The use of inertial sensors for the classification of rehabilitation exercises

Giggins, Oonagh M.; Sweeney, Kevin T.; Caulfield, Brian

doi:10.1109/embc.2014.6944245

Cited by 18 publications

(22 citation statements)

References 9 publications

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“…The YBT reach direction classification results presented in this study are in line with previously published IMU exercise identification results which range between 85-95% depending on the exercises and IMU setups (Giggins et al, 2014, Pernek et al, 2015, Chang et al, 2007. Giggins and colleagues (2014) demonstrated that a single IMU location could differentiate between seven basic rehabilitation exercises with an accuracy of between 93-95% depending on the mounting location.…”

Section: Discussionsupporting

confidence: 92%

“…This is significant as the excellent levels of accuracy (98%) presented in this study were achieved using just 252 observations. In contrast, the exercise classification work presented above used a greater number of observations to train the classifiers, with Giggins et al (2014) utilising 3940 observations and Pernek et al (2015) using 440 observations per exercise.…”

Section: Discussionmentioning

confidence: 99%

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Objective Classification of Dynamic Balance Using a Single Wearable Sensor

O’Reilly¹,

Dolan²,

Reid³

et al. 2016

Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support

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Abstract:The Y Balance Test (YBT) is one of the most commonly used dynamic balance assessments in clinical and research settings. This study sought to investigate the ability of a single lumbar inertial measurement unit (IMU) to discriminate between the three YBT reach directions, and between pre and post-fatigue balance performance during the YBT. Fifteen subjects (age: 23±4, weight: 67.5±8, height: 175±8, BMI: 22±2) were fitted with a lumbar IMU. Three YBTs were performed on the dominant leg at 0, 10 and 20 minutes. A modified Wingate fatiguing intervention was conducted to introduce a balance deficit. This was followed immediately by three post-fatigue YBTs. Features were extracted from the IMU, and used to train and evaluate the random-forest classifiers. Reach direction classification achieved an accuracy of 97.80%, sensitivity of 97.86±0.89% and specificity of 98.90±0.56%. "Normal" and "abnormal" balance performance, as influenced by fatigue, was classified with an accuracy of 61.90%-71.43%, sensitivity of 61.90%-69.04% and specificity of 61.90%-78.57% depending on which reach direction was chosen. These results demonstrate that a single lumbar IMU is capable of accurately distinguishing between the different YBT reach directions and can classify between pre and post-fatigue balance with moderate levels of accuracy.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Objective Classification of Dynamic Balance Using a Single Wearable Sensor

O’Reilly¹,

Dolan²,

Reid³

et al. 2016

Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support

Self Cite

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show abstract

“…Our research therefore sought to investigate whether a single IMU sensor can provide sufficient data to be used in the development of an interactive exercise classification and feedback system. The results of this work demonstrated that a single IMU sensor is capable of classifying between seven different exercises with a high level of accuracy [14]. It was also shown that it is possible to classify between correct and incorrect performance of an exercise, and to classify the particular error in an exercise using data from a single IMU sensor with satisfactory levels of accuracy [1,19].…”

Section: A Exercise Performance Evaluationmentioning

confidence: 72%

“…It was also shown that it is possible to classify between correct and incorrect performance of an exercise, and to classify the particular error in an exercise using data from a single IMU sensor with satisfactory levels of accuracy [1,19]. This work also indicated that the addition of extra IMU sensors does not significantly improve results [14,19].…”

Section: A Exercise Performance Evaluationmentioning

confidence: 90%

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

Whelan

O’Reilly

Huang

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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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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The use of inertial sensors for the classification of rehabilitation exercises

Cited by 18 publications

References 9 publications

Objective Classification of Dynamic Balance Using a Single Wearable Sensor

Objective Classification of Dynamic Balance Using a Single Wearable Sensor

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

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

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