Martin O'Reilly

Evaluating squat performance with a single inertial measurement unit

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.

Evaluating Performance of the Single Leg Squat Exercise with a Single Inertial Measurement Unit

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.

Objective Classification of Dynamic Balance Using a Single Wearable Sensor

4th International Congress on Sport Sciences Research and Technology Support 2016, Porto, Portugal, 7-9 November 2016

Classification of deadlift biomechanics with wearable inertial measurement units

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 51.2Hz, 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.

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

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.

Binary classification of running fatigue using a single inertial measurement unit

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 Sensor Technology Can Capture Changes in Dynamic Balance Control during the Y Balance Test

Introduction: 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.

The influence of feature selection methods on exercise classification with inertial measurement units

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.

Inertial sensory data provides depth to clinical measures of dynamic balance

Study Design Case Study. Objectives Establish the role a single inertial sensor may play in the objective quantification of dynamic postural stability following acute ankle injuries. Background 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 those with acute ankle injuries and chronic ankle instability. However, reach distances fail to provide information relating to the quality of balance strategy and dynamic stability. Motion capture systems are often employed to provide micro-level detail pertaining to an individual’s postural stability. However, such systems are expensive, lack accessibility, hinder natural movement and require extensive processing expertise. The addition of inertial sensors may allow for the inexpensive, accessible quantification of postural stability in an unconstrained environment. Case Description Forty-two elite under-20 rugby union players were recruited as part of a wider study. Two athletes were identified to have sustained acute ankle injuries two weeks previously; one lateral ankle sprain and one deltoid ligament sprain. A single inertial sensor was mounted at the level of the 4th lumbar vertebra. Participants completed four practice YBTs bilaterally, prior to completing 3 recorded YBTs. Reach distance and inertial sensor data were recorded for each reach excursion. Outcomes When compared to the group mean, both athletes demonstrated no clinically meaningful reduction in reach distances for all three reach directions. However, both athletes demonstrated a higher 95% ellipsoid volume of sway than the healthy control group for all three directions of the YBT when completed on their affected limb. Conclusions Preliminary analysis suggests that inertial sensor data may provide information relating to the quality of dynamic postural stability following an acute ankle injury. Further investigation is required to establish the role that such measures may play in the assessment and management of ankle injuries.

Use of body worn sensors to predict ankle injuries using screening tools

Background 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.