuries using a new index, the Neck Functional Holistic Analysis Score: Clustering approach to determine degrees of impairment

Fidalgo-Herrera¹,

Jové-Blanco²,

Martínez-Beltrán

et al. 2020

PLoS ONE

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Background Traumatic cervical spine injuries are amongst the traffic injuries that can cause most harm to a person. Classifying subtypes of clinical presentations has been a method used in other pathologies to diagnose more efficiently and to address the appropriate treatment and the prognosis. The management of patients suffering from cervical injuries could be improved by classifying the severity of the impairment. This will allow clinicians to propose better treatment modalities according to the severity of the injury. Materials and methods The present study is a retrospective cohort study performed with the clinical data from 772 patients stored at Fisi-(ON) Health Group. All the patients treated for cervical spine injuries are evaluated using the EBI-5 ® system, which is based on inertial measurement unit (IMU) technology. The normalized range of motion of each patient was incorporated into a single index, the Neck Functional Holistic Analysis Score (NFHAS). Results Clustering analysis of the patients according to their NFHAS resulted in five groups. The Kruskal-Wallis H test showed that there were statistically relevant differences in the ROM values and NFHAS of the patients depending on the cluster they were assigned to: FE X 2 (4) = 551.59, p = 0.0005; LB ROM X 2 (4) = 484.58, p = 0.0005; RT ROM X 2 (4) = 557.14, p = 0.0005; NFHAS X 2 (4) = 737.41, p = 0.0005. Effect size with ηp 2 for the comparison of groups were: FE = 0.76, LB = 0.68, RT = 0.76 and NFHAS = 0.96.

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Leveraging IMU data for accurate exercise performance classification and musculoskeletal injury risk screening

Cited by 27 publications

References 14 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

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

Improving the staging of neck injuries using a new index, the Neck Functional Holistic Analysis Score: Clustering approach to determine degrees of impairment

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