Equipment and testing protocol

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Introduction

Gait analysis is performed on a routine basis by clinicians all around the world. The most common method for gathering gait analysis data is to do an observational gait analysis (OGA). Nowadays the demand for evidence based practice is increasing, and since OGA is a subjective method of analyzing, it conflicts with the demand for validated results.
A known validated method for gait analysis is 3-dimensional optoelectronic motion analysis. The systems used to perform such an analysis are comprehensive, often expensive and more commonly used for research. They are seldom used in clinical practice, first and foremost because of the cost, secondly since it is time demanding and impractical, and thirdly the personal operating the system has to have a thorough education and experience to operate the system.
What clinicians are looking for is an analysis method that can aid them in improving the OGA, but still be practical to use in a clinical setting. It should be able to quantify gait data and be validated. Video-based 2-dimensional motion analysis systems are recognized as a useful tool for gait analysis. It does not require an as in-depth knowledge as 3D systems, and the equipment used is commonly available and reasonably cheap. To the authors knowledge there have been few studies concerning clinical validation of 2D video-based motion analysis, of which there are several different systems, so the use of it as the basis for evidence-based decision making can be questioned.
The aim of this study was to validate Hu-m-an^TM video-based 2D analysis (HMA Technology Inc. Ontario, Canada), by comparing simultaneous measurements with the 3D motion analysis system: Qualisys Tracking Manager (Qualisys Medical AB, Gothenburg, Sweden).
Randomily selected sagital plane relative angles for the knee and ankle during gait were analyzed, as well as the angles at initial contact.

Background

There are different opinions among clinicians on the value of gait analysis as a clinical tool. The relevance of a basic gait analysis, for assessing the patient with gait disorders in a clinical setting, is generally recognized. The discussion revolves around whether the methods for clinical application should be validated in the same way as those used for research, and if using 3D analysis outside research projects is beneficial (Baker, 2006).
Baker (2006) states that the criterion for valid clinical research is not the same as that for clinical testing. In clinical testing there is only one subject, and random errors within the test can influence the results and make them unreliable. By increasing the number of subjects for research purposes, the effect of the random errors can be reduced and allow for meaningful conclusions. This fact influences the validity of using gait analysis in clinical practice.
The areas of interest, for which data can be collected, are kinetics, kinematics and dynamic EMG (Barker, Craik, Freedman, Herrmann & Hillstrom., 2006). The variables most often analyzed are kinematic variables (Smidt, 1974). Though kinematic results are more easily interpreted, it must be noted that it only describes a patient’s movement, whereas kinetics can determine the cause of the movement. Sagital plane kinematics are more reliable than frontal and transverse, particularly angle measurements for the larger joints such as hip and knee, compared to the measurements for the ankle (Krebs, Edelstein & Fishman, 1985; Kadaba et al., 1989). With modern technology quantification of motion data is now easier. The results are not intended to form the basis of medical diagnostics, but instead aid in choosing the appropriate intervention for patients with gait disorders, and assess the outcome of the intervention (Simon, 2004).
The most utilized type of gait analysis is OGA, but Krebs et al. (1985) found the reliability of observational kinematic gait analysis to be only moderate at best. Even though the clinical settings for this study provided better terms for doing an accurate and repeatable analysis, than would be encountered in practice, and the clinicians had considerable experience, the reliability was still lower than expected.
Coutts (1999) states that OGA is insufficiently reliable and should not be used on its own, unless done by a clinician with a high level of expertise. The analyses should be supplemented with some sort of objective test to get a reliable measure. Videotaping the gait allows for repeated viewing, slow motion and freezing of a specific frame, which will improve the reliability of OGA.
The demand for evidence based practice is increasing with the added pressure from the authorities on the clinicians to state the cost and quality of life ratio, before reimbursement of intervention will be approved (Nielsen, 2002). The arguments for reimbursement should be based on well defined research publications, and the methods for assessing the patients need to be validated, to ensure that the result can be trusted as a base for a treatment plan.
Optoelectronic 3D motion analysis systems have been validated as a patient assessment tool, but still have their limitations (Richards, 1999). One of the major limitations to the systems is the need for applying skin markers on the patient. The placement of the markers has an influence on the kinematic results (Kadaba et al., 1989; Kadaba, Ramakrishnan & Wootten, 1990). The markers are placed manually based on anatomical landmarks palpated at application. Differences can occur between different clinicians applying the markers on the same patient (inter-class correlation), and also between applications done by the same clinician (intra-class correlation) (Kadaba et al., 1990). Since the markers are placed on the skin, their movement is influenced by the movement of the soft tissue underneath. Benoit et al. (2006) did a study determining the effect of skin movement on the kinematics for the knee joint during gait, by comparing skin markers to pin in bone markers. They found that although the skin markers could provide repeatable results, it could not be compared with the kinematics retrieved from the pin in bone analysis. Therefore kinematic analysis is prone to errors resulting from the movement of the soft tissue, and this should be considered when interpreting kinematic data.
The major reason that 3D optoelectronic gait analysis are not used more frequently in clinical practice, even though it has been proven to be a valid method of testing, is the time and expense factor. It is a considerable investment to purchase the equipment and very few clinics can afford it. The gait analysis system must pay for itself within a reasonable time span. This is often only possible if the gait laboratory runs full time, which is rarely manageable in practice. Therefore gait laboratories are often regarded as inefficient, unproductive, and uneconomical (Simon, 2004).
An alternative method for quantifying gait analysis data is to use 2D video-based motion analysis. It is a simpler and less expensive analysis method, where all that is required is a digital video camera, and a digitizing software program that can be bought at a reasonable price. For these reasons 2D video-based motion analysis is being used more frequently in clinical practice. The issue with 2D analysis is whether or not it can be validated for clinical use.
Churchill, Halligan & Wade (2002) did a study validating the use of the Rivermead video-based clinical gait analysis method (RIVCAM), which is based on the principle of 2D video-based motion analysis. Among others they tested the accuracy of angle measurement of a fixed angle, approximately 69°-70°, that is moved along a walk line, and found a standard deviation (SD) of 0,46°. Finally three illustrative trials were analyzed; one from a “normal” subject, and two from a hemiplegic subject before and after orthotic intervention. This was done in order to try and assess the utility of the system in clinical practice. The results were promising but the significance can be discussed since the number of subjects was limited.
When working with 2D there are a couple of things that need to be considered. One is the phenomenon of parallax error, which occurs when objects are viewed away from the optical axis of the camera. This error can be minimized by aligning the optical axis with the center of the motion (Kirtley, 2006). Another thing is perspective errors, or out-of-plane errors. These occur when the object is moving out of the calibrated plane, where the actual size of the object is known. If the object moves closer or farther away from the camera, than the distance to the calibrated plane, the presumed size will be incorrectly measured (Sih, Hubbard & Williams, 2001).
Errors can occur in the measurement of angles, if an analysis is performed on a transversal rotation, where the limb rotates out of the plane, e.g. a cerebral palsy child with excessive femoral rotation. Mathematical corrections can be implemented to correct this error but Stevens, Schmitt, Cole and Chan (2006) stated that for transversal rotations, less than 20° out of plane, it is not recommendable to make this adjustment. Correction of this error does not result in significant different outcomes than the non-adjusted outcomes.
This study examines criterion-based, concurrent validity by simultaneous gait analyses with 2D video-based Hu-m-an digitizing software (HMA Technology Inc. Ontario, Canada) and a 3D optoelectronic system: Qualisys Tracking Manager (Qualisys Medical AB, Gothenburg, Sweden). The criterion measurement system is the Qualisys Tracking Manager.

Method
Subjects

18 subjects, 8 males and 10 females, were recruited for the study at Jönköping University. The subjects were included on the criteria that they did not have any pathological symptoms influencing their gait. The mean age was 24,72 years (SD 1,96 years), mean height was 173,50 cm (SD 9,03 cm) and mean mass was 66,94 kg (SD 10,20 kg).
Prior to the study a student’s ethical consent form was filled in. The subjects were informed orally about their rights and the ethical consideration concerning this study before any testing began. They were allowed to leave the study at any time and their anonymity was ensured.
Equipment and testing protocol
Testing was performed in the Biomechanics laboratory at the school of health sciences, Jönköping University. The subjects were asked to walk at a self-selected walking velocity along a 10 m walkway while being recorded by both the 2-dimensional and 3-dimensional system. The direction in which the subjects walked was randomized.
The 3-dimensional optoelectronic motion data was recorded using eight motion capture cameras, ProReflex 120, with a recording speed of 100 Hz. The data was collected using Qualisys Tracking Manager (Qualisys Medical AB, Gothenburg, Sweden).
Before testing the system was calibrated to less than 1,5 mm in error of length, within a calibration area of 3m x 1,2m x 0,7m, which corresponds with the area where the 2D analysis should be performed. The subjects were fitted with reflective markers on the lower limbs according to the cluster marker model (see Figure 1). See appendix I for detailed information.
In order to capture a video sequence for 2-dimensional analysis, a digital video camera (Canon XM2 PAL Mini, Canon Inc., Tokyo, Japan) was set up so that it could record the subjects in the sagital plane. The camera was placed on a level tripod, perpendicular to the center of the pathway at a distance of 3 meters, and 55 cm above the floor (see Figure 2). Since it was the lower limb that was to be analyzed, the camera was set in this height so that the optical axis of the camera was aligned with the knee. Any changes of the picture related to the shape of the standard lens (Canon Professional L-series Fluorite Lens, Canon Inc., Tokyo, Japan) would then be evenly distributed over the thigh and shank. This setting ensured that the calibration area covered the lower limb. The camera was set to view 3 of the 10 meters of the walkway. The gait was captured in 25 Hz with a shutter speed of 1/1000 seconds.

Table of contents
Introduction
Background
Method
Subjects
Equipment and testing protocol
Analysis
Statistical analyses
Results
Relative knee and ankle angles randomized during gait
Angles at Initial contact (IC)
Angles during stance
Angles during swing
Intra-class correlation
Discussion
Results
Methods
Limitations
Conclusion
Acknowledgement
References
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Comparison of angular measurements by 2D and 3D gait analysis