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At Output we are an interdisciplinary team of practitioners, sports-scientists, UX experts and engineers driven to use our unique development process to produce easy to use, diversely capable, valid and reliable athlete assessment tools. Our work stems from 2 PhDs and research in Europe’s largest academic data-analytics research centre, the Insight Centre for Data Analytics, which commenced in 2013.
Whether it’s VBT, power, movement, balance, agility or any other aspect of performance we’re measuring our process is always the same:
The result: an ever-growing diversely capable, highly valid and reliable performance tool. You can dive deeper in the Output research portfolio below.
The body of evidence supporting our system’s validity and reliability is ever-growing. Below we provide links to research publications, conference presentations, and sample data-sets which highlight our system’s scientific underpinnings. We have divided the evidence into key sections of the system’s measures e.g. VBT, Power, movement, A-VBT etc.
The purpose of this study was to examine the test-retest reliability, usefulness, and validity of field- based devices, in determining jump height (JH) during a countermovement jump (CMJ).Twenty-one male and 7 female field sport athletes performed 3 CMJs with data simultaneously recorded using a force plate (criterion measure), Optojump, Output Capture, and Push-Band 2.0. Reliability was determined by intraclass correlation (ICC) and coefficient of variation (CV) analyses. Usefulness was assessed by comparing typical error (TE) with the smallest worthwhile change (SWC), and the validity analyses involved repeated measures analysis of variance with post hoc analysis, Pearson correlation coefficient (r), coefficient of determination, and Bland-Altman 95% limits of agreement analyses.All 3 field-based devices were deemed reliable in assessing CMJ height as the respective ICCs ≥ 0.80 and the CV ≤ 10%. Only the Optojump and Output Capture devices were rated as “good” at detecting the SWC in performance. The Output Capture device demonstrated acceptable validity for CMJ height assessment, whereas the Push-Band 2.0 showed systematic bias when compared with the criterion force plate data.Although all 3 devices showed excellent reliability, the Optojump and Output Capture devices offer practitioners a cost effective, reliable, and valid method of assessing the smallest worthwhile change in CMJ performance in an applied setting.
INTRO: To investigate the concurrent validity of the Output V2 IMU for measuring Counter-Movement Jump (CMJ) height, the system was compared to an optical measurement system (OptoJump). It is essential that a system has good validity and accuracy for evidence-based practice which is vital for any practitioners using theOutput Capture system. Given the portability and practicality of the system, this will ensure that wherever and whenever practitioners are, they will be able to accurately measure CMJ height.
METHODOLOGY: An OptoJump was used as the ground truth for this validity investigation, as it has been shown to have excellent concurrent validity with force platform data. TheOutput V2 IMU was worn on the foot for the investigation while performing the various CMJs. A total of 26 participants participated in the study. They all completed CMJs of varying heights with their hands on their hips, with each participant completing 1-12 CMJs. A total of 163 CMJs were recorded for the investigation. The Pearson Correlation Coefficient (r), Adjusted R2, Mean AbsoluteError (MAE), and Root Mean Square Error (RMSE) were used to complete the analysis.
RESULTS: The results can be seen in Table 1 below. All measurements were plotted in a correlation plot and can be seen below.
CONCLUSION: These results show a strong correlation and excellent agreement between OutputV2 IMU and OptoJump. A limitation of this investigation is the number of participants that were included. Further investigation could be carried out to validate Output V2 IMU across a wider cohort of participants of varying ability.However, given the high correlation and excellent agreement, it is expected that this accuracy will carry over to practical application of CMJ testing. Therefore, enabling practitioners to accurately measure CMJ height, without the usual laboratory constraints, using the Output V2 IMU. Want to run your own stats? Spreadsheet of data available for [download here](https://25970650.fs1.hubspotusercontent-eu1.net/hubfs/25970650/Blogs Info/CMJ Testing Jan_2023.xlsx).
Want to run your own stats? Spreadsheet of data available for [download here](https://25970650.fs1.hubspotusercontent-eu1.net/hubfs/25970650/Blogs Info/CMJ Testing.xlsx).
Data submitted by Eamonn Flanagan, Lead S&C Coach, Sport Ireland Institute. You can download the data [here](https://25970650.fs1.hubspotusercontent-eu1.net/hubfs/25970650/Blogs Info/Output+ForceDecks CMJ+SL CMJ.xlsx).
A preliminary validation study on our athlete jump assessment feature was presented at the NSCA Conference in Washington D.C. in July 2019. A PDF copy of the poster can be downloaded via [this link](https://25970650.fs1.hubspotusercontent-eu1.net/hubfs/25970650/Blogs Info/NSCA 2019 CMJ.pdf).
INTRO: To investigate the concurrent validity of the Output V2 IMU for measuring Reactive Strength Index (RSI) during a drop jump, the system was compared to an optical measurement system (OptoJump). It is essential that a system has good validity and accuracy for evidence-based practice which is vital for any practitioners using theOutput Capture system. Given the portability and practicality of the system, this will ensure that wherever and whenever practitioners are, they will be able to accurately measure Drop Jump RSI.
METHODOLOGY: An OptoJump was used as the ground truth for this validity investigation, as it has been shown to have excellent concurrent validity with force platform data. TheOutput V2 IMU was worn on the foot for the investigation while performing the various Drop Jumps. A total of 6 participants participated in the study. They all completed Drop Jumps with their hands on their hips from a set height of 30cm, with each participant completing 6-14 reps. A total of 63 Drop Jumps were recorded for the investigation. The Pearson Correlation Coefficient (r), Adjusted R2,Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were used to complete the analysis.
RESULTS: The results can be seen in Table 1 below. All measurements were plotted in a correlation plot and can be seen below.
CONCLUSION: These results show a strong correlation and excellent agreement between OutputV2 IMU and OptoJump. A limitation of this investigation is the number of participants that were included. Further investigation could be carried out to validate Output V2 IMU across a wider cohort of participants of varying ability and varying drop jump heights. However, given the high correlation and excellent agreement, it is expected that this accuracy will carry over to practical application ofDrop Jump testing. Therefore, enabling practitioners to accurately measure RSI using the Output V2 IMU without the usual laboratory constraints.
Spreadsheet of data to run your own stats can be downloaded [here](https://25970650.fs1.hubspotusercontent-eu1.net/hubfs/25970650/Blogs Info/Output 10-5 Test Vs. Optojump.xlsx).
NSCA Conference 2019 - Barbell Velocity
The development and evaluation of our barbell velocity sensor was presented at the NSCA Conference in Washington D.C. in July 2019. A PDF copy of the presentation slides can be found [here](https://25970650.fs1.hubspotusercontent-eu1.net/hubfs/25970650/Blogs Info/NSCA 2019 MOR.pdf).
In early 2020, an external research study was completed in Technical University of Dublin. The below summarises the findings:
Data submitted by Eamonn Flanagan, Lead S&C at Sport Ireland Institute. You can download the data spreadsheet [here](https://25970650.fs1.hubspotusercontent-eu1.net/hubfs/25970650/Blogs Info/Output Vs GymAware.xlsx) to run your own statistics and analysis.
J Merrigan, J Martin, JSCR 2020
The 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.
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.
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.
The lunge is an important component of lower limb reha- bilitation, strengthening and injury risk screening. Complet- ing the movement incorrectly alters muscle activation and increases stress on knee, hip and ankle joints. This study sought to investigate whether IMUs are capable of discrimi- nating between correct and incorrect performance of the lunge. Eighty volunteers (57 males, 23 females, age: 24.68± 4.91 years, height: 1.75± 0.094m, body mass: 76.01±13.29kg) were fitted with five IMUs positioned on the lumbar spine, thighs and shanks. They then performed the lunge exercise with correct form and 11 specific deviations from acceptable form. Features were extracted from the labelled sensor data and used to train and evaluate random-forests classifiers. The system achieved 83% accuracy, 62% sensitivity and 90% speci- ficity in binary classification with a single sensor placed on the right thigh and 90% accuracy, 80% sensitivity and 92% specificity using five IMUs. This multi-sensor set up can de- tect specific deviations with 70% accuracy. These results in- dicate that a single IMU has the potential to differentiate be- tween correct and incorrect lunge form and using multiple IMUs adds the possibility of identifying specific deviations a user is making when completing the lunge.
Lunges are a common, compound lower limb resistance exercise. If completed with aberrant technique, the increased stress on the joints used may increase risk of injury. This study sought to first investigate the ability of inertial measurement units (IMUs), when used in isolation and combination, to (a) classify acceptable and aberrant lunge technique (b) classify exact deviations in lunge technique. We then sought to investigate the most important features and establish the minimum number of top-ranked features and decision trees that are needed to maintain maximal system classification efficacy. Eighty volunteers performed the lunge with acceptable form and 11 deviations. Five IMUs positioned on the lumbar spine, thighs, and shanks recorded these movements. Time and frequency domain features were extracted from the IMU data and used to train and test a variety of classifiers. A single-IMU system achieved 83% accuracy, 62% sensitivity, and 90% specificity in binary classification and a five-IMU system achieved 90% accuracy, 80% sensitivity, and 92% specificity. A five-IMU set-up can also detect specific deviations with 70% accuracy. System efficiency was improved and classification quality was maintained when using only 20% of the top-ranked features for training and testing classifiers.
Strength and conditioning (S&C) coaches offer expert guidance to help those they work with achieve their personal fitness goals. However, because of cost and availability issues, individuals are often left training without expert supervision. Recent developments in inertial measurement units (IMUs) and mobile computing platforms have allowed for the possibility of unobtrusive motion tracking systems and the provision of real-time individualized feedback regarding exercise performance. These systems could enable S&C coaches to remotely monitor sessions and help gym users record workouts. One component of these IMU systems is the ability to identify the exercises completed. In this study, IMUs were positioned on the lumbar spine, thighs, and shanks on 82 healthy participants. Participants completed 10 repetitions of the squat, lunge, single-leg squat, deadlift, and tuck jump with acceptable form. Descriptive features were extracted from the IMU signals for each repetition of each exercise, and these were used to train an exercise classifier. The exercises were detected with 99% accuracy when using signals from all 5 IMUs, 99% when using signals from the thigh and lumbar IMUs and 98% with just a single IMU on the shank. These results indicate that a single IMU can accurately distinguish between 5 common multijoint exercises.
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: 234, weight: 67.58, height: 1758, BMI: 222) 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 97.860.89% and specificity of 98.900.56%. 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.
The deadlift is a compound full-body exercise that is fundamental in resistance training, rehabilitation programs and powerlifting competitions. Accurate quantification of deadlift biomechanics is important to reduce the risk of injury and ensure training and rehabilitation goals are achieved. This study sought to develop and evaluate deadlift exercise technique classification systems utilising Inertial Measurement Units (IMUs), recording at 51.2 Hz, worn on the lumbar spine, both thighs and both shanks. It also sought to compare classification quality when these IMUs are worn in combination and in isolation. Two datasets of IMU deadlift data were collected. Eighty participants first completed deadlifts with acceptable technique and 5 distinct, deliberately induced deviations from acceptable form. Fifty-five members of this group also completed a fatiguing protocol (3-Repition Maximum test) to enable the collection of natural deadlift deviations. For both datasets, universal and personalised random-forests classifiers were developed and evaluated. Personalised classifiers outperformed universal classifiers in accuracy, sensitivity and specificity in the binary classification of acceptable or aberrant technique and in the multi-label classification of specific deadlift deviations. Whilst recent research has favoured universal classifiers due to the reduced overhead in setting them up for new system users, this work demonstrates that such techniques may not be appropriate for classifying deadlift technique due to the poor accuracy achieved. However, personalised classifiers perform very well in assessing deadlift technique, even when using data derived from a single lumbar-worn IMU to detect specific naturally occurring technique mistakes.
The popularity of running has increased in recent years. A rise in the incidence of running-related overuse musculoskeletal injuries has occurred parallel to this. This study investigates the capability of using data from a single inertial measurement unit (IMU) to differentiate between running form in a non-fatigued and fatigued state. Data was captured from an IMU placed on the lumbar spine, right shank and left shank in 21 recreational runners (10 male, 11 female) during separate 400m running trials. The trials were performed prior to and following a fatiguing protocol. Following stride segmentation, IMU signal features were extracted from the labelled (non-fatigued vs fatigued) sensor data and used to train both a Global and Personalised classifier for each individual IMU location. A single IMU on the Lumbar spine displayed 75% accuracy, 73% sensitivity and 77% specificity when using a Global Classifier. A single IMU on the Right Shank displayed 100% accuracy, 100% sensitivity and 100% specificity when using a Personalised Classifier. These results indicate that a single IMU has the potential to differentiate between non-fatigued and fatigued running states with a high level of accuracy.
Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. Objective: The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. Methods: We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. Results: With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. Conclusions: The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds.
Biofeedback 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. Objective: We 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. Methods: A 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. Results: The 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. Conclusions: A 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.
Strength and conditioning (S&C) coaches offer expert guidance to help those they work with achieve their personal fitness goals. However it is not always practical to operate under the direct supervision of an S&C coach and consequently individuals are often left training without expert oversight. Recent developments in inertial measurement units (IMUs) and mobile computing platforms have allowed for the possibility of unobtrusive motion tracking systems and the provision of real-time individualised feedback regarding exercise performance. These systems could enable S&C coaches to remotely monitor sessions and help individuals record their workout performance. One aspect of such technologies is the ability to assess exercise technique and detect common deviations from acceptable exercise form. In this study we investigate this ability in the context of a bodyweight (BW) squat exercise. IMUs were positioned on the lumbar spine, thighs and shanks of 77 healthy participants. Participants completed repetitions of BW squats with acceptable form and five common deviations from acceptable BW squatting technique. Descriptive features were extracted from the IMU signals for each BW squat repetition and these were used to train a technique classifier. Acceptable or aberrant BW squat technique can be detected with 98% accuracy, 96% sensitivity and 99% specificity when using features derived from all 5 IMUs. A single IMU system can also distinguish between acceptable and aberrant BW squat biomechanics with excellent accuracy, sensitivity and specificity. Detecting exact deviations from acceptable BW squatting technique can be achieved with 80% accuracy using a 5 IMU system and 72% accuracy when using a single IMU positioned on the right shank. These results suggest IMU based systems can distinguish between acceptable and aberrant BW squat technique with excellent accuracy with a single IMU system. Identification of exact deviations is also possible but multi- IMU systems outperform single IMU systems.
Inertial measurement units (IMUs) are becoming increasingly prevalent as a method for low cost and portable biomechanical analysis. However, to date they have not been accepted into routine clinical practice. This is often due to a 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. Our work indicates that IMU based systems have the potential to bridge the gap between laboratory and clinical movement analysis. Future studies will focus on collecting a diverse range of movement data and using more sophisticated data analysis techniques to refine systems.
The Y Balance Test (YBT) is one of the most commonly utilised clinical dynamic balance assessments. Research has demonstrated the utility of the YBT in identifying balance deficits in individuals following lower limb injury. However, quantifying dynamic balance based on reach distances alone fails to provide potentially important information related to the 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 balance control, using reach distances and 95EV (95% ellipsoid volume), and evaluate the ability of 95EV to capture alterations in dynamic balance control, which are not detected by YBT reach distances. Methods: As part of this descriptive laboratory study, 15 healthy participants completed repeated YBTs at 20, 10, and 0 min prior to and following a modified 60-s Wingate test that was used to introduce a short-term reduction in dynamic balance capability. Dynamic balance was assessed using the standard normalised reach distance method, while dynamic balance control during the reach attempts was simultaneously measured by means of the 95EV 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 0.76 to 0.92, demonstrating strong intrasession test-retest reliability. Statistically significant alterations (p < 0.05) in both reach distance and the inertial sensor-derived 95EV measure were observed immediately post-fatigue. However, reach distance deficits returned to baseline levels within 10 min, while 95EV remained significantly increased (p < 0.05) beyond 20 min for all 3 reach distances. Conclusion: These findings demonstrate the ability of an inertial sensor-derived measure to quantify alterations in dynamic balance control, which are not captured by traditional reach distances alone. This suggests that the addition of an inertial sensor to the YBT may provide clinicians and researchers with an accessible means to capture subtle alterations in motor function in the clinical setting.
Recent advances in mobile sensing and computing technology have provided a means to objectively and unobtrusively quantify postural control. This has resulted in the rapid development and evaluation of a series of wearable inertial sensor-based assessments. However, the validity, reliability and clinical utility of such systems is not fully understood. Objectives: This systematic review aims to synthesise and evaluate studies that have investigated the ability of wearable inertial sensor systems to validly and reliably quantify postural control performance in sports science and medicine applications. Methods: A systematic search strategy utilising the PRISMA guidelines was employed to identify eligible articles through ScienceDirect, Embase and PubMed databases. In total, 47 articles met the inclusion criteria and were evaluated and qualitatively synthesised under two main headings: measurement validity and measurement reliability. Furthermore, studies that investigated the utility of these systems in clinical populations were summarised and discussed. Results: After duplicate removal, 4374 articles were identified with the search strategy, with 47 papers included in the final review. In total, 28 studies investigated validity in healthy populations, and 15 studies investigated validity in clinical populations; 13 investigated the measurement reliability of these sensor-based systems. Conclusions: The application of wearable inertial sensors for sports science and medicine postural control applications is an evolving field. To date, research has primarily focused on evaluating the validity and reliability of a heterogeneous set of assessment protocols, in a laboratory environment. While researchers have begun to investigate their utility in clinical use cases such as concussion and musculoskeletal injury, most studies have leveraged small sample sizes, are of low quality and use a variety of descriptive variables, assessment protocols and sensor-mounting locations. Future research should evaluate the clinical utility of these systems in large high-quality prospective cohort studies to establish the role they may play in injury risk identification, diagnosis and management.
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 [SD 0.07], 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 nonexercise-related tasks with the app. Following this, a workout was completed using the system, involving squats, deadlifts, 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.
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.
Inertial measurement unit (IMU) based systems are becoming increasingly popular in the classification of human movement. While research in the area has established the utility of various machine learning classification methods, there is a paucity of evidence investigating the effect of feature selection on classification efficacy. The aim of this study was therefore to investigate the influence of feature selection methodology on the classification accuracy of human movement data. The efficacy of four commonly used feature selection and classification methods were compared using four IMU human movement data sets. Optimisation of classification and features selection methodologies resulted in an overall improvement in F1 score of between 1-8% for all four data sets. The findings from this study illustrate the need for researchers to consider the effect classification and feature selection methodologies may have on system efficacy.
Biomechanical screening assessments are used to provide useful information about an athlete's movement proficiency. Clinically, movement proficiency is typically evaluated visually. This can result in low levels of agreement, leading to difficulties in ensuring consistent athlete assessment. Objective: The objective was to determine levels of agreement within and between physical therapists and physical therapist students when visually evaluating athletes’ movement proficiency during biomechanical screening assessments. Design: This was an observational study. Methods: Twenty-seven physical therapists and 20 physical therapist students assessed 100 video recordings of athletes performing 4 lower-extremity biomechanical screening assessments: squat, lunge, single leg squat, and deadlift. Analysis was completed on conditioned and unconditioned data. In the conditioned data, technique deviations were induced purposefully by the athletes. In the unconditioned data, deviations occurred naturally due to increased weight or movement complexity. In order to determine levels of agreement in the assessments, participants were required to classify the athletes’ movement as acceptable or aberrant. Each participant assessed the same video recordings on 2 separate occasions at least 30 days apart. Agreement levels were determined using Cohen κ and Fleiss κ. Results: Kappa scores at an interrater level ranged from 0.18 to 0.53, and intrarater agreement ranged from 0.38 to 0.62. Levels of agreement were higher in the conditioned data compared with the unconditioned data. Overall, the lunge and squat produced higher levels of agreement than the deadlift and single-leg squat. Students and physical therapists demonstrated similar levels of agreement. Limitations: Screening assessments were evaluated through the use of video analysis. Conclusions: Greater efforts are needed to ensure standardization of movement analysis.
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.
Neuromuscular training programmes have demonstrated success in the rehabilitation of ankle joint injuries, as well having proven success in reducing the risk of injury recurrence. However athlete motivation to do these exercises can be poor, with many athletes performing their exercises incorrectly when they are not supervised by their trainer/therapist.
Concussion is one of the most common sports-related injuries, with little understood about the modifiable and nonmodifiable 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 ± SD) were as follows: age, 22.6 ± 3.6 years; height, 185 ± 6.5 cm; weight, 98.9 ± 12.5 kg; body mass index, 28.9 ± 2.9 kg/m2; and leg length, 98.8 ± 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.
Analysis of lower limb exercises is traditionally completed with four distinct methods: (1) 3D motion capture; (2) depth-camera-based systems; (3) visual analysis from a qualified exercise professional; and (4) self-assessment. Each method is associated with a number of limitations. Objective: The aim of this systematic review is to synthesise and evaluate studies which have investigated the capacity for inertial measurement unit (IMU) technologies to assess movement quality in lower limb exercises. Data Sources: A systematic review of studies identified through the databases of PubMed, ScienceDirect and Scopus was conducted. Study Eligibility Criteria: Articles written in English and published in the last 10 years which investigated an IMU system for the analysis of repetition-based targeted lower limb exercises were included. Study Appraisal and Synthesis Methods: The quality of included studies was measured using an adapted version of the STROBE assessment criteria for cross-sectional studies. The studies were categorised into three groupings: exercise detection, movement classification or measurement validation. Each study was then qualitatively summarised. Results: From the 2452 articles that were identified with the search strategies, 47 papers are included in this review. Twenty-six of the 47 included studies were deemed as being of high quality. Conclusions: Wearable inertial sensor systems for analysing lower limb exercises is a rapidly growing field of research. Research over the past 10 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.
The purpose of this study was to examine the test-retest reliability, usefulness, and validity of field-based devices, in determining jump height (JH) during a countermovement jump (CMJ). Twenty-one male (22.8 ± 2.4 years; 1.82 ± 0.07 m; 86.0 ± 10.4 kg) and 7 female field sport athletes (20.5 ± 1.5 years; 1.65 ± 0.06 m; 65.4 ± 7.2 kg) performed 3 CMJs with data simultaneously recorded using a force plate (criterion measure), Optojump, Output Capture, and Push-Band 2.0. Reliability was determined by intraclass correlation (ICC) and coefficient of variation (CV) analyses. Usefulness was assessed by comparing typical error (TE) with the smallest worthwhile change (SWC), and the validity analyses involved repeated measures analysis of variance with post hoc analysis, Pearson correlation coefficient (r), coefficient of determination, and Bland-Altman 95% limits of agreement analyses. All 3 field-based devices were deemed reliable in assessing CMJ height as the respective ICCs ≥ 0.80 and the CV ≤ 10%. Only the Optojump and Output Capture devices were rated as “good” at detecting the SWC in performance (Optojump SWC: 1.44 > TE: 1.04; Output Capture SWC: 1.47 > TE: 1.05). The Output Capture device demonstrated acceptable validity for CMJ height assessment, whereas the Push-Band 2.0 showed systematic bias when compared with the criterion force plate data. Systematic difference was also evident for the Optojump potentially due to the optical switching-cell position on the Optojump. Although all 3 devices showed excellent reliability, the Optojump and Output Capture devices offer practitioners a cost effective, reliable, and valid method of assessing the smallest worthwhile change in CMJ performance in an applied setting.