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Original Article 

Accelerometer and Gyroscope Based Gait Analysis 
Using Spectral Analysis of Patients with 
Osteoarthritis of the Knee 



J. Phys. Ther. Sci. 
26: 997-1002, 2014 



WiELAND StAAb'' ^' RaLF HoTTOWITZ^', CHRISTIAN SOHNS, MD^ *\ JaN MaRTIN SoHNS, MD'' ^\ 

Fabian Gilbert, MD''\ Jan Menke, MD'\ Andree Niklas, MD^\ Joachim Lotz, MD'-^^ 

Institute for Diagnostic and Interventional Radiology, Heart Center, University Medical Center 
Goettingen: Robert-Koch-Str. 40, 37099 Goettingen, Germany 
DZHK, German Center for Heart Research, Germany 
^) Department of Cardiology and Pneumology, Heart Center, University Medical Center Goettingen, 
Germany 

''^ Division of Imaging Sciences and Biomedical Engineering, King's College London, United Kingdom 
^' Department of Sports Medicine, University Medical Center Goettingen, Germany 
^) Department of Orthopedics, University Medical Center Wilrzburg, Germany 



Abstract. [Purpose] A wide variety of accelerometer tools are used to estimate human movement, but there are 

no adequate data relating to gait symmetry parameters in the context of knee osteoarthritis. This study's purpose 
was to evaluate a 3D-kinematic system using body-mounted sensors (gyroscopes and acceleromcters) on the trunk 
and limbs. This is the first study to use spectral analysis for data post processing. [Subjects] Twelve patients with 
unilateral knee osteoarthritis (OA) (10 male) and seven age-matched controls (6 male) were studied. [Methods] Mea- 
surements with 3-D acceleromcters and gyroscopes were compared to video analysis with marker positions tracked 
by a six-camera optoelectronic system (VICON 460, Oxford Metrics). Data were recorded using the 3D-kinematic 
system. [Results] The results of both gait analysis systems were significantly correlated. Five parameters were sig- 
nificantly different between the knee OA and control groups. To overcome time spent in expensive post-processing 
routines, spectral analysis was performed for fast differentiation between normal gait and pathological gait signals 
using the 3D-kinematic system. [Conclusions] The 3D-kinematic system is objective, inexpensive, accurate and 
portable, and allows long-term recordings in clinical, sport as well as crgonomic or functional capacity evaluation 
(FCE) settings. For fast post-processing, spectral analysis of the recorded data is recommended. 
Key words: Gait, Accelerometer, Gyroscope 

(This article was submitted Nov. 29, 2013, and was accepted Jan. 8, 2014) 



INTRODUCTION 

Osteoarthritis (OA), the most common form of arthritis, 
is also the most common type of musculoskeletal disorder. 
While OA can affect virtually all joints, knee OA is of par- 
ticular interest since it has the potential to severely limit 
mobility'"*'. Severity of knee OA can be determined radio- 
graphically using the Kellgren-Lawrence criteria^'. Current 
treatment strategies fail to prevent progression of knee OA, 
and therefore the current therapeutic goal is to limit pain 
and disability^'. For assessing functional disability in OA an 
objective measurement method is desired. Accelerometry is 
a practical, cost-effective and repeatable method of reliably 
evaluating human movement of individual body parts or the 



*Corresponding author. Wieland Staab (E-mail: wieland. 
staab@med.uni-goettingen.de) 

©2014 The Society of Physical Therapy Science. Published by IPEC Inc. 
This is an open-access article distributed under the terms of the Cre- 
ative Commons Attribution Non-Commercial No Derivatives (by-nc- 
nd) License <http://creativecommons.Org/licenses/by-nc-nd/3.0/>. 



whole body without any radiation exposure^"''. Accelerom- 
cters can be used for observing a range of different move- 
ments such as postural sway and falls, sit-to-stand transfer 
and gait''. Equal values of gait variables on both sides of 
the body are the definition of gait symmetry'"' In this 
context, pathological gait patterns are reflected by a certain 
amount of gait asymmetry. However, there is no general 
agreement about when gait asymmetry should be consid- 
ered pathological, although it is frequently assessed and de- 
scribed in the pre- and postoperative clinical evaluation of 
patients, as well as in general rehabilitation. For example, 
Vogt et al."' pointed out that gait asymmetry is often pres- 
ent in patients with hip OA. An arbitrary cut-off value of 
10% difference from perfect symmetry has been reported 
as a criterion of asymmetry in gait'^- '^'. This 10% criterion, 
however, has been considered invalid because of its non- 
parameter-specific nature'^'. Visual gait analysis is used in 
the context of functional capacity evaluation (FCE). It is 
known as a clinical tool used internationally for the purpose 
of predicting a safe return to work. FCEs are standardized 
batteries of tests in which a participant's functional ability 



998 J. Phys. Ther. Sci. Vol. 26, No. 7, 2014 



is tested, analyzed and compared to the required physical 
job demands'^' For quantitative analyses of gait sym- 
metry or asymmetry, statistical differences between groups 
or limbs are used"'. However, gait symmetry or asymmetry 
should be evaluated in footfall measures and the movement 
of the upper body. Accelerometer-based gait analyses of 
knee OA has to be established as a valid criterion of gait 
asymmetry that is easily measured and interpreted, making 
it possible to use in the rehabilitation of individual patients 
in clinical practice as well as in research. A wide variety 
of accelerometer tools are used to estimate human move- 
ment, but there are no adequate data relating to gait sym- 
metry parameters in the context of knee OA. The aim of 
our study was to assess and describe 3D-accelerometer/3D- 
gyroscope based parameters that are able to differentiate 
between pathological and normal gait patterns as well as 
distinguish gait symmetry from gait asymmetry. 

SUBJECTS AND METHODS 

Twelve patients (2 female, 10 male) with knee OA diag- 
nosed by a physician, confirmed radiographically'^- '^', and 
graded with the Kellgren-Lawrence scale (1 to 4; Grade III 
(7 patients) and Grade IV (5 patients)) participated in this 
study. Patients were excluded if they had neurological, ves- 
tibular or musculoskeletal disorders, fracture of the lower 
extremity, rheumatoid arthritis, generalized osteoarthritis, 
limping gait or any condition that may have influenced a 
treadmill walking evaluation. The characteristics of the 
included patients were (mean ± SD): age, 44.4 ± 7.6 years; 
body weight, 75.5 ± 11.1 kg; height, 171.1 ± 6.5 cm; and body 
mass index, (BMI): 26.9 ± 3.2 kg/m^. A control group of 7 
healthy persons was also tested (1 female, 6 male). Their 
characteristics were (mean ± SD): age, 41.7 ± 8.8 years; 
body weight, 73.7 ± 9.8 kg; height, 175.6 ± 7.2 cm; and body 
mass index, 26.1 ± 2.9 kg/m^. This study was approved by 
the Ethics Committee of the University Medical Center of 
Goettingen. Patients and controls gave their written con- 
sent to participation in this study. The method of this study 
conformed to the principles of the Declaration of Helsinki. 

First, kinematics data were obtained using a six-camera 
optoelectronic system (VICON 460, Oxford Metrics). Us- 
ing Kistler force plates (Type 9287A, Kistler Instrumente 
AG, Switzerland), kinetic data was obtained. When the 
magnitude of vertical ground reaction forces exceeded 2% 
of a subject's body weight, gait cycles were normalized (0- 
100%) between two successive foot contacts. Second, ac- 
celerometers and gyroscopes were attached to the subjects 
for comparive measurements. They measure the tendency 
of an object to resist a change in motion. Accelerometers 
operate on a "spring-mass" principle. The motion sensors 
(Freescale semiconductor. Type MMA76260Q) were three 
tri-axial accelerometers and three gyroscopes (ADXRS 
300, Analog Devices) connected to a portable data logger 
(Glonner Electronic Noraxon OY). Body acceleration and 
angular velocity were recorded three-dimensionally (3D) 
in the anteroposterior, vertical and mediolateral directions. 
The accelerometers are piezo-electric sensors that were 
coupled to amplifiers (± 2 g). Angular velocity is measured 



by a rate gyroscope through the Coriolis force and can be 
measured directly'^' The data logger was attached to 
the back or side of the body. Data were sampled at 1 kHz. 
Each study participant was advised to wear light clothing 
to allow comfortable movement. The measurement sensors, 
the accelerometer and gyroscope, were placed inside a con- 
tainer that was firmly attached to the individual by means 
of a flexible belt around the waist over the middle part of 
the lower back over the L3 process so that it was attached 
close to the centre of gravity of the human body. Partici- 
pants had to feel free to move, with respect to the skin. Ac- 
cording to the marker positioning of the optoelectronic sys- 
tem, the other two sensors were fixed to rigid bodies which 
were attached by belts to the lateral malleoli of both legs, as 
described in previously published studies'*' '■'I The 31 re- 
flective markers of the optoelectronic system were attached 
to anatomical locations according to the VICON Plug-in- 
Gait marker placement protocol. Additionally, six reflective 
markers were attached to the rigid plates of the combined 
accelerometers and gyroscopes for correlation and evaluat- 
ing accuracy with the gold standard video analysis. After 
calibration of the systems, all participants walked 500 m on 
a treadmill (HP Cosmos, Germany) at self-selected speed 
while acceleration and angular velocity were measured us- 
ing the setup described above. After the experiment, all ob- 
tained data (500 m walking distance) were downloaded to 
a computer and converted into earth acceleration units (g) 
following a standard calculation. Subsequently, data pro- 
cessing was conducted with Noraxon Software (Noraxon 
MyoReasearch 2.02, Noraxon, Germany). Data acquisition 
and post-processing for the optoelectronic system was done 
on a Vicon-PC using Vicon software (Vicon Body-Builder 
3.5). The generated acceleration data was post-processed 
using a lowpass filter, fast Fourier transformation (FFT), 
and spectral analysis to analyze differences in trunk and 
locomotor limb acceleration. We used gravity as a known 
constant acceleration for calibrating the accelerometers. 
The output of a stationary accelerometer's sensing axis 
aligned with the global vertical must correlate to 9.81 m/ 
s^. In this context, a two-point linear calibration can be 
performed that transforms the raw output to units of accel- 
eration once a raw accelerometer output has been obtained 
for the static condition of 9.81 m/s^. Calibration procedures 
were similar to the two-point method with a slope-intercept 
and zero-span, assuming linearity between the raw output 
and acceleration. For the gyroscopes, dynamic calibrations 
were performed by sensor rotations of 90° and 180° within 
a known time. Drift correction and calibration was done 
according to the literature'^' ^"l For further details on 
calibration, see Veltink et al^^'. According to previous stud- 
ies''- '■'' '"'', symmetry parameters were calculated for foot- 
fall and trunk measurements. The angular velocity (co) was 
recorded. By integrating the angular velocity over time, the 
angle can be determined [a = J m x 5t]. Accelerometers pri- 
marily measure accelerations (a), but integration over time 
allows determination of velocities [ v = J a x 5t (m/s)] and 
paths [s = JJflx5T = Jvx5T (m)]. Following these prin- 
ciples, we measured accelerations and angles as described 
above. Spatiotemporal walking speed (distance per walking 



999 



Time (sec) 



Fig. 1. The comparison of 3D-accelerometry (blue lines) with 
video analysis (Vicon®, red lines) shows a significant cor- 
relation over time. 



time) and cadence (number of steps per walking time) were 
determined. The step time asymmetry was calculated to de- 
termine differences between left and right leg movements. 
Using FFT, front acceleration data as well as mediolateral 
angular velocities were evaluated as a power spectrum on a 
spectral frequency scale (1/sec). It is a known fact that many 
gait variables change with walking speed''''. To differentiate 
between gait abnormality and the effect of different walk- 
ing speeds, variables need to be controlled for the effect 
of speed. For this reason, every participant was asked to 
walk back and forth at three steady-state speeds ranging 
from slow to fast (a total of six walks), allowing individual 
linear trend lines to be calculated over the speed range dem- 
onstrated by the individual participants'*'. This data nor- 
malization allowed inter-individual comparisons among the 
study participants, even though the actual walking speeds 
were self chosen. Quantitative variables were expressed as 
the mean ± standard deviation. The patient group (n = 12) 
and the control group (n = 7) were compared using unpaired 
t-test with a significance level of p = 0.05. Statistical analy- 
ses were performed using commercially available software 
(SPSS 17 for Windows, SPSS Inc., Chicago, IL, USA). The 
fast Fourier transform function within the analysis package 
of MATLAB (Matlab 7.0, Mathworks, Inc., Concord, MA) 
was used in the evaluation of power spectra. To detect lin- 
ear correlations between the recorded parameters, a linear 
regression model was utilized. 

RESULTS 

Figure 1 shows the comparison ofthe velocities measured 
by accelerometers in the lumbar region (L3) with those de- 
rived from video analysis measured by the Vicon® System. 
Figure 2 shows the comparison of mediolateral lumbar (L3) 
angles measured by gyroscopes with those derived from 
measurements with the Vicon® video analysis system. The 
root mean square difference was used to evaluate the close- 
ness of the results of video analysis (Vicon®) with those of 
the accelerometer/gyroscope-based analysis. The results of 
the Vicon® system were used as the gold standard. Linear 
regression analysis revealed a significant correlation be- 
tween video analysis results and the 3D-gyroscopes and 




Time (sec) 



Fig. 2. The comparison of 3D-gyroscopes (blue lines) with video 
analysis (Vicon®, red lines) shows a significant correla- 
tion. 



accelerometers analysis results (r^=0.69; p=0.01; Fig. 1 and 

Fig. 2). As shown in Figs. 1 and 2, errors increased at the 
highest walking speed for the accelerometer data and peaks 
of angular velocities measured by gyroscopes, probably due 
to the sensors being jarred or vibrated by heel strike. The 
patients in the knee OA group were older, heavier and short- 
er than the subjects in the control group, but no significant 
differences in the significance level (p values > 0.05) were 
found between the two groups. Preferred gait velocity was 
significantly lower in the knee OA group (0.74 ± 0.08 meters 
per second) than in the control group (1.27 ± 0.14 meters 
per second) (p < 0.001). Controlling for differences in age, 
the patients' walking speeds were significantly reduced at 
higher ages (p < 0.01). The other parameters showed no age- 
related differences. Cadence was significantly (p < 0.001) 
reduced in the knee OA group (97.8 ± 11.5 steps per min- 
ute) compared to the control group (116.3 ± 7.4 steps per 
minute). For trunk symmetry, the mediolateral (ML) angles 
of the lumbar region (L3) determined by gyroscopes were 
significantly (p < 0.001) higher in the knee OA group (9.4 
± 2.2°) than in the control group (4.3 ± 1.9°). For locomo- 
tor limb symmetry of the footfall (FF) measures, angular 
differences between both shanks were significantly (p < 
0.001) higher in the knee OA group (11.7 ± 1.6°) than in the 
control group (3.1 ± 2.3°). Despite this, FF time differences 
between the limbs were significantly (p < 0.001) higher 
in the knee OA group (5.7 ± 2.8 msec) than in the control 
group (1.2 ± 0.9 msec). Gait asymmetries were significantly 
higher (p < 0.001) in the knee OA group than in the con- 
trols. Using the 10% criterion, values were still significant 
(p < 0.01). The knee OA patients walked with lower cadence 
(p < 0.001), showed more asymmetric trunk acceleration 
(ML) (p < 0.001) and the stance phase of the affected limb 
was longer than that of the unaffected limb (p < 0.001), as 
shown by the FF time differences between both legs. The 
FFT is a method of calculating the component frequencies 
of a signal. The summation of an infinite number of sine 
and cosine waves of different frequencies and amplitudes 
created from the movement of different anatomical struc- 
tures can be thought of as a frequency signal resulting from 
ground reaction forces. FFT calculates the amount of move- 
ment (amplitude) at each frequency. The collection of all 



1000 J. Phys. Then Sci. Vol. 26, No. 7, 2014 



8. 

a 10 



Fig. 3. 



Spectral frequency (1/sec) 

Spectral visualization of normal gait (control group, a 
46-year-old male). The blue lines show 3D-accelerometer 
measurements in the anteroposterior direction, and the 
red lines show 3D-gyroscope measurements in the medio- 
lateral direction. The plot shows two peaks, one each for 
gyroscope and accelerometry. 



IS ,. 




».i «.* •.( t.) 



1.1 



Spectral fluency (1/sec) 



Fig. 4. 



Spectral visualization of abnormal gait (knee OA group, a 
50 year-old male). The blue lines show 3D-accelerometer 
measurements in the anteroposterior direction, and the 
pink lines show 3D-gyroscope measurements in the me- 
diolateral direction. There are three peaks, two measured 
by 3D-accelerometry, and one measured with a 3D-gyro- 
scope, indicating abnormal values due to an asymmetric 
gait pattern. 



the component frequencies is here termed the spectrum'^l 
Figure 3 shows the spectral visualization of normal human 
gait derived from measurements by 3D-accelerometry and 
gyroscopes. It shows two peaks, whereas the trace of the 
knee OA group. Fig. 4, demonstrates an additional peak, 
which was representative of asymmetric gait. 

DISCUSSION 

The aim of this study was to explore 3D-accelerometer 
and 3D-gyroscope parameters that have the potential to dif- 
ferentiate between normal gait and pathological gait. Spec- 
tral analysis provides a simple evaluation method, which is 
able to differentiate between normal and pathological gait. 
This sensor-based method showed good measurement reli- 
ability of the five parameters that exhibited significant dif- 
ferences between the knee OA and control groups. 

According to previous studies'*' body-mounted ac- 
celerometers and gyroscopes give results in kinematics 
analyses that are comparable to those of Vicon ® video 
analyses. As shown in Figs. 1 and 2, errors increased at 
the highest speed for the accelerometer data or peaks of 
angular velocities measured by gyroscopes, probably due 
to the sensors being jarred or vibrated by heel strike. This 
should be considered when designing future applications 
for accelerometers and gyroscopes. However, using body- 
mounted accelerometers and rate gyroscopes is as accurate 
as the gold standard video analysis'^^ as a physical method 
for collecting kinematic data of healthy subjects as well 
as patients with knee OA. In addition, accelerometers and 
gyroscopes used as body-mounted sensors are inexpensive 
and, combined with a portable data-logger, fulfill all crite- 
ria for a fully portable system that can be used in almost 
any environment. Comparable systems have been used in 
prior biomechanical studies'*' '^' "' ^''. Our body-mounted 
system could also be used in sports situations with accelera- 
tions similar to those of walking because of its portability. 
Furthermore, this system could be attached to participants 
undergoing FCE observations to obtain quantitative data. 



Huisinga et al.^^' reported that acceleration of the trunk dur- 
ing walking by patients with multiple sclerosis (MS) had 
larger frequency dispersion in the ML direction, than in the 
AP direction. This agrees with our result that ML differ- 
ences from normal gait are indicative of a pathological gait 
pattern. Unsteady walking speeds can lead to an increase 
in trunk acceleration. In the present study, however, par- 
ticipants walked at a constant speed during the testing. Ac- 
cording to the study by Turcot et al.'^\ high accelerations in 
the ML direction may be a consequence of joint instability 
due to knee OA and an unstable limb alignment. Ogata et 
al.^^' reported lateral acceleration generated by initial foot 
contact was caused by varus deformity in medial knee OA 
patients. Therefore, ML differences in patients with unilat- 
eral knee OA measured with 3D gyroscopes and represent 
compensatory posture, as observed in the present (Table 
1). Linear accelerations observed in this study were esti- 
mated at the same functional location for each participant 
rather than an arbitrary or changing location on the seg- 
ment as in previous studies"' '^' Estimation of linear ac- 
celerations is less affected by angular components induced 
by the movement of segments during gait when transposing 
accelerations close to joint contact surfaces'^l Estimation 
of internal acceleration may be affected by the vector join- 
ing sensor to bone. To verify the sensitivity of the method, 
we compared the data of the sensor-based, body-mounted 
system with Vicon® video analysis. The results show a dif- 
ference in 3D-acceleration and gyroscope measurements 
at peak magnitude and angles less than 5° (Figs. 1 and 2). 
These results are in line with the results of Mayagoitia et 
al'*'. In the present study, between-limb differences in FF 
measures were significantly higher in the knee OA group 
than in the controls. Furthermore, our results showed a cer- 
tain amount of gait asymmetry was also present in the con- 
trols (Table 1), due to the fact that values of either the 3D 
accelerometer or 3D gyroscope measurements would tend 
to zero representing perfect symmetry'*- '^' Sadeghi et 
al.'^^^ showed that a certain amount of gait asymmetry is 
also present in healthy subjects and may reflect functional 



1001 



Table 1. Comparison of gait parameters between tlie study groups 



Parameter 


Knee OA group 
(12 patients) 


Control group 
(7 participants) 


Gait velocity (m/s)* 


0.74 ± 0.08 


1.27 ±0.14 


Cadence (steps/min)* 


97.8 ± 11.5 


116.3 ±7.4 


ML trunk symmetry (angular degree)* 


9.4° ±2.2° 


4.3° ± 1.9° 


Limb symmetry (angular degree)* 


11.7° ± 1.6° 


3.1° ±2.3° 


Limb symmetry (time, msec)* 


5.7 ±2.8 


1.2 ±0.9 



* p<0.001 



between the limbs differences. For differentiating subjects 
with pathological gait asymmetry from subjects without 
pathological gait asymmetry, the reported cutoff criteria of 
gait asymmetry are too strict. In our present study, a gen- 
eral 10% criterion was used to assess differences between 
groups gait asymmetry. This is preferred because it is easily 
derived in clinical and research settings'^' The lack of 
standardization of the placement location of accelerometers 
can explain the variability of the results reported in previ- 
ous studies'*' ^^K Benoit et al.^'"' reported that skin 
movement artifacts originate from the skin moving over the 
musculoskeletal structure. For this reason, the movement of 
a subject's skeleton may not exactly correlate with the mea- 
sured movement on the skin where the sensors are attached. 
To address this, a non-invasive technique is needed. Apply- 
ing an algorithm, as used in the present study, to the mea- 
sured movement data during post-processing, can remove 
most of the skin movement'^"^^'. Sensors can be applied in 
different ways to the participants using medical tape, wrap- 
ping bandages, Velcro, plastic plates, elastic straps and bone 
pins^**'. In hne with previous studies, we used double-sided 
medical tape to attach the sensors to plastic plates and then 
fixed them to the subjects with tape'^' The relaxation 
and contraction of the muscles underneath the straps can 
cause their positions to change during locomotion when 
sensors are fixed to elastic straps or Velcro^''. Previous 
studies^' have used accelerometers to measure the ac- 
celeration experienced at impact at sampling rates of 10 
kHz. These high sampling rates are not required when the 
measurement of the impact transmitted by a subject's limb 
is done proximal to the point of contact. In line with the 
study by Henriksen et al.^*^' we used a sample rate of 1 kHz, 
significantly less than 10 kHz. For data post-processing of 
longer distance walks as used in the present study, of about 
400 m and more, a considerable amount of data is collected. 
In this context, acceleration data can be post-processed us- 
ing a low pass filter, FFT, and spectral analysis to observe 
differences in trunk acceleration to distinguish normal 
from pathological gait (Figs. 3 and 4). Our study data sup- 
port the hypothesis that differences in FF measurements 
occur due to unilateral knee OA, and the consequent adop- 
tion of a compensatory posture. The 3D-kinematic system 
is objective, inexpensive, accurate, and portable, and allows 
long-term recordings in clinical, sport, ergonomic and FCE 
settings. For fast post-processing, spectral analysis of the 
obtained data is recommended. 



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