Knee Implant Loosening Detection: A Vibration Analysis Investigation
Knee Implant Loosening Detection: A Vibration Analysis Investigation
ARASH ARAMI 0 2
JEAN-ROMAIN DELALOYE 1
HOSSEIN ROUHANI 2 4
BRIGITTE M. JOLLES 1 3
KAMIAR AMINIAN 2
0 Human Robotics Group, Department of Bioengineering, Imperial College London , London , UK
1 Centre Hospitalier Universitaire Vaudois (CHUV) , Lausanne , Switzerland
2 Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fe ́ de ́ rale de Lausanne (EPFL) , Lausanne , Switzerland
3 University of Lausanne (UNIL) , Lausanne , Switzerland
4 Department of Mechanical Engineering, University of Alberta , Edmonton , Canada
-Knee implant loosening is mainly caused by the weakness of the prosthesis-bone interface and is the main reason for surgical revisions. However, pre-operative diagnosis is difficult due to lack of accurate tests. In this study, we developed a vibration-based system to detect the loosening of the tibial implant of an instrumented knee prosthesis. The proposed system includes an instrumented vibrator for transcutaneous stimulation of the bone in a repeatable manner, and accelerometer sensors integrated into the implants to measure the propagated vibration. A coherence-based detection technique was proposed to distinguish the loosened implants from the secure ones. Fourteen ex vivo lower limbs were used, on which the knee prosthesis was implanted, and harmonic-forced vibration was applied on the tibia. The input-output coherence measure provided 92.26% accuracy, a high sensitivity (91.67%) and specificity (92.86%). This technique was benchmarked against power spectrum based analysis of the propagated vibration to the implant. In particular, loosening detection based on new peak appearance, peak shift, and peak flattening in power spectra showed inferior performance to the proposed coherence-based technique. As such, application of vibration on our instrumented knee prosthesis together with input-output coherence analysis enabled us to distinguish the secure from loose implants.
Knee prosthesis; Implant loosening; Vibration
Osteoarthritis is the most common joint disease, the
primary cause of joint pain and disability in the elderly
population. The knee is the main articulation involved
and the risk of developing symptomatic knee
osteoarthritis during a person’s lifetime has been
estimated over 40%.28 Total knee arthroplasty (TKA) is a
successful and widely performed procedure for
endstage osteoarthritis. TKA operations, which have been
dramatically increased, recently outnumbered the
performed hip replacements.8 This increment precipitated
an increasing need for TKA revisions, especially in
younger and active patients.8 TKA revisions could
increase by six-fold from 2005 to 2030.21 Aseptic
implant loosening remains the main cause for this
procedure associated with more than 30% of all TKA
revisions, and tibial implant being mostly involved.8
Nonetheless, diagnosis of aseptic TKA loosening is
still a challenge.
X-ray remains an informative, quick, and
inexpensive method to diagnose implant loosening. However,
the described Knee Society’s criteria for TKA
loosening13 depend on the exam quality,7,29 and showed a
poor accuracy suggesting that X-ray cannot reliably
detect early prosthesis debonding.38,43 Recently,
techniques such as radionuclide arthrography,20
18-fluorodeoxyglucose positron emission tomography,12,43
single photon emission computerized tomography
associated with CT-scan, or bone scintigraphy to
detect implant loosening have been studied.1,9,42
However, their TKA loosening detection performances are
variable. Bone scintigraphy is widely used, despite the
inherent difficulty in image interpretation,25,37 being
time-consuming and costly, and reported unreliable in
detecting implant loosening.31
The vibration and modal analysis, which has been
broadly used for structural integrity and damage
analysis,18,39 is another class of methods for prosthesis
loosening detection. After a successful implantation,
2017 The Author(s). This article is an open access publication
the implant and bone form unit system, while any
crack or mechanical stiffness deviation can result in
natural frequencies alternations.
Early studies in total hip arthroplasty (THA)
loosening focused on the transmission of sinusoidal
vibration waves to the implants,24,37 where the
nonsinusoidal output waveform indicated THA loosening.
Other researchers used a simple hand-held set-up to
vibrate and measure acceleration over the bony
landmarks of the tibia and femur and studied the frequency
domain amplitude response of the system.16 However,
their set-up suffered from inaccuracies due to the
change in hand-held contacts and the soft tissue
attenuation effect. Although integrating sensors in the
ball head of the THA can reduce the inaccuracies,33 the
inaccuracies related to the shaker contact still
remained. A complete wireless measurement system with
integrated sensors and electronics was introduced and
tested on the bare femur and artificial thigh.26 Using
finite-element models, Qi et al.34 investigated the
vibration analysis for THA loosening and showed that
the peak shifts and harmonics can appear in different
frequency bands and could be indicators of implant
loosening with different sensitivities. Several vibration
analysis techniques including the linearity of system
response to Gaussian input were simulated.44 A device
was designed to excite the bone-implant system and
estimate the frequency response function before,
during and after application of an external torque to
analyze the THA stability.23,46 Vibration analysis was
performed for preoperative monitoring of the THA
integration in which the evolution of frequency
response function at various cement curing stages was
demonstrated.32 In another study,35 six sawbone
models were used for THA loosening detection with a
discrimination between the cup and stem loosening.
The number of peaks in spectra was used to detect the
To design a reliable system for TKA loosening
detection, the general methodology of previous studies
can be used. To compare the bone-prosthesis system
responses before and after implant loosening the
sensor and vibrator placements and the boundary
conditions must be accurately controlled and repeated. To
increase the sensitivity of TKA loosening detection, the
sensors can be integrated into the prosthesis itself.
However, current studies on instrumented prostheses
mainly focused on measurements of force, moments
and kinematics,2,4,5,11,19,22 and none were designed for
in vivo implant loosening detection.
In this study, we targeted the design and feasibility
study of a vibration-based system to detect cemented
knee tibial implant loosening in an objective, facile and
repeatable way. Since the geometry and bone-implant
contact in TKA are totally different from THA, new
and potentially different features need to be extracted
and investigated. We hypothesized that tibial implant
loosening can be detected based on the alternation of
the frequency response of the tibia-cement-implant to
repeatable vibrational stimuli and the identified
discriminative features are repeatable among a
population. Particularly we hypothesized that input–output
vibration coherence can present a discriminative
feature for loosening detection.
MATERIALS AND METHODS
Vibration System and Measurement Units
A vibrator system (Exciter type 4809, Bru¨ el & Kjaer,
Denmark) was used to provide the input vibration
(stimulus) to the bone through skin interface. The
actuator tip was not bolted but was firmly in contact
with skin and applied oscillating compressive force
(Fig. 1). Input vibration was controlled using an
operational power amplifier (BOP72-6 M, KEPCO,
USA) and a signal generator (HP3314a,
HewlettPackard, USA). Stimuli had a sinusoidal waveform,
twice linearly swept from 30 Hz to 3 kHz. The
resultant chirp signal provides rich stimuli which have been
recommended and widely used for system
identification and modal analysis problems.30,40
To generate repeatable stimuli for the
bone-cementimplant system, we built an apparatus (Fig. 1a) which
provides 3 degrees of freedom (DOF) for displacement
(X, Y, and Z axes) and 2 DOF for rotation (around Y
and Z axes) for the vibrator. This apparatus was
securely fixed to the surgical table. The leg was fixed on a
knee implantation bed, fixed on the apparatus
(Fig. 1a), to provide an approximate flexion angle of
45 . This apparatus facilitated the manipulation and
fixation of the vibrator to impact on anatomical
positions with desired impact angle, avoided the slipping of
exciter tip over the skin and allowed repeatable
A 2D accelerometer [ADXL203 family, Analog
Devices, USA, range: ± 5 g, min bandwidth: (0.5 Hz
5.5 kHz)], selected to match the vibration amplitude
and frequency ranges of the stimuli, was fixed on the
vibrator tip (Fig. 1b). It measured the axial and lateral
vibrations and was also used as an inclinometer, before
the test, to control the vibrator tip impact angle. A
force sensor (KD40S, ME-measurement, Germany)
was sandwiched in the vibrator pole to measure the
applied force to the bone before and during the
vibrations (Fig. 1b). The force was monitored in
realtime before starting the stimulation, allowed us to
control the vibrator-bone contact and maintain an
identical contact force across different experiments. All
sensors were linked to a data acquisition board
(NIDAQ6016, National Instrument, USA) which acquired
data with 12-bit ADC resolution and at a sampling
frequency of 8 kHz.
To measure the propagated vibration to the tibial
implant, we sealed two dual-axis accelerometers
(ADXL203) perpendicularly inside a metallic cubic
case (11 9 11 9 13 mm3), to obtain tri-axial
acceleration measurement with desired range and resolution
(Fig. 2). The perpendicularity of sensors axes was
tested prior to the experiments. The gain and offset of
accelerometers in the sensor cube were obtained using
Ferarris calibration.14 This cube is a large scale
demonstrator of the sensors to be integrated into the
smart prosthesis in the future.
We used 14 fresh-frozen lower limb specimens. This
work aims at testing the feasibility of knee implants
loosening detection, while no prior data were available
to perform a power analysis. Cadavers (8 women and 6
men) with no infection history were selected by the
local Institute of Anatomy (CHUV, Switzerland) from
a donor program (mean age 85 y/o, range 46–100 y/o).
The lower limbs were separated from the body at the
upper third level of the thigh. No information about
the age, height and weight of cadavers were available
due to local ethical regulations. Macroscopic
evaluation of the specimens during dissection allowed us to
rule out the presence of any excessive subcutaneous
adipose tissue and major bone defect or disease and
showed that two of them were over-weighted. However
fat subcutaneous tissue covering the medial side of the
proximal tibia was considered low (< 1 cm) in all the
legs. Legs were kept frozen at 2 8 Celsius and kept at
room temperature the day before the experiment. A
size-3 tibial component of F.I.R.S.T TKA (Symbios
SA, Switzerland) was implanted and cemented in each
leg by a senior orthopedic surgeon and no implant was
bigger than the bone surface, suggesting a similar tibia
cut surface area across the specimens. The surgical
procedure was as follows: midline skin incision,
centered on the middle of the patella, followed by medial
parapatellar arthrotomy, removal of the menisci and
both cruciate ligaments, and tibial preparation using
an extramedullary guide for a posterior-stabilized knee
implant. The tibial cut was then cleaned using pumping
water and drying, followed by fixation of the tibial
implant using 40 grams of Palacos R bone cement
(Palacos R, Zimmer, USA). We allowed the cement
become solid for 15 min. Cemented implantation was
chosen due to the exclusive experience in the medical
institution, possibility of a closer-to-reality simulation
of secure and loose implants for cadaver legs since the
integration of cementless TKA in cadaver specimen is
The sensor cube was glued (Loctite 420, Loctite
Corp., USA) on the tibial component. Each leg was
then firmly fixed to the bed with two elastic bands
positioned at the thigh root and directly above
malleoli. The bed was fixed in the adjustable apparatus. The
vibrator was then manipulated to vibrate 10 cm below
the lower tip of the patella with a perpendicular impact
angle with respect to the tibial crest (Figs. 1, 3). This
impact point was chosen to facilitate the measurement
repeatability. The vertical position of the vibrator was
adjusted prior to the stimulation, to maintain a 5 N
force on the contact. The vibration was applied at very
low amplitude (always smaller than 0.39 mm).
Each experiment lasted 4 min during which the
input vibration, contact force, and propagated 3D
vibrations to the tibial implant were measured.
Thereafter the surgeon systematically removed the
cement layer from the bone-prosthesis interface with a
2centimeter wide bone chisel. The removal was
performed under the entire surface of the tibial tray and
around the stem to be closest to clinical findings that
demonstrated that tibial tray loosening happened all
around the implant.17 The implant was then manually
separated from the bone that is currently the criterion
for confirming the implant loosening during prosthesis
surgical revisions.27 After replacing the implant by
press fit, and repositioning and adjusting the vibrator
to get the same impact angle and contact force, we
repeated all measurements. To investigate the
repeatability, each measurement was performed at
least twice after the vibrator was moved away from the
leg and replaced.
The experimental protocol was approved by the
research ethics committee of the faculty of medicine at
the University of Lausanne.
Input–output coherence was used to detect implant
loosing (2.3.1). While for the sake of comparison, other
frequency domain detection techniques (described in
2.3.2) were implemented.
Coherence is a bounded measure of linear
association between two signals.36 In our work, it was used to
indicate to what extent the output vibrations could be
predicted by a linear function of input vibrations. As
such, Coherence (Csx) between the stimulation (s) and
axial output vibration (x) is defined as:
CsxðfÞ ¼ SssSxx
where Ssx, Sss and Sxx are the estimated cross-spectral
density, and estimated auto-spectral densities of s and
x, respectively. In order to compare the coherence of
different trials, the Pearson correlation coefficient
between the coherences (either between repeated
measurements of secure implants or between secure and
loose implants) was calculated and compared to a
threshold to detect any potential loosening. The effect
of different thresholds was demonstrated on a Receiver
Operating Characteristic (ROC) curve.
Power Spectrum Density Estimation
Power spectra of the axially propagated vibrations
to the tibial plate were computed. Considering the
peaks of these spectra as the landmarks of energy
concentration, we extracted a pattern. The power
spectrum (S(f)) of a signal is the Fourier transform of
its autocorrelation function (Rxx):
We applied Welch power spectrum estimation47
with a Blackman windowing to reduce amplitude
errors using MATLAB (Mathworks, USA). Then four
features were extracted from the power spectra: (i)
number of peaks in 750–900 Hz frequency band, (ii)
peak shift at 700–1200 Hz band, (iii) peak shift at
1200–2200 Hz band, and (iv) peak flattening at 500–
1500 Hz band.
First, peaks were extracted automatically as the
points with larger amplitude than the two left and two
right neighbors, representing a neighborhood window
of 15.625 Hz, in the Welch spectra. For detecting a
peak shift, only the peaks with the shifted frequency of
more than a threshold of 70 Hz were considered. For
detection of peak flattening, first, a Gaussian was fitted
to a window around each peak. Then, the peak width
was calculated as the distance between the
mid-amplitude points of the Gaussian, also known as the full
width at half maximum. Assuming that each peak can
fit a Gaussian function with a standard deviation of r,
we computed its width using Eq. 3. A threshold of
100 Hz was used to detect if a peak was flattened, in
other words, if its width increased more than 100 Hz.
Peakwidth ¼ 2rpffi2ffiffilffinffiffiffiffiffi2ffiffiffi :
Since two or three repetitions of trials were
performed for each leg, for secure and loosened case,
subsamples of measurements were used to evaluate
sensitivity, specificity, and accuracy of different
methods. Each subsample contained a secure and a
loosened measurement trial for each specimen. Subsamples
were chosen such that every combination of
measurements per specimen included in data analysis. Then the
expected value and standard deviation of each
performance metric were estimated for each specimen and
then across the 14 legs.
In order to investigate the robustness of output
acceleration power spectra, the intra-class correlation
(ICC) was computed for each specimen (leg) on the
spectra resulted from the repeated measurements at
each condition (secure or loosened). The mean and
standard deviation of ICCs for each condition were
then calculated over the 14 legs. The frequency at
which the power spectra peaks appear were estimated
and compared statistically between the secure and
loosened cases using Wilcoxon rank sum test with a
significance level of 0.05. The choice of statistical test
was made due to the low number of samples and the
expected not-Normally distributed frequencies of the
A McNemar test with Bonferroni correction was
used to compare the proportions of successful
detection. Besides, to statistically compare the sensitivity,
specificity and accuracy of different detection
techniques, a Friedman test was performed to investigate
the group-level statistical differences. Then, Wilcoxon
signed rank test with Bonferroni correction was
performed between each pair of techniques.
Figure 4a demonstrates the True Positive Rate
against the False Positive Rates for different applied
thresholds on the correlation between the obtained
coherences from different trials. The distribution of
correlation between the coherences for the secure
implants and loose implants are demonstrated in Fig. 4b.
The correlation coefficient between the coherences
computed for the repeated measurements of secure
implants was 0.87 ± 0.19 (mean ± SD over the 14 legs),
while the correlation between the coherences computed
for the corresponding secure (baseline) and loosened
implants decreased to 0.60 ± 0.15. The optimal
threshold for distinguishing loosened implants from
secure ones was selected at 0.82 based on Fig. 4 leading
to the highest expected accuracy, sensitivity, and
specificity of 92.26, 91.67 and 92.86% respectively (Table 1).
In addition, a correlation coefficient of 0.92 ± 0.07
was obtained between the coherences computed for the
repeated measurements of the loosened implants.
The power spectrum of the measured output
vibration showed variable repeatability across the legs
on tridimensional axes. Since the most repeatable
pattern was obtained in the longitudinal axis, the
vibration analysis was performed exclusively on this axis.
Power spectra for each leg were estimated in two
cases: after implant cementation, considered as a
baseline (well-fixed), and after total implant loosening.
Tibial implant loosening was characterized by the
appearance of a new peak between 750 and 900 Hz
(Fig. 5). This peak appeared in 11 out of 14 total
implant loosening cases. In eight legs, the new peak
appeared between the first and the second baseline
peaks. In two cases the new peak appeared between
second and third and in one case between third and
fourth peaks of the baseline spectrum. Repeated
measurement of the vibration propagation on each
condition was analyzed and summarized in Table 1,
i.e., sensitivity, specificity and accuracy of the method
in detection of secure or loosened implants. It must be
noted that the new peak did not always appear in the
repeated measurements which resulted in an expected
sensitivity and specificity of 78.9 and 77.2%
Peak shifts were examined as an indicator of
loosening. While, in general, a shift to the left side of the
spectrum can represent the loosening, in some cases, a
peak split can instead appear. The peak split might be
observed as the shifting of peaks to the both left and
right side of the frequency band.
The frequencies of the detected peaks are shown in
Fig. 6. As demonstrated in the boxplots, Normal
distribution cannot be assumed for the frequencies,
therefore a Wilcoxon rank sum test performed in each
peak clusters between the secure and loose implant
samples (Fig. 6). The test showed two significant
differences in the frequencies of two peak groups, i.e.,
second peaks (mostly around 750–1000 Hz) and fourth
peaks (mostly in the band of 1200–1800 Hz), with p
values < 0.0001.
The appearance of multiple peaks in a close
frequency band can possibly result in flattening of a
spectrum peak computed via Welch method.
Therefore, the peak flattening results (Table 1) showed high
In test–retest repeatability analysis of the power
spectra, no change in the number of peaks was
observed in the measurements of 11 legs, while in two
legs a peak in the power spectrum sometimes was not
observed in repeated measurements of secure implant.
In another leg, one of the peaks in the power spectrum
sometimes was not observed both in repeated
measurements of secure and loosened implant. ICCs of
obtained spectra for sampled repeated measurements
were 0.86 ± 0.08 and 0.97 ± 0.02 (mean ± SD over
the 14 legs) for secure and loosened cases, respectively.
The result of McNemar test on the proportions of
successful loosening detection is depicted in Fig. 7. The
correlation coefficient between the computed input–
output coherences of secured and loosened implants
showed significantly better detection results than peak
shift and peak flattening in propagated vibration
spectrum. Moreover, the appearance of new peak
showed significantly better detection results than peak
shift in 700–1200 Hz and peak flattening. Friedman
test on the sensitivity, specificity and accuracy of the
implant loosening detection methods yields
p < 0.000004. The Wilcoxon signed rank tests between
each pair of loosening detection techniques (Fig. 8),
with Bonferroni correction, revealed significant
differences in sensitivity and accuracy between the
coherence based detection and output vibration based
techniques. The coherence based technique showed
significantly higher specificity than loosening detection
The mean and standard deviation of each metric were obtained over repeated measurements over 14 legs.
based on a new peak or peak shift in output vibration
DISCUSSION AND CONCLUSION
In this work, we presented the proof of concept of a
vibration analysis based system to detect the tibial
implant loosening in cemented knee prostheses for
post-operative follow-up. To the best of our
knowledge, this is the first vibration analysis study for tibial
components loosening detection in knee prostheses.
Our system works based on transcutaneous
stimulation of the crest of the tibia and measuring the
propagated vibration using a 3D accelerometer unit firmly
attached to the tibial implant. Input–output vibration
coherence was used to detect the implant loosening and
benchmarked against the output vibration frequency
domain analysis techniques used for hip implants in
the previous studies. 14 lower limb specimens were
tested, a much larger sample size than existing studies
on THA, to evaluate the performance of the detection
techniques. The proposed coherence-based technique,
with 91.67% sensitivity and 92.86% specificity
outperformed the other methods which exclusively relied
on the output vibration propagated to the implant.
Among the tested output vibration frequency
domain features, the appearance of a new peak and peak
flattening demonstrated high sensitivity (77.2%) and
specificity (84.6%). Obtained high repeatability of
power spectra and input–output coherence was
associated with our proposed instrumentation that allowed
producing highly repeatable vibrations stimulation.
This beside simplicity of adjusting and performing the
test, i.e., 2 to 3 min to reorient the vibrator and 4 min
to perform the experiment, suggested a great potential
for translating the proposed system to loosening
evaluations of instrumented knee implants in clinical
The output power spectrum techniques relied on the
analysis of the vibration measured by fixed
accelerometer cube on the tibial implant, and the
implicit assumption of identical stimulation for different
loosening states. Close scrutiny on the peaks of the
spectra revealed that after implant loosening, a new
peak appeared in the 750–900 Hz band at 11 out of 14
legs. The appearance of the new peak which is in
accordance with several reported results in THA
loosening detection,16,35,46 can be justified as a new
mode in the tibia-cement-implant dynamics caused by
the broken cements and micromotions. The peak shifts
showed to be less sensitive to loosening with
sensitivities less than 70%. It must be noted that a peak split
occurred in several cases due to the implemented
loosening which could be observed as the shifting of
peaks to the both left and right side of the frequency
band. This ambiguity could be contributed to the
inferior performance of loosening detection based on
the shift of peaks when comparing to loosening
detection based on the appearance of a new peak. The
peak flattening showed however to be a specific feature
which can be used to reduce the false negatives.
Coherence analysis, in contrast to the previous
methods, is less sensitive to the variations of the input
stimulation across the experiments. However, it relies
on a linear system assumption which is not preserved
particularly for the loosened implants.24,37
Nevertheless, significant difference was observed between the
correlation of coherence before and after the implant
loosening with the baseline coherence (coherence in the
secure implant). The coherence-based method also
obtained the highest accuracy 92.26% among the other
methods, and can thus be recommended for implant
In general, the explored features showed
encouraging outcome in the detection of totally loosened
implants. Our approach to artificially creating the
loosening was confirmed by the surgeons to be similar
to the natural loosening observed for in vivo cases.
Since our hypothesis was that the implant loosening
changes the mechanical contact between the implant
and bone, our approach to creating artificial loosening
was a necessary step in the validation of our proposed
Notably, the correlation coefficients between the
coherences calculated for the two repeated
measurements of the secured implants and loosened implants
were around 0.90. Although slightly larger in the latter
case, this difference is not statistically significant
(Wilcoxon rank sum test p value > 0.51). These high
correlations indicate the repeatability of the
measurements in both cases. At the same time, the correlation
coefficient between the coherences computed for the
corresponding secure and loosened implants was
around 0.60. This low correlation shows a large
difference between coherences measurements of secure
and loosened implants. This considerable difference
(0.90 vs. 0.60) indicates the introduced coherence
correlation as a robust measure for loosening detection.
Considering the complex geometry and material
properties of the tibia and tibial implant, and the
boundary conditions at their cemented interface, we
did not expect to have all the peaks in similar
frequency bands to THA studies and have vibration
propagation exclusively in the excitation plane.
Instead, we expected to observe a 3D micromovement of
the implant as the result of vibration propagation
throughout the bone. Despite designing the cube to
provide 3D acceleration measurement, the power
spectrum of the measured acceleration showed lower
repeatability across the specimens in the traverse axes
than the longitudinal axis. Since the most
repeatable patterns were obtained in the longitudinal axis,
the vibration analysis was performed exclusively on
this axis. The reason for lower repeatability in
transverse axes might be two folds. First, the tibia could
have slightly rotated around its longitudinal axis on
the implantation bed that could cause crosstalk in the
two transverse components of the accelerometer
readout. Because of such specimen placement error, we
expect to have higher inter-specimen variability in each
of these two transverse components. Second, the
vibration propagated longitudinally along tibia and
thus its amplitude measured on the implant was larger
in the longitudinal direction leading to larger signal to
noise ratio and thus higher inter-specimen
Despite exclusive vibration analysis on the
longitudinal axis, the 3D acceleration measurement is
beneficial for controlling and repeating vibration tests,
particularly for the leg placement. When the leg is in a
static position, the accelerometers (off-axial channels)
could be used as inclinometers, based on the projection
of the gravitational force, to correct the orientation of
the leg. The 3D accelerometer could also be be fused
with other external sensors for kinematic
measurements.3 Although this study was a proof of concept
and focused on the feasibility of the loosening
detection method, the other aspects of the smart implant
design, namely remote powering and wireless
communication units,4,6 supplementary electronics and
packaging have been addressed in our other
studies.15,45 The discussed sensor cube, fixed on the tibial
tray, needs to be miniaturized and embedded in the
final smart prosthesis together with the supplementary
In the current study, only one size of tibial cemented
implant was used to provide a fair comparison in a
rather small sample size. Experiments with the other
types and sizes would be necessary for future. Also it is
worthy to note that no femoral component was
implanted in this study due to isolating the effect of
loosening at the tibia-prosthesis interface. We expect
that the contact of tibial and femoral parts in a
prosthetic knee would minimally alter the results since this
contact is much looser than the loosened-cemented
contact between the tibia and tibial implant.
Nevertheless, this effect should be investigated in the future.
Bone density, fat content and leg size could
influence the output power spectra and input–output
coherences across different specimens. While the
difference in bone density and leg size can result in peak
shift in the frequency bands of interest for implant
loosening detection, the fat content is expected to affect
mostly the low-frequency component of both output
power spectrum and input–output coherence. In the
present study, since each leg was compared to itself (its
baseline results) no significant change in the detection
performance is expected due to the fat density, leg size
and bone density. Nevertheless, the effects of these
features on the frequency response of the bone-implant
should be investigated in the future studies.
Also further studies can be performed to examine
the possibility of localizing the defect in the
bone-cement-implant interface by employing the result of
studies in structural damage detection and
localization.10,18,41 The comparison of our results with
current radiological techniques was not relevant due to
the low accuracy of X-ray for implant loosening
detection.43 Alternatively, the techniques such as bone
scintigraphy, which has higher accuracy, cannot be
performed on cadaver specimens.
This study focused on the cemented tibial
component loosening due to the possibility of simulating it in
ex vivo legs and the exclusive experience with cemented
TKA in our institution. The proposed system can also
be used for cementless implants in future.
In this study, we proposed sensitive and objective
loosening detection techniques that do not depend on
the physicians’ subjective views. These techniques
could be easily translated into clinical practice where
the results of vibration analysis on each leg would be
compared to itself. The inter-subject differences in
bone quality, soft tissue density and volume, and the
implant type would minimally affect the implant
loosening detection. This was reflected in high
specificity of our proposed implant loosening detection
The authors acknowledge Nano-Tera
(SNF20NAN1_123630) and the ‘‘Lausanne
Orthopedic Research Foundation (LORF)’’ for funding this
work, M. Bourki for his valuable assistance during the
experiments, P. Morel and J. Gramiger for their help in
setup design. We would like to acknowledge the equal
contribution of the first two authors, namely AA and
CONFLICT OF INTEREST
There is no conflict of interest.
This article is distributed under the terms of the
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indicate if changes were made.
32Pastrav, L. C., S. V. Jaecques, I. Jonkers, G. Perre, and M.
Mulier. In vivo evaluation of a vibration analysis technique for
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