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O'Reilly, Martin
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O'Reilly, Martin
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O'Reilly, Martin
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- PublicationUse of body worn sensors to predict ankle injuries using screening toolsBackground The Single Leg Squat (SLS) is an important screening tool in predicting those at an increased risk of ankle injuries as it relates to landing, running and cutting tasks. However, clinical analysis of this exercise is often completed visually with relatively poor intra-rater reliability. More detailed analysis of SLS completed in biomechanics laboratories is time-consuming and costly. Recent developments in body worn sensors may allow for quick assessments that produce valid and reliable data.Objective To explore a model for leveraging data obtained from wearable sensors to aid in ankle injury risk factor screening.Design A single case study design, with qualitative analysis of quantitative data.Setting University research laboratory.Participants A single participant (female, age = 24 years; height = 158 cm, body mass = 47 kg) was chosen. The participant was familiar with the SLS exercise and had completed it as part of their exercise routine for the past year.Interventions The participant completed 10 left SLS repetitions. These were recorded using the sensors and repetitions where the participant lost balance were noted. Loss of balance was defined as when the subject was unable to maintain single leg stance during the downward or upward phase of the movement and placed their other foot on the ground for support.Main outcome measurements Visual analysis showed signals from the wearable sensors (accelerometer Y and gyroscope Z) were altered when the participant lost their balance compared to signals obtained when the participant maintained balance.Conclusions These preliminary results indicate that body worn sensors may be able to automatize screening tools such as the SLS. An automated system for characterising and quantifying deviations from good form could be developed to aid clinicians and researchers. Such a system would provide objective and reliable data to clinicians and allow researchers to analyse movements quicker and in a more naturalistic setting.
334 - PublicationEvaluating Performance of the Single Leg Squat Exercise with a Single Inertial Measurement UnitThe single leg squat (SLS) is an important component of lower limb rehabilitation and injury risk screening tools. This study sought to investigate whether a single lumbar-worn IMU is capable of discriminating between correct and incorrect performance of the SLS. Nineteen healthy volunteers (15 males, 4 females, age: 26.09± 3.98 years, height: 1.75± 0.14m, body mass: 75.2±14.2kg) were fitted with a single IMU on the lumbar spine and asked to perform 10 left leg SLS. These repetitions were recorded and labelled by a chartered physiotherapist. Features were extracted from the labelled sensor data. These features were used to train and evaluate a random-forests classifier. The system achieved an average of 92% accuracy, 78% sensitivity and 97% specificity. These results indicate that a single IMU has the potential to differentiate between a correctly and incorrectly completed SLS. This may allow such devices to be used by clinicians to help track rehabilitation of patients and screen for potential injury risks. Furthermore, the classifier described may be a useful input to an exercise biofeedback application.
574Scopus© Citations 21 - PublicationA Wearable Sensor-Based Exercise Biofeedback System: Mixed Methods Evaluation of Formulift(JMIR Publications, 2018-01-31)
; ; ; Background: Formulift is a newly developed mobile health (mHealth) app that connects to a single inertial measurement unit (IMU) worn on the left thigh. The IMU captures users movements as they exercise, and the app analyzes the data to count repetitions in real time and classify users exercise technique. The app also offers feedback and guidance to users on exercising safely and effectively. Objective: The aim of this study was to assess the Formulift system with three different and realistic types of potential users (beginner gym-goers, experienced gym-goers, and qualified strength and conditioning [S&C] coaches) under a number of categories: (1) usability, (2) functionality, (3) the perceived impact of the system, and (4) the subjective quality of the system. It was also desired to discover suggestions for future improvements to the system. Methods: A total of 15 healthy volunteers participated (12 males; 3 females; age: 23.8 years [SD 1.80]; height: 1.79 m [SD0.07], body mass: 78.4 kg [SD 9.6]). Five participants were beginner gym-goers, 5 were experienced gym-goers, and 5 were qualified and practicing S&C coaches. IMU data were first collected from each participant to create individualized exercise classifiers for them. They then completed a number of non exercise-related tasks with the app. Following this, a workout was completed using the system, involving squats, dead lifts, lunges, and single-leg squats. Participants were then interviewed about their user experience and completed the System Usability Scale (SUS) and the user version of the Mobile Application Rating Scale (uMARS). Thematic analysis was completed on all interview transcripts, and survey results were analyzed. Results: Qualitative and quantitative analysis found the system has good to excellent usability. The system achieved a mean (SD) SUS usability score of 79.2 (8.8). Functionality was also deemed to be good, with many users reporting positively on the systems repetition counting, technique classification, and feedback. A number of bugs were found, and other suggested changes to the system were also made. The overall subjective quality of the app was good, with a median star rating of 4 out of 5 (interquartile range, IQR: 3-5). Participants also reported that the system would aid their technique, provide motivation, reassure them, and help them avoid injury. Conclusions: This study demonstrated an overall positive evaluation of Formulift in the categories of usability, functionality, perceived impact, and subjective quality. Users also suggested a number of changes for future iterations of the system. These findings are the first of their kind and show great promise for wearable sensor-based exercise biofeedback systems.451Scopus© Citations 16 - PublicationEvaluating Squat Performance with a Single Inertial Measurement Unit(IEEE, 2015-06-12)
; ; ; ; ; ; Inertial measurement units (IMUs) may be used during exercise performance to assess form and technique. To maximise practicality and minimise cost a single-sensor system is most desirable. This study sought to investigate whether a single lumbar-worn IMU is capable of identifying seven commonly observed squatting deviations. Twenty-two volunteers (18 males, 4 females, age: 26.09±3.98 years, height: 1.75±0.14m, body mass: 75.2±14.2 kg) performed the squat exercise correctly and with 7 induced deviations. IMU signal features were extracted for each condition. Statistical analysis and leave one subject out classifier evaluation were used to assess the ability of a single sensor to evaluate performance. Binary level classification was able to distinguish between correct and incorrect squatting performance with a sensitivity of 64.41%, specificity of 88.01% and accuracy of 80.45%. Multi-label classification was able to distinguish between specific squat deviations with a sensitivity of 59.65%, specificity of 94.84% and accuracy of 56.55%. These results indicate that a single IMU can successfully discriminate between squatting deviations. A larger data set must be collected and more complex classification techniques developed in order to create a more robust exercise analysis IMU-based system.1202Scopus© Citations 34 - PublicationInterpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations(Springer, 2019-05-21)
; ; ; ; The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. Prior research has mostly focused on improving the accuracy and efficiency of classifiers, with interpretability being somewhat neglected. This aspect of classifiers has become critical for many application domains and the introduction of the EU GDPR legislation in 2018 is likely to further emphasize the importance of interpretable learning algorithms. Currently, state-of-the-art classification accuracy is achieved with very complex models based on large ensembles (COTE) or deep neural networks (FCN). These approaches are not efficient with regard to either time or space, are difficult to interpret and cannot be applied to variable-length time series, requiring pre-processing of the original series to a set fixed-length. In this paper we propose new time series classification algorithms to address these gaps. Our approach is based on symbolic representations of time series, efficient sequence mining algorithms and linear classification models. Our linear models are as accurate as deep learning models but are more efficient regarding running time and memory, can work with variable-length time series and can be interpreted by highlighting the discriminative symbolic features on the original time series. We advance the state-of-the-art in time series classification by proposing new algorithms built using the following three key ideas: (1) Multiple resolutions of symbolic representations: we combine symbolic representations obtained using different parameters, rather than one fixed representation (e.g., multiple SAX representations); (2) Multiple domain representations: we combine symbolic representations in time (e.g., SAX) and frequency (e.g., SFA) domains, to be more robust across problem types; (3) Efficient navigation in a huge symbolic-words space: we extend a symbolic sequence classifier (SEQL) to work with multiple symbolic representations and use its greedy feature selection strategy to effectively filter the best features for each representation. We show that our multi-resolution multi-domain linear classifier (mtSS-SEQL+LR) achieves a similar accuracy to the state-of-the-art COTE ensemble, and to recent deep learning methods (FCN, ResNet), but uses a fraction of the time and memory required by either COTE or deep models. To further analyse the interpretability of our classifier, we present a case study on a human motion dataset collected by the authors. We discuss the accuracy, efficiency and interpretability of our proposed algorithms and release all the results, source code and data to encourage reproducibility.1104Scopus© Citations 72 - PublicationTechnology in Rehabilitation: Evaluating the Single Leg Squat Exercise with Wearable Inertial Measurement UnitsBackground: The single leg squat (SLS) is a common lower limb rehabilitation exercise. It is also frequently used as an evaluative exercise to screen for an increased risk of lower limb injury. To date athlete / patient SLS technique has been assessed using expensive laboratory equipment or subjective clinical judgement; both of which are not without shortcomings. Inertial measurement units (IMUs) may offer a low cost solution for the objective evaluation of athlete / patient SLS technique. Objectives: The aims of this study were to determine if in combination or in isolation IMUs positioned on the lumbar spine, thigh and shank are capable of: (a) distinguishing between acceptable and aberrant SLS technique; (b) identifying specific deviations from acceptable SLS technique. Methods: Eighty-three healthy volunteers participated (60 males, 23 females, age: 24.68 + / − 4.91 years, height: 1.75 + / − 0.09 m, body mass: 76.01 + / − 13.29 kg). All participants performed 10 SLSs on their left leg. IMUs were positioned on participants’ lumbar spine, left shank and left thigh. These were utilized to record tri-axial accelerometer, gyroscope and magnetometer data during all repetitions of the SLS. SLS technique was labelled by a Chartered Physiotherapist using an evaluation framework. Features were extracted from the labelled sensor data. These features were used to train and evaluate a variety of random-forests classifiers that assessed SLS technique. Results: A three IMU system was moderately successful in detecting the overall quality of SLS performance (77 % accuracy, 77 % sensitivity and 78 % specificity). A single IMU worn on the shank can complete the same analysis with 76 % accuracy, 75 % sensitivity and 76 % specificity. Single sensors also produce competitive classification scores relative to multi-sensor systems in identifying specific deviations from acceptable SLS technique. Conclusions: A single IMU positioned on the shank can differentiate between acceptable and aberrant SLS technique with moderate levels of accuracy. It can also capably identify specific deviations from optimal SLS performance. IMUs may offer a low cost solution for the objective evaluation of SLS performance. Additionally, the classifiers described may provide useful input to an exercise biofeedback application.
874Scopus© Citations 35 - PublicationLeveraging IMU Data for Accurate Exercise Performance Classification and Musculoskeletal Injury Risk Screening(IEEE, 2016-08-20)
; ; ; ; ; Inertial measurement units (IMUs) are becoming increasingly prevalent as a method for low cost and portable biomechanical analysis. However, to date they have not tended to be accepted into routine clinical practice. This is often due to the disconnect between translating the data collected by the sensors into meaningful and actionable information for end users. This paper outlines the work completed by our group in attempting to achieve this. We discuss the conceptual framework involved in our work, the methodological approach taken in analysing sensor signals and discuss possible application models. The work completed by our group indicates that IMU based systems have the potential to bridge the gap between laboratory and clinical movement analysis. Future work will focus on collecting a diverse range of movement data and using more sophisticated data analysis techniques to refine systems.802Scopus© Citations 25 - PublicationAssociation of dynamic balance with sports related concussion: a prospective cohort study(Sage, 2018-12-03)
; ; ; ; Background: Concussion is one of the most common sports-related injuries, with little understood about the modifiable and non-modifiable risk factors. Researchers have yet to evaluate the association between modifiable sensorimotor function variables and concussive injury. Purpose: To investigate the association between dynamic balance performance, a discrete measure of sensorimotor function, and concussive injuries. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: A total of 109 elite male rugby union players were baseline tested in dynamic balance performance while wearing an inertial sensor and prospectively followed during the 2016-2017 rugby union season. The sample entropy of the inertial sensor gyroscope magnitude signal was derived to provide a discrete measure of dynamic balance performance. Logistic regression modeling was then used to investigate the association among the novel digital biomarker of balance performance, known risk factors of concussion (concussion history, age, and playing position), and subsequent concussive injury. Results: Participant demographic data (mean 6 SD) were as follows: age, 22.6 6 3.6 years; height, 185 6 6.5 cm; weight, 98.9 612.5 kg; body mass index, 28.9 6 2.9 kg/m2; and leg length, 98.8 6 5.5 cm. Of the 109 players, 44 (40.3%) had a history of concussion, while 21 (19.3%) sustained a concussion during the follow-up period. The receiver operating characteristic analysis for the anterior sample entropy demonstrated a statistically significant area under the curve (0.64; 95% CI, 0.52-0.76; P \ .05), with the cutoff score of anterior sample entropy 1.2, which maximized the sensitivity (76.2%) and specificity (53.4%) for identifying individuals who subsequently sustained a concussion. Players with suboptimal balance performance at baseline were at a 2.81-greater odds (95% CI, 1.02-7.74) of sustaining a concussion during the rugby union season than were those with optimal balance performance, even when controlling for concussion history. Conclusion: Rugby union players who possess poorer dynamic balance performance, as measured by a wearable inertial sensor during the Y balance test, have a 3-times-higher relative risk of sustaining a sports-related concussion, even when controlling for history of concussion. These findings have important implications for research and clinical practice, as it identifies a potential modifiable risk factor. Further research is required to investigate this association in a large cohort consisting of males and females across a range of sports.635Scopus© Citations 25 - PublicationInertial Sensor Technology Can Capture Changes in Dynamic Balance Control during the Y Balance TestIntroduction: The Y Balance Test (YBT) is one of the most commonly utilised clinical dynamicbalance assessments. Research has demonstrated the utility of the YBT in identifying balancedeficits in individuals following lower limb injury. However, quantifying dynamic balancebased on reach distances alone fails to provide potentially important information related tothe quality of movement control and choice of movement strategy during the reaching action.The addition of an inertial sensor to capture more detailed motion data may allow for the inexpensive,accessible quantification of dynamic balance control during the YBT reach excursions.As such, the aim of this study was to compare baseline and fatigued dynamic balancecontrol, using reach distances and 95EV (95% ellipsoid volume), and evaluate the ability of95EV to capture alterations in dynamic balance control, which are not detected by YBT reachdistances. Methods: As part of this descriptive laboratory study, 15 healthy participants completedrepeated YBTs at 20, 10, and 0 min prior to and following a modified 60-s Wingate testthat was used to introduce a short-term reduction in dynamic balance capability. Dynamicbalance was assessed using the standard normalised reach distance method, while dynamicbalance control during the reach attempts was simultaneously measured by means of the95EV derived from an inertial sensor, worn at the level of the 4th lumbar vertebra. Results:Intraclass correlation coefficients for the inertial sensor-derived measures ranged from 0.76to 0.92, demonstrating strong intrasession test-retest reliability. Statistically significant altera-tions (p < 0.05) in both reach distance and the inertial sensor-derived 95EV measure wereobserved immediately post-fatigue. However, reach distance deficits returned to baseline levelswithin 10 min, while 95EV remained significantly increased (p < 0.05) beyond 20 min forall 3 reach distances. Conclusion: These findings demonstrate the ability of an inertial sensorderivedmeasure to quantify alterations in dynamic balance control, which are not capturedby traditional reach distances alone. This suggests that the addition of an inertial sensor tothe YBT may provide clinicians and researchers with an accessible means to capture subtlealterations in motor function in the clinical setting.
302Scopus© Citations 19 - PublicationTechnology in Rehabilitation: Comparing Personalised and Global Classification Methodologies in Evaluating the Squat Exercise with Wearable IMUs(Thieme, 2017)
; ; ; ; BACKGROUND: The barbell squat is a popularly used lower limb rehabilitation exercise. It is also an integral exercise in injury risk screening protocols. To date athlete/patient technique has been assessed using expensive laboratory equipment or subjective clinical judgement; both of which are not without shortcomings. Inertial measurement units (IMUs) may offer a low cost solution for the objective evaluation of athlete/patient technique. However, it is not yet known if global classification techniques are effective in identifying naturally occurring, minor deviations in barbell squat technique.OBJECTIVES: The aims of this study were to: (a) determine if in combination or in isolation, IMUs positioned on the lumbar spine, thigh and shank are capable of distinguishing between acceptable and aberrant barbell squat technique; (b) determine the capabilities of an IMU system at identifying specific natural deviations from acceptable barbell squat technique; and (c) compare a personalised (N=1) classifier to a global classifier in identifying the above. METHODS: Fifty-five healthy volunteers (37 males, 18 females, age = 24.21 +/- 5.25 years, height = 1.75 +/- 0.1 m, body mass = 75.09 +/- 13.56 kg) participated in the study. All participants performed a barbell squat 3-repetition maximum max strength test. IMUs were positioned on participants' lumbar spine, both shanks and both thighs; these were utilized to record tri-axial accelerometer, gyroscope and magnetometer data during all repetitions of the barbell squat exercise. Technique was assessed and labelled by a Chartered Physiotherapist using an evaluation framework. Features were extracted from the labelled IMU data. These features were used to train and evaluate both global and personalised random forests classifiers.RESULTS: Global classification techniques produced poor accuracy (AC), sensitivity (SE) and specificity (SP) scores in binary classification even with a 5 IMU set-up in both binary (AC: 64%, SE: 70%, SP: 28%) and multi-class classification (AC: 59%, SE: 24%, SP: 84%). However, utilising personalised classification techniques even with a single IMU positioned on the left thigh produced good binary classification scores (AC: 81%, SE: 81%, SP: 84%) and moderate-to-good multi-class scores (AC: 69%, SE: 70%, SP: 89%).CONCLUSIONS: There are a number of challenges in developing global classification exercise technique evaluation systems for rehabilitation exercises such as the barbell squat. Building large, balanced data sets to train such systems is difficult and time intensive. Minor, naturally occurring deviations may not be detected utilising global classification approaches. Personalised classification approaches allow for higher accuracy and greater system efficiency for end-users in detecting naturally occurring barbell squat technique deviations. Applying this approach also allows for a single-IMU set up to achieve similar accuracy to a multi-IMU setup, which reduces total system cost and maximises system usability.522Scopus© Citations 12