Mentiplay et al. Journal of Foot and Ankle Research 2013, 6:14
http://www.jfootankleres.com/content/6/1/14
RESEARCH
JOURNAL OF FOOT
AND ANKLE RESEARCH
Open Access
Reliability and validity of the Microsoft Kinect for
evaluating static foot posture
Benjamin F Mentiplay1, Ross A Clark1, Alexandra Mullins1, Adam L Bryant2, Simon Bartold2 and Kade Paterson1*
Abstract
Background: The evaluation of foot posture in a clinical setting is useful to screen for potential injury, however
disagreement remains as to which method has the greatest clinical utility. An inexpensive and widely available
imaging system, the Microsoft Kinect™, may possess the characteristics to objectively evaluate static foot posture in
a clinical setting with high accuracy. The aim of this study was to assess the intra-rater reliability and validity of this
system for assessing static foot posture.
Methods: Three measures were used to assess static foot posture; traditional visual observation using the Foot
Posture Index (FPI), a 3D motion analysis (3DMA) system and software designed to collect and analyse image and
depth data from the Kinect. Spearman’s rho was used to assess intra-rater reliability and concurrent validity of the
Kinect to evaluate foot posture, and a linear regression was used to examine the ability of the Kinect to predict
total visual FPI score.
Results: The Kinect demonstrated moderate to good intra-rater reliability for four FPI items of foot posture (ρ = 0.62
to 0.78) and moderate to good correlations with the 3DMA system for four items of foot posture (ρ = 0.51 to 0.85).
In contrast, intra-rater reliability of visual FPI items was poor to moderate (ρ = 0.17 to 0.63), and correlations with
the Kinect and 3DMA systems were poor (absolute ρ = 0.01 to 0.44). Kinect FPI items with moderate to good
reliability predicted 61% of the variance in total visual FPI score.
Conclusions: The majority of the foot posture items derived using the Kinect were more reliable than the
traditional visual assessment of FPI, and were valid when compared to a 3DMA system. Individual foot posture
items recorded using the Kinect were also shown to predict a moderate degree of variance in the total visual FPI
score. Combined, these results support the future potential of the Kinect to accurately evaluate static foot posture
in a clinical setting.
Keywords: Foot morphology, Injury screening, Clinical assessment, Lower limb, Biomechanics, Anthropometry
Background
Abnormal foot posture and mechanics have long been
associated with lower limb injuries [1-4]. For instance,
clinical measures of a pronated foot posture, such as a
low arch [5] and excessive navicular drop [6], have been
retrospectively identified with knee injuries and anterior
cruciate ligament injury respectively. Similarly, measures
indicating a more supinated foot posture, such as a
high arch [7] and rearfoot varus [3] have been retrospectively associated with stress fractures and patellofemoral pain respectively. Consequently, the measurement
* Correspondence: kade.paterson@acu.edu.au
1
School of Exercise Science, Faculty of Health Sciences, Australian Catholic
University, Melbourne, Australia
Full list of author information is available at the end of the article
and classification of foot posture in a clinical setting has
become a central focus of lower extremity medicine, and
now is widely used to evaluate injury risk and monitor
treatment efficacy.
Despite the existence of many different techniques to
evaluate foot posture in the clinical setting, there is still
disagreement as to which method is the most clinically
useful [8]. Indeed, some studies have found contrasting
results regarding the association between abnormal foot
type and injury depending on the clinical technique
employed [3,4,9,10], with some researchers arguing that
these conflicting findings may be at least partly due to
the lack of reliability and validity of many of these measures [11-13]. Moreover, the inability of many of the
© 2013 Mentiplay et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Mentiplay et al. Journal of Foot and Ankle Research 2013, 6:14
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static measures of foot posture to predict dynamic function also calls in to question their clinical utility [14,15].
To address these concerns, Redmond, Crosbie and
Ouvrier [16] developed a subjective measure of static
foot posture termed the Foot Posture Index (FPI). The
FPI is comprised of one palpatory and five visual criteria
used to determine whether the foot is in a supinated,
neutral or pronated position [16]. Research has reported
that the FPI possesses acceptable intra-rater reliability
[1,17,18], and the tool has been validated against both
static and dynamic three-dimensional (3D) lower limb
models [16]. However, despite these advantages, the subjective nature and limited five-point Likert-type scoring
scale of the FPI may limit the tool’s precision, with some
researchers suggesting that the results need to be
interpreted with caution and may actually have limited
value, especially in a research setting [18]. Consequently,
there remains a need for an inexpensive, portable and
accurate assessment tool that can quantifiably assess
static foot posture, which could be implemented in a
clinical setting for everyday patient assessment.
The Microsoft Kinect™ is an inexpensive and portable video game accessory that combines a video and
infrared-sensing camera to create a 3D model of the
body. Recent research has shown that the Kinect system
is capable of creating this 3D human model with similar
accuracy to expensive and complex 3D body scanning
systems [19]. Similarly, early work has also shown promising results for the Kinect to evaluate gait velocity [20],
hand and elbow movements [21] and anatomical landmark displacement and trunk angle [22] when compared
to 3D motion analysis systems. Combined, these studies
demonstrate that the Kinect is able to obtain some kinematic and anatomical mapping data with a similar degree of accuracy to more expensive 3D motion analysis
and scanning systems [19,22]. Consequently, the Kinect
may have the potential to objectively evaluate static foot
posture in a clinical setting with more accuracy than the
subjectively based FPI. This in turn may permit better
injury prediction accuracy, increased measurement reliability and improved clinical utility. Therefore, this
study aimed to evaluate whether the Kinect is able to reliably and validly evaluate static foot posture, as measured using the FPI. A secondary aim was to validate the
Kinect-derived data with assessment of static foot posture using a 3D motion analysis system. Lastly, a third
aim was to examine whether Kinect measures of foot
posture were able to predict the variance in the total visual FPI score.
Methods
Participants
A convenience sample of 30 young, healthy males
(age: 22.2 ± 3.2 years, height: 177.4 ± 5.0 cm, mass:
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74.4 ± 4.7 kg, leg length - right limb: 92.9 ± 3.9 cm, knee
width - right limb: 9.9 ± 0.7 cm, ankle width – right limb:
7.5 ± 0.3 cm) with no lower limb injuries in the prior two
months volunteered to participate in this study. Only
males were recruited due to the potential influence of
menstrual cycle phase on lower limb function and tendon
mechanical properties and the likely impact of this on test
re-test reliability data [23,24]. Participants completed informed consent forms prior to testing and all procedures
were approved (approval number 2012 47V) by the
Australian Catholic University Human Research Ethics
Committee.
Procedure
This study used a concurrent validity, test re-test reliability design to determine if the Kinect can reliably and
validly evaluate static foot posture, using items described
for the FPI. This design validated the accuracy of measuring the FPI using the Kinect against the traditional
visual observation of the FPI and a benchmark reference,
the Vicon motion analysis system. Additionally, the repeated measures design was used to evaluate the reliability of measuring the FPI using the Kinect.
Eligible participants attended two testing sessions,
seven days apart (median = 7; IQR = 5 to 9). Basic anthropometric data were recorded in the first session,
such as height (cm), mass (kg), leg length (cm), knee
width (cm) and ankle width (cm). Next, each participant’s foot posture was evaluated by visual observation
(FPI), and by using both the Vicon and Kinect systems
as described below. Participants were required to return
approximately one week following the initial testing
session where these foot posture measurements were
repeated.
Assessment of the Foot Posture Index using visual
observation
The Foot Posture Index (FPI) is a validated, subjective
measure that is widely used in a clinical setting, and
which is comprised of six measurements of the foot to
evaluate static foot posture [16]. The FPI has been shown
to possess good intra-rater reliability (ICC = 0.81 – 0.94),
however inter-rater reliability has only been shown to be
moderate to good (ICC = 0.53 – 0.79) [1,17,18]. The FPI
has also been validated against an electromagnetic motion
tracking system with results demonstrating that the
six-item FPI total score predicted 64% of the variation
(R2 = 0.64, p < 0.001) in static measurements [16].
The six-item version of the FPI includes evaluation of
talar head palpation (FPI item 1), supra and infra lateral
malleolar curvature (FPI item 2), calcaneal inversion/
eversion (FPI item 3), talo-navicular joint bulging (FPI
item 4), congruence of the medial longitudinal arch (FPI
item 5), and forefoot abduction/adduction (FPI item 6)
Mentiplay et al. Journal of Foot and Ankle Research 2013, 6:14
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[16]. A five-point Likert-type scale is used to score each
of these items, from −2 to +2, with zero being the central value or a neutral position and negative and positive
values indicating a more supinated or pronated position
respectively. A total FPI score of above +10 indicates
a highly pronated foot posture, +6 to +9 indicates a
pronated foot posture, 0 to +5 indicates a normal or
neutral foot posture, -1 to −4 indicates a supinated foot
posture while a score of −5 to −12 indicates a highly supinated foot posture [16].
Participants were told to assume a relaxed comfortable
stance with their right foot on a straight line drawn on
the ground. This line was orientated on the Y-axis of the
laboratory to align the foot with the axes of the global
coordinate system. Their foot was then positioned so
that the posterior calcaneus and the interspace between
the second and third metatarsophalangeal joints were
bisecting the line, aligning the central axis of the foot
with the axis of the laboratory reference frame. Scores
for each of the six items of the FPI were recorded after
visual observation of the participant’s right foot. This
was completed on each of the two sessions, approximately one week apart, with the same rater recording
visual FPI scores for every session who was blinded to
any earlier scores. The rater in this study was a researcher who received three one-hour training sessions
for the FPI prior to the study from an experienced podiatrist. The same researcher conducted all analyses of
the Vicon and Kinect systems.
Assessment of the Foot Posture Index using the Vicon
analysis system
An eight camera visible red Vicon 3D motion analysis
system (Vicon, Oxford, United Kingdom) sampling at
200 Hz was used to determine each of the FPI items,
with the exception of talar head palpation. To evaluate
the static FPI using the Vicon system, a calibration wand
was used to identify the items of the modified FPI. Following calibration, the system used the fixed markers on
the wand to determine the position of the wand tip in
3D space [25]. Pointing the wand tip at specific anatomical landmarks, or tracing regions using the wand tip,
may be used to provide a high degree of accuracy without the limitation of soft tissue artefact or marker placement inaccuracy [26]. Five different trials were captured,
being lateral curvature, lateral and medial landmarks, rearfoot mesh and medial mesh trials as explained below.
To establish the lateral malleolar curvature (FPI item
2), the proximal and distal groove sizes were calculated
from the virtual marker, and the proximal groove was
subtracted from the distal groove. This was done by
plotting the vertical and mediolateral position of the
wand tip (in the anatomical reference frame) on a twodimensional (2D) scatter plot for visualisation using the
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data from this trial. This created a graph that represented the lateral surface of the lower leg along a slice
running from mid-shank to the plantar calcaneal fat pad.
The distal groove was identified based on the minimum,
or most medial, wand tip position below the malleolus,
while the proximal groove was deemed the minimum
value above the malleolus.
To assess calcaneal inversion/eversion (FPI item 3), a
rearfoot mesh trial was used. The mesh was created by
sweeping the wand tip across the rearfoot mediolaterally,
gradually moving from the most inferior to superior
point on the calcaneus, with the vertical distance between each sweep within 5 mm. The most posterior
point of the calcaneus was found for each sweep, with
this position identified by plotting the medial-lateral and
anterior-posterior positions of the marker on a 2D scatter plot for visualisation. The single most posterior position of the wand tip in each medial-lateral sweep was
then identified and extracted. The resultant angle of
these points to the vertical was established using a leastsquare error linear fit to calculate calcaneal inversion/
eversion.
Talo-navicular joint bulging (FPI item 4) was determined by using the medial landmarks trial, calculated by
measuring the medial-lateral displacement of the navicular tuberosity in relation to the medial calcaneus. Congruence of the medial longitudinal arch (FPI item 5) was
calculated from a medial mesh trial and split into two
categories, arch height and arch peak. The wand tip was
swept across the medial foot vertically, gradually moving
from the medial calcaneus to the head of the first metatarsal. In this case, the most medial position of the wand
tip during each vertical sweep was extracted, and was
plotted on a 2D scatter plot for visualisation of the medial arch. These extracted points were then smoothed
using a polynomial filter and the most superior point on
the plot was deemed the arch height. Additionally, the
anterior-posterior position of the arch height point was
also expressed as a percentage distance from the calcaneus to the head of the first metatarsal, termed arch
peak. Therefore, FPI item 5 was compared to two measures, arch height and arch peak, derived from the Vicon
system.
Lastly, forefoot abduction/adduction (FPI item 6) used
both the lateral and medial landmark trials. The mediallateral displacement between the lateral calcaneus and
the head of the fifth metatarsal was compared with the
medial-lateral displacement between the medial calcaneus
and the head of the first metatarsal. The medial displacement was then subtracted from the lateral displacement
to give a score for forefoot abduction/adduction. Refer to
Additional file 1 for further detail and images regarding
the assessment and data analysis of foot posture using the
Vicon 3D motion analysis system.
Mentiplay et al. Journal of Foot and Ankle Research 2013, 6:14
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Assessment of the Foot Posture Index using the Microsoft
Kinect™
Each of the FPI items, with the exception of talar head
palpation, was also captured using a Microsoft Kinect
camera. The video camera can record images at a variety
of resolutions, with the resolution for this study set at
640×480 pixels. The infrared-sensing camera acts as a
depth sensor that determines the distance of objects in
front of the Kinect, and was used to obtain a calibrated
depth map of this area at a resolution of 320×240 pixels.
The precision of the depth map becomes exponentially
worse as the distance from the Kinect increases, however it has been shown to possess a precision of < 3 mm
at the range used in this study [27]. Additionally, although the individual pixel data is somewhat noisy, the
use of a 2D median filter allows for accurate depth mapping to be performed [28].
To acquire the Kinect data, the device was firstly
placed on the ground at a distance of 100 cm from where
the participant was required to stand, and a calibration
technique was performed to set the global reference
frame [22]. In each position, the participant’s right foot
was placed on a line drawn on the ground 100 cm from
the Kinect. The items of the modified FPI (talar head
palpation being excluded) were then acquired in lateral,
posterior and medial views of the foot whilst the participant stood as still as possible. To allow the camera to see
the medial aspect of their foot, the participant’s left foot
was placed in a comfortable position behind them and
they were instructed to keep their right shank perpendicular to the ground and to have approximately 50% of
their body mass through their right foot.
Custom made LabVIEW software (National Instruments, U.S.A.) was used to collect and analyse the data
from the Kinect using the Microsoft Software Development Kit (SDK) Beta 2 (Microsoft, U.S.A.). Data were
sampled at the native frequency of the Kinect, which is
irregular at ≈ 30Hz. The depth image was converted to
the same resolution as the video image using interpolation, and the two images were aligned using a cross
correlation function. The real world coordinates of the
pixels in the video and depth images were determined
using the calibration data extracted from the SDK.
When anatomical landmarks or specific positions needed to be identified, these were located using the video
image and the corresponding points on the depth map
were extracted. For all values the median of five consecutive frames of Kinect data were used.
The lateral position trial was used to determine the
lateral malleolar curvature (FPI item 2). Specifically, the
video image was used to place cursors along a vertical
line that had previously been drawn on the participant’s
leg to represent the middle of the lateral aspect of their
shank. The proximal and distal groove size was then
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calculated from the corresponding depth sensor data,
and the proximal groove was subtracted from the distal
groove. To remove noise and improve the accuracy of
the depth data, a five point median filter with the centre
positioned on the line drawn on the participant’s leg was
applied to each vertical pixel row.
For calcaneal inversion/eversion (FPI item 3) the rearfoot position trial was used. The most posterior points
of the calcaneus in each pixel row were determined from
the depth sensor data and the resultant angle of these
points was calculated against the vertical.
Talo-navicular joint bulging (FPI item 4) was determined from the medial trial by calculating the mediallateral displacement of the navicular tuberosity in relation
to the medial calcaneus. This was done through the identification of these landmarks by manually placing cursors
on the video image and using the corresponding points on
the depth map.
The medial trial was also used to determine the congruence of the medial longitudinal arch (FPI item 5) in a
similar way to the Vicon analysis. The vertical position
of the point in each vertical column of the depth map
pixels was identified and plotted on a 2D scatter plot
along its anterior-posterior position. The maximum vertical position was deemed arch height, and the anteriorposterior position of this was termed the arch peak. The
arch peak was calculated relative to the distance between
the calcaneus and head of the first metatarsal, and expressed as a percentage from the calcaneus. Therefore,
similar to the Vicon analysis, FPI item 5 was compared
to two measures derived from the Kinect, arch height
and arch peak.
Lastly, forefoot abduction/adduction (FPI item 6) used
both the lateral and medial position trials. The mediallateral displacement between the lateral calcaneus and
the head of the fifth metatarsal was compared with the
medial-lateral displacement between the medial calcaneus and the head of the first metatarsal. These points
were first identified manually on the video image and
the corresponding points on the depth image were
extracted for analysis. The medial displacement was
then subtracted from the lateral displacement. Refer
to Additional file 2 for further detail and images regarding the assessment and data analysis of foot posture using the Kinect system.
Statistical analysis
All data were imported to and analysed using the Statistical Package for Social Sciences (SPSS) version 19.0. Data
were initially assessed for normality using the ShapiroWilks test (p > 0.05), however the majority of data were
not normally distributed hence non-parametric statistics
were used.
Mentiplay et al. Journal of Foot and Ankle Research 2013, 6:14
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To examine test-retest reliability of the FPI and Kinect
data, Spearman’s rho was calculated for each FPI item
(visual observation, Vicon and Kinect analysis of FPI)
and a rank transformed intraclass correlation coefficient
(ICC) (2,1) was used for total FPI score (visual observation of FPI only), which is consistent with previous research [29]. To determine the concurrent validity of the
Kinect to measure foot posture, Spearman’s rho, the
non-parametric measure of association between two
variables, was used [30]. Data obtained from the Kinect
were compared to scores obtained from both the visual
observation of the FPI as well as those derived from the
Vicon system for each of the FPI items recorded. Additionally, the visual observation of the FPI was also
compared to FPI data obtained from the Vicon system.
Based on the thresholds provided by Portney and Watkins
[31], poor correlations were interpreted as a fair or belowfair relationship (< 0.50) and acceptable correlations were
deemed reliable and valid as a moderate to good (0.50 –
0.75) or good to excellent (> 0.75) relationship for all reliability and validity analyses.
To further examine the agreement between the Vicon
and Kinect systems for each FPI item, 95% limits of
agreement (LOA) and Bland-Altman plots were also
used if the mean difference between systems was normally distributed [32]. The LOA method uses the mean
difference between the measures and the standard deviation of the differences, and states that the plots should
fall between ±1.96SD of the mean difference [32]. The
Bland-Altman method plots the difference between the
FPI items recorded by the Vicon and Kinect systems
against the mean of the two measures. Visual inspection
for any fixed or proportional bias were performed on the
Bland-Altman plots. Finally, to determine if the Vicon
and Kinect systems were able to predict the total scores
obtained on the clinical FPI observation, a linear regression was used on rank transformed data. The reliable
items from the Vicon and Kinect systems obtained in
the first testing session were entered as the independent
variables with the total FPI score as the dependent variable. The total FPI score can be compared against the
continuous Vicon and Kinect data as the summation
of the FPI items to obtain a total FPI score results in
continuous data [29]. If a measure demonstrated significance (p < 0.05) in this regression, a Spearman’s rho
correlation was used to determine the strength of the association between the measure and total FPI score.
Results
Reliability
Descriptive statistics and intra-rater reliability for each
of the FPI items measured by the visual FPI, Kinect and
Vicon systems are presented in Table 1. Reliability of the
visual FPI items was mixed, with the ICC value for the
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total visual FPI score demonstrating good to excellent
intra-rater reliability (ICC = 0.87), however moderate to
good reliability was shown for lateral curvature and congruence of the medial longitudinal arch (ρ = 0.52 to
0.63), and poor reliability for talar head palpation, calcaneal inversion/eversion, talo-navicular joint bulging
and forefoot abduction/adduction (ρ = 0.17 to 0.42). The
Spearman’s rho for the Kinect indicates good to excellent intra-rater reliability for lateral curvature (ρ = 0.78),
with moderate to good reliability for talo-navicular joint
bulging, arch height and arch peak (ρ = 0.62 to 0.72) and
poor reliability for forefoot abduction/adduction and calcaneal inversion/eversion (ρ = 0.21 to 0.30). The intrarater reliability for Vicon items were similar, with good
to excellent reliability for talo-navicular joint bulging
and arch height (ρ = 0.78 to 0.79), with moderate to
good reliability for lateral curvature and forefoot abduction/adduction (ρ = 0.55 to 0.73) and poor reliability
for arch peak and calcaneal inversion/eversion (ρ = 0.13
to 0.37).
Validity
Concurrent validity between the visual FPI, Kinect and
Vicon systems are displayed in Table 2. Results of the
validity analysis indicate a good to excellent correlation
between the Kinect and Vicon items of lateral curvature
(ρ = 0.85). Moderate to good correlations were shown
for talo-navicular joint bulging, arch height and forefoot
abduction/adduction (ρ = 0.51 to 0.74) and poor correlations were found for arch peak and calcaneal inversion/
eversion (ρ = 0.30 to 0.34). The concurrent validity results demonstrated that all individual visual FPI items
correlated poorly with the corresponding items recorded
using either the Kinect or Vicon systems, with all of
the items indicating little to fair relationships (absolute
ρ = 0.01 to 0.44).
Bland-Altman plots were also used to indicate the
agreement between the Kinect and Vicon systems for
each FPI item (Figure 1). The LOA method was calculated for all items except for calcaneal inversion/eversion
(FPI item 3) due to the mean difference between the systems not being normally distributed for this item. Fixed
biases were evident between devices for the arch height
(Figure 1D) and arch peak items (Figure 1E), whereas no
obvious relationship between the difference and the
mean was observed for any item. These plots demonstrate poor absolute agreement between the two devices.
The Kinect items with moderate to excellent reliability
(ρ > 0.50) that were entered into the regression model
were lateral curvature, talo-navicular joint bulging, arch
height and arch peak. The regression analysis showed
that the reliable Kinect items explained 61% (r = 0.78,
R2 = 0.61, F = 10.08, p < 0.001) of the variance in the
total visual FPI score. However, the only item that
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Table 1 Intra-rater reliability of the visual FPI, Kinect and Vicon systems for each item of the FPI
FPI
Kinect
Vicon
N/A
N/A
FPI 1 Talar head
Day 1 median (IQR)
0 (0 to 1)
Day 2 median (IQR)
1 (0 to 1)
Spearman’s rho
0.38*
FPI 2 Lateral curvature
Day 1 median (IQR)
0 (0 to 1)
−1.55 (−3.40 to 0.75)
−1.32 (−5.06 to 0.93)
Day 2 median (IQR)
0 (0 to 1)
−0.97 (−4.29 to 0.74)
−2.83 (−4.06 to 0.51)
Spearman’s rho
0.52*
0.78*
0.73*
Day 1 median (IQR)
0 (0 to 1)
10.03 (7.54 to 11.71)
9.77 (6.82 to 12.98)
Day 2 median (IQR)
1 (0 to 1)
11.34 (9.32 to 14.26)
10.73 (7.57 to 12.18)
Spearman’s rho
0.42*
0.30
0.37
Day 1 median (IQR)
1 (0 to 1)
−17.00 (−22.00 to −13.00)
−12.93 (−15.86 to −8.52)
Day 2 median (IQR)
1 (0 to 1)
−16.50 (−19.25 to −13.00)
−11.63 (−16.22 to −8.71)
Spearman’s rho
0.17
0.62*
0.79*
Day 1 median (IQR)
1 (0 to 2)
49.35 (39.94 to 61.36)
26.75 (22.19 to 31.12)
Day 2 median (IQR)
1 (0 to 1)
46.28 (40.33 to 58.88)
28.82 (23.86 to 32.72)
Spearman’s rho
0.63*
0.72*
0.78*
17.65 (15.69 to 32.65)
42.67 (38.25 to 45.03)
24.20 (15.56 to 35.76)
42.41 (34.59 to 46.53)
0.66*
0.13
FPI 3 Calcaneal inv/ev
FPI 4 TNJ bulging
FPI 5 Congruence of MLA/Arch height
FPI 5 Arch peak
Day 1 median (IQR)
Day 2 median (IQR)
N/A
Spearman’s rho
FPI 6 Forefoot ab/add
Day 1 median (IQR)
1 (0 to 2)
−3.00 (−6.00 to 3.00)
2.87 (−0.71 to 7.88)
Day 2 median (IQR)
1 (0.75 to 1)
0.00 (−6.00 to 6.50)
4.19 (−1.15 to 8.75)
Spearman’s rho
0.29
0.21
0.55*
N/A
N/A
FPI total score
Day 1 median (IQR)
3 (2 to 6)
Day 2 median (IQR)
4 (2 to 5.25)
ICC
0.87*
Note: * p < 0.05; FPI = Foot Posture Index; IQR = interquartile range; TNJ = talo-navicular joint; MLA = medial longitudinal arch; ICC = Intraclass correlation coefficient.
Table 2 Concurrent validity between systems for each FPI item
FPI item
Instrumented item
Kinect-FPI ρ
Kinect-Vicon ρ
Vicon-FPI ρ
FPI 2 Lateral curve
Lateral curve
−0.03
0.85*
−0.01
FPI 3 Calcaneal inv/eversion
Calcaneal inv/eversion
0.36*
0.34
0.44*
FPI 4 TNJ bulging
TNJ bulging
−0.34
0.74*
−0.35
FPI 5 Congruence of MLA
Arch height
−0.01
0.51*
−0.20
FPI 6 Forefoot ab/adduction
Arch peak
−0.36
0.30
−0.44*
Forefoot ab/adduction
−0.14
0.57*
−0.19
Note: * p < 0.05; FPI = Foot Posture Index; ρ = Spearman’s rho; TNJ = talo-navicular joint; MLA = medial longitudinal arch.
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Figure 1 Bland-Altman plots demonstrating the agreement between the Kinect and Vicon systems for each FPI item. A: Lateral
curvature (FPI item 2). B: Calcaneal inversion/eversion (FPI item 3). C: Talo-navicular joint bulging (FPI item 4). D: Arch height (FPI item 5). E: Arch
peak (FPI item 5). F: Forefoot abduction/adduction (FPI item 6). Note: LOA were not calculated for B due to the mean difference not being
normally distributed.
independently demonstrated significance (p < 0.05) was
talo-navicular joint bulging. Similarly, the Vicon items with
moderate to excellent reliability (ρ > 0.50) (lateral curvature, talo-navicular joint bulging, arch height and forefoot
abduction/adduction) explained 58% (r = 0.76, R2 = 0.58,
F = 8.2, p < 0.001) of the variance in the total visual FPI
score, with only talo-navicular joint bulging again independently demonstrating significance (p < 0.05). Correlations between talo-navicular joint bulging, recorded
using the Kinect and Vicon systems, and the total visual
FPI score were shown to be good to excellent (ρ = 0.75,
p < 0.001; ρ = 0.74, p < 0.001 respectively).
Discussion
This study was the first to examine the reliability and
validity of the Microsoft Kinect to evaluate static foot
posture. The Kinect demonstrated moderate to good
reliability for four out of six items of the modified FPI
(lateral malleolar curvature, talo-navicular joint bulging
and medial arch height and peak). Additionally, the
Kinect also displayed moderate to good concurrent validity for four items of the FPI when compared to the
Vicon 3D analysis system (lateral malleolar curvature,
talo-navicular joint bulging, medial arch height and forefoot abduction/adduction). However the relationship between Kinect and visual FPI items was found to be poor.
Regression analysis revealed that the Kinect FPI items
with moderate to good reliability were able to predict
61% of the variance in the total visual FPI score, with
the only significant variable (talo-navicular joint bulging)
also demonstrating a good to excellent relationship with
the total visual FPI score. The advantage of the Kinect in
comparison with the individual items of the visual FPI is
that it provides quantified data on a continuous scale rather than on a limited ordinal scale. Although somewhat
mixed, these results support the future potential use of
the Kinect as a tool to assess static foot posture in a clinical setting.
Mentiplay et al. Journal of Foot and Ankle Research 2013, 6:14
http://www.jfootankleres.com/content/6/1/14
The Kinect showed improved intra-rater reliability for
individual items of the FPI when compared with the visual FPI observations. Specifically, individual FPI items
recorded by the Kinect showed moderate to good reliability whereas visual FPI items demonstrated poor to
moderate intra-rater reliability, which is consistent with
previous research [29]. The total visual FPI score demonstrated good to excellent intra-rater reliability, which
has also been reported previously [1,17,18]. The improved item reliability found with the Kinect could be
attributed to the continuous data of the Kinect which,
compared with the limited ordinal scale of the FPI, potentially provides improved accuracy in the evaluation of
foot posture.
Similar to the finding of improved reliability of FPI
items recorded using the Kinect, Vicon FPI items also
demonstrated superior reliability when compared to the
visual assessment of FPI. However, there was some variation in reliability levels between the systems. For instance, arch peak, which is part of the congruence of the
medial longitudinal arch item of the FPI, demonstrated
moderate to good intra-rater reliability for the Kinect
system whilst poor reliability was shown for the Vicon
system. In contrast, forefoot abduction/adduction demonstrated moderate to good reliability for the Vicon
system whereas poor reliability was shown for the
Kinect system. Furthermore, the item calcaneal inversion/
eversion demonstrated poor intra-rater reliability for both
systems.
The variable reliability results for the Kinect and Vicon
systems may be partly due to the different data collection methods employed, with the Kinect system using
depth data and the Vicon system using a wand to locate
landmarks and trace over regions of the foot in 3D
space. For instance, the precision along the longitudinal
axis of the foot of the medial mesh techniques used in
the Vicon analysis of the FPI, which involved performing
sweeping movements over the medial surface of the foot
using the wand tip, was poor relative to the depth data
from the Kinect for measurement of the arch peak along
the longitudinal axis of the foot. The distance between
each sweep may have been too large (approximately
10 mm at the top of the arch), whilst the Kinect was able
to measure the 3D position of the arch in each pixel with a longitudinal axis precision of approximately
3 mm. Furthermore, the wand tip may depress the soft
tissue of the foot as the sweeps were performed along
the medial arch. This may limit the ability to identify the
position of the arch peak due to the varying compressions in the medial-lateral plane of the soft tissue, which
would not affect the measure of arch height in the vertical plane. Instrumenting the wand and controlling force
application during the assessment may have reduced
this error, however during our pilot testing this proved
Page 8 of 10
difficult to control via feedback given that the forces applied through the wand tip were quite low (< 5 Newtons).
Additionally, the Kinect demonstrated poor reliability
for the FPI item of forefoot abduction/adduction, which
may be attributed to errors in visual anatomical landmark identification. The reliability for this item may be
improved by identifying the furthest point of the rearfoot
to the Kinect compared to the closest point of the forefoot to the Kinect. Finally, calcaneal inversion/eversion
also demonstrated poor reliability for both the Kinect
and Vicon systems. As discussed for the Vicon assessment of arch peak, the precision of the mesh technique
for evaluation of calcaneal inversion/eversion may have
been affected by the distance between each sweep and
the varying compression of the soft tissue. Similarly, the
poor reliability found for the Kinect assessment of calcaneal inversion/eversion may have been due to errors in
visual estimation of calcaneal position, as suggested previously for forefoot abduction/adduction. Indeed, difficulties in visually estimating rearfoot position have been
highlighted previously [33], and given the present study
visually assessed a depth image rather than the actual
foot, it is likely that this technique may have led to further errors in the evaluation of this item.
Validity analysis revealed that all individual items of
the FPI derived from visual observations were poorly
correlated with items from both the Kinect and 3D motion analysis system. This inability to correlate the visual
FPI with other measures of foot posture is consistent
with Scharfbillig et al. [34], who reported that four items
of the FPI were poorly correlated with radiographic measures, which they partly attributed to a lack of agreement between bony architecture and the overlying skin.
Given each item of the visual FPI has only five possible
scores, this limited spread of data will reduce the likelihood of finding strong relationships when compared
with a continuous set of data such as depth or radiological-based measures. Further, the reduced reliability
of the individual visual FPI items when compared to
both the Kinect and Vicon FPI items may limit the appropriateness of the visual inspection of foot posture as
a concurrent validity measure. In contrast, mostly moderate to good correlations were found between the
Kinect and Vicon, which is likely due to the two analysis
systems using continuous data and having the same outcome measures. Bland-Altman plots revealed poor absolute agreement between the devices, although this may
be explained by the different scales used by the Kinect
and Vicon.
The Kinect items with moderate to excellent reliability
were shown to predict 61% of the variance in the total
visual FPI score. Similarly, the Vicon items with moderate to excellent reliability predicted 58% of the variance
in the total FPI score derived from visual observations.
Mentiplay et al. Journal of Foot and Ankle Research 2013, 6:14
http://www.jfootankleres.com/content/6/1/14
In both cases, talo-navicular joint bulging was the only
item entered into the regression model to independently
demonstrate significance (p < 0.05). Although no previous study has investigated the ability of objective 3D
measures of foot posture or an individual analysis of
each of the FPI items to predict total FPI score, Redmond et al. [16] reported that total FPI score was able to
predict 64% of the variance in a measure of ankle joint
position in 3D space. The validation of the Kinect in
explaining a high proportion of the variance in total visual FPI scores suggests the potential feasibility of the
Kinect and custom analysis software to be further refined to classify overall foot posture. Indeed, given the
greater reliability of the individual items of the FPI derived from the Kinect, and the greater similarity in
Kinect FPI items to FPI items from the 3D analysis system, future studies may attempt to develop a total
Kinect FPI-type score that could be used to classify foot
posture with greater accuracy and reliability compared
to the FPI derived from visual observations.
Interestingly, one item of the FPI, talo-navicular joint
bulging, demonstrated good to excellent correlations with
the total visual FPI score when measured by the Kinect.
This may indicate the potential of this particular item of
the FPI derived from the Kinect to be used as a standalone measure to classify foot posture. Similarly, other research has reported that clinical measures of the midfoot
strongly correlated with radiographic measures of foot
posture [35]. Future research should further examine the
agreement between the measure of talo-navicular joint
bulging derived from the Kinect and total FPI score.
A limitation of the current study is the lack of a total
score for the Kinect FPI items. Given the different scales
used within each FPI item for the Kinect, this made the
generation of a total FPI-type score and foot posture
classification problematic. Future research may wish to
first implement techniques, such as multiple regressions,
to derive a total FPI-type score and foot classification
from the Kinect items and secondly to collect a comprehensive data set of the full range of foot posture using
the Kinect, from highly supinated to highly pronated.
Additionally, future research should examine the interrater reliability of the Kinect to evaluate foot posture
given previous research has shown poor inter-rater reliability of the FPI based on visual observation [1,18]. If
inter-rater reliability was found to be superior, this may
further the potential use of the Kinect as a tool to assess
static foot posture in a clinical setting. Although mostly
moderate to excellent correlations were found between
the Kinect and the 3D motion analysis system, another
potential limitation may have been the use of the Vicon
system as a benchmark reference. This is supported by
the poor test re-test reliability of the Vicon data in evaluating two FPI items, and may be partially explained by
Page 9 of 10
the limited accuracy from the distance between sweeps
along the longitudinal axis of the foot and from soft tissue deformation as explained previously. In contrast,
previous research has shown that a 3D foot scanner is
able to reliably and accurately provide a 3D digital representation of the foot [36,37]. Although such a tool may
provide a more appropriate benchmark reference with
which to compare the Kinect, the Vicon system was used
in this study due to the need of further research to develop standardised 3D foot scanning protocols for evaluating foot posture and morphology, and the limited
availability of such systems. Future studies may wish to
examine the concurrent validity of the Kinect to evaluate
static foot posture when compared to 3D foot scanners.
Furthermore, the generalizability of the study may be
comprised due to the male only participants and the use
of a novice rater for assessment. To attain generalised
results, future research is required using many raters
with larger participant numbers.
Conclusions
This study found that four foot posture items derived
using the Microsoft Kinect demonstrated good intrarater reliability and four items were valid when compared to a 3D analysis system. In contrast, poor reliability and validity was shown for the visual inspection of
individual FPI items. Items of foot posture recorded
using the Kinect were also shown to predict a moderate
degree of variance in the total FPI score derived from
visual observations. Future research should consider developing a total FPI-type score and foot posture classification using the Kinect FPI items. Combined, these
results support the future potential of the Kinect to accurately evaluate static foot posture in a clinical setting.
Additional files
Additional file 1: Assessment of the Foot Posture Index using the
Vicon analysis system. Procedure and data analysis for the assessment
of foot posture using the Vicon motion analysis system.
Additional file 2: Assessment of the Foot Posture Index using the
Microsoft Kinect™. Procedure and data analysis for the assessment of
foot posture using the Kinect.
Competing interests
This study was funded in part by ASICS Oceania. All authors have received
funding from ASICS Oceania either directly or indirectly via research grants
or employment. Author RC designed the software and may at some stage
release it either for free or at a cost.
Authors’ contributions
Authors BM, KP and RC were involved in all aspects of the study. Author AM
was involved in the pilot testing, data collection and drafting stage of the
study. Authors SB and AB were involved in the preliminary design and
drafting stages of the study. All authors read and approved the final
manuscript.
Mentiplay et al. Journal of Foot and Ankle Research 2013, 6:14
http://www.jfootankleres.com/content/6/1/14
Author details
1
School of Exercise Science, Faculty of Health Sciences, Australian Catholic
University, Melbourne, Australia. 2Department of Physiotherapy, Faculty of
Medicine, Dentistry and Health Sciences, The University of Melbourne,
Melbourne, Australia.
Received: 8 November 2012 Accepted: 29 March 2013
Published: 8 April 2013
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doi:10.1186/1757-1146-6-14
Cite this article as: Mentiplay et al.: Reliability and validity of the
Microsoft Kinect for evaluating static foot posture. Journal of Foot and
Ankle Research 2013 6:14.
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