Darragh Whelan

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.

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.

Does external walking environment affect gait patterns

The objective of this work is to develop an understanding of the relationship between mobility metrics obtained outside of the clinic or laboratory and the context of the external environment. Ten subjects walked with an inertial sensor on each shank and a wearable camera around their neck. They were taken on a thirty minute walk in which they mobilized over the following conditions; normal path, busy hallway, rough ground, blind folded and on a hill. Stride time, stride time variability, stance time and peak shank rotation rate during swing were calculated using previously published algorithms. Stride time was significantly different between several of the conditions. Technological advances mean that gait variables can now be captured as patients go about their daily lives. The results of this study show that the external environment has a significant impact on the quality of gait metrics. Thus, context of external walking environment is an important consideration when analyzing ambulatory gait metrics from the unsupervised home and community setting.

Classification of lunge biomechanics with multiple and individual inertial measurement units

Abstract 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.

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.

Technology in Strength and Conditioning Tracking Lower-Limb Exercises With Wearable Sensors

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 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.

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.