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Performance testing and tracking is vital to ensure athletes reach goals in a safe and efficient manner. It can be used to quantify athletic capability during certain predefined activities and the information derived is used to inform decisions regarding training programmes and injury risk stratification. It is usually completed using two distinct methods – subjective and objective analysis.
Objective analysis traditionally involves using equipment such as 3D motion capture, force plates laser based and EMG systems. Such equipment can accurately estimate key performance indicators such as joint kinetics, kinematics and muscle activity. Once equipment is calibrated correctly, they provide objective data that is consistent and reliable which allows for informed decision making and the ability to demonstrate progress to the athlete/client.
Examples of traditional objective performance analysis tools including force plates, linear positional transducers and laser based systems.
However, using this equipment has a number of disadvantages associated with it. Access is often limited to elite athletes and researchers (1). Even when available, the equipment is very expensive in terms of initial outlay, general upkeep and user training. Furthermore, it is time consuming to set up correctly and interpret the data obtained.
This form of analysis primarily involves the visual evaluation of an athlete. It is used because it is quick, inexpensive, and requires minimal equipment. However, evaluating performance in this manner often involves the use of ambiguous grading criteria and practitioner intuition, leaving it prone to bias. This is compounded by the difficulty of having to simultaneously assess numerous performance components simultaneously. Furthermore, it is difficult to demonstrate improvements to athletes/clients when assessing performance in a subjective manner due to the lack of hard data points to illustrate progress.
Subjective visual evaluation of an individual's movement kinematics during a single leg squat
But, perhaps the most challenging aspect of analysing performance in a subjective manner is ensuring consistency of measurement. Previous research has investigated the between rater (inter-rater) and within rater (intra-rater) agreement and reliability for a number of screening assessments (2-6). In general, the research indicates varying levels of agreement and that this agreement is influenced by factors such as clinician experience as well as the complexity of the screening assessment itself.
Low levels of agreement lead to a number of issues. Firstly, an athlete’s performance levels and/or risk of injury may be incorrectly stratified (i.e. high risk or low risk) depending on when they were assessed, and the practitioner involved. Low levels of agreement mean changes in response to a specific intervention cannot be assessed consistently. Finally, it can lead to the potential for different practitioners to change athlete management based on their interpretation, compromising athlete management continuity.
IMUs can acquire tri-planar inertial acceleration and rate of angular rotation. With appropriate signal processing they are capable of providing data that is objective, accurate and reliable in a variety of performance tasks (7, 8). Their size means that devices that incorporate them are generally small and unobtrusive meaning they can be used in outside of laboratory and gym settings. Furthermore, the costs associated with purchasing and maintaining IMU sensors are less than those of traditional performance testing equipment.
Output - an IMU based system
Because of the reasons listed above, IMUs have become increasingly commonplace in sports and rehab settings to assess performance. These devices can be augmented with desktop tools that allow athletes and patients to track their progress and ensure they reach goals in a safe and effective manner.
Users of the Output system, an IMU based system, have found the same when analysing patients stating “Output has enhanced the way we work by allowing us to go beyond our usual assessment metrics. This has really helped with how I prepare athletes to return to play by ensuring rehabilitation programmes address the demands required in their sport”.
An example of adding objective analysis to a previously subjective assessment can be seen when assessing an individual’s stability during a static balance task as shown in the video below. While it is difficult to assess a marked difference in performance with purely visual evaluation, the IMU system is capable of demonstrating an objective data point which can be used as a performance marker in future.
The measurement displayed represents the amount of variation from a stationary position that indicates an individual’s overall stability. This data can then be used to show improvements or move individuals back to their baseline measurement.
Longitudinal tracking of an individual's stability during a static single leg balance task using Output Hub.
'Use technology but don’t let the technology use you'
Accurate performance tracking and assessment is vital to allow athletes and patients achieve their goals. Ideally, this should be completed in a systematic and objective manner to ensure optimum outcomes. Increasingly, IMU based systems are being used to achieve this due to their unobtrusive nature, price point and portability.
However, it is important to ensure that the data from these systems is used and interpreted appropriately and feeds into a decision-making process rather than making these decisions for practitioners. As a whole, use the technology but don’t let the technology use you!
The Inertial Sensor: A Base Platform for Wider Adoption in Sports Science Applications. Journal of Fitness Research. Espinosa HG, Lee J, James DA. 2015;4(1):13-20.
Determining Interrater and Intrarater Levels of Agreement in Students and Clinicians When Visually Evaluating Movement Proficiency During Screening Assessments. D Whelan, E Delahunt, M O'Reilly, B Hernandez, B Caulfield. Physical therapy 99 (4), 478-486
Interrater Reliability of the Functional Movement Screen. Minick KI, Kiesel KB, Burton L, Taylor A, Plisky P, Butler RJ. The Journal of Strength & Conditioning Research. 2010;24(2):479-86.
Intrarater Reliability of the Functional Movement Screen. Gribble PA, Brigle J, Pietrosimone BG, Pfile KR, Webster KA. The Journal of Strength & Conditioning Research. 2013;27(4):978-81.
Reliability and Validity of Observational Risk Screening in Evaluating Dynamic Knee Valgus. Ekegren CL, Miller WC, Celebrini RG, Eng JJ, Macintyre DL. Journal Of Orthopaedic & Sports Physical Therapy. 2009;39(9):665-74.
Physiotherapist Agreement When Visually Rating Movement Quality During Lower Extremity Functional Screening Tests. Physical Therapy in Sport. Whatman C, Hing W, Hume P. 2012;13(2):87-96.
A Least-Squares Identification Algorithm for Estimating Squat Exercise Mechanics Using a Single Inertial Measurement Unit. Bonnet V, Mazza C, Fraisse P, Cappozzo A. Journal of Biomechanics. 2012;45(8):1472-7.
Validation of the Angular Measurements of a New Inertial-Measurement-Unit Based Rehabilitation System: Comparison with State-of-the-Art Gait Analysis. Leardini A, Lullini G, Giannini S, Berti L, Ortolani M, Caravaggi P. Journal of Neuroengineering and Rehabilitation. 2014;11(1):1-7.