Sensor Validation and Diagnostic Potential of Smartwatches in
Julian Varghese 1*, Catharina Marie van Alen 2, Michael Fujarski 1, Tobias Warnecke 3, and Christine Thomas 2
1 Institute of Medical Informatics, University of Münster, Germany; firstname.lastname@example.org
2 Institute of Geophysics, University of Münster, Germany; email@example.com
3 Department of Neurology, University Hospital Münster, Germany; firstname.lastname@example.org
* Correspondence: email@example.com;
Abstract: Smartwatches provide technology-based assessments in Parkinson’s disease (PD). We
present results for sensor validation and disease classification via Machine Learning (ML). A com-
parison setup was designed with two different series of Apple smartwatches, one Nanometrics seis-
mometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical
smartwatch measurements were acquired from a prospective study including 450 participants with
PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches
and within a 15-min examination. Symptoms and medical history were captured on the paired
smartphone. A broad range of different ML classifiers were cross-validated. Amplitude and fre-
quency differences between smartwatches and the seismometer were under the level of clinical sig-
nificance. The most advanced task of distinguishing PD vs DD was evaluated with 74,1% balanced
accuracy, 86,5% precision and 90,5% recall by Multilayer Perceptrons. Deep Learning architectures
significantly underperformed in all classification tasks. Smartwatches are capable of capturing sub-
tle-tremor signs with low noise. This study provided the largest PD sample size of two-hand smart-
watch measurements and our preliminary ML-evaluation shows that such a system provides pow-
erful means for diagnosis classification and new digital biomarkers but it remains challenging for
distinguishing similar disorders.
Keywords: Smartwatches, Artificial Intelligence, Movement Disorders, Parkinson’s Disease
Parkinson’s Disease (PD) is the second-most neurodegenerative disorder – following
Alzheimer Dementia - and worldwide burden that has more than doubled over the last
two decades . Early and accurate diagnoses improve quality of life, reduce work losses,
which is why missed diagnoses mean missed opportunities . Currently, PD diagnosis
is primarily based on clinical assessment, which is challenging and associated with overall
misclassification rates of around 20%-30%; Rizzo et al. 2016 conducted a meta-analysis
and reported pooled diagnostic accuracy of 73.8% for general practitioners or general neu-
rologists with 95% Credible Interval (CRI) of 67.8%-79.6%.
Clinical assessment may not identify subtle changes in movement pathologies as e.g.
weak tremor, its frequency or slowness of movements . Regarding diagnostic accuracy
and treatment monitoring, there is a strong need for new technological objective bi-
omarkers, which are capable of capturing these subtleties with high precision and ma-
chine-readable . In the era of digital transformation of healthcare, consumer wearables
with multi-sensor technology provide a source of objective movement monitoring allow-
ing for greater precision in recording subtle changes unlike current clinical rating scales
in hospital routine . Though there is an increasing number of such wearables and mo-
bile apps or even mature medical devices as the Parkinson’s KinetiGraphTM system, there
is a low number of large-scale deployments .
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 22 March 2021 doi:10.20944/preprints202103.0542.v1
© 2021 by the author(s). Distributed under a Creative Commons CC BY license.
Regarding PD, some systems have shown promising diagnostic potential when ana-
lyzing voice, hand movements, gait, facial expressions, eye movements and balance [7–
12]. Most of these promising examples used Machine Learning approaches for disease
classification. However, the reported accuracies need to be taken with high caution be-
cause the implemented models were trained and tested on low sample sizes regarding PD
(n<<100), which carries a high risk of overfitting. Moreover, we could not find any ap-
proach that include similar movement disorders as an important control group for differ-
ential diagnoses. A simple classification model that only differentiates between PD and
Healthy is of only limited clinical use as it was only trained and tested between those
classes and thus might have only learned to identify general movement anomalies, which
differ from the healthy population but do not represent Parkinson-specific features.
Hence, such models could misclassify other movement disorders as Multiple Sclerosis or
Essential Tremor. Also, in clinical reality, the Health Practitioner or the Neurologist can-
not initially assume whether the patient is either healthy or has PD. Therefore, classifica-
tion models for potential diagnosis should consider differential diagnoses.
Our research focusses on acceleration-based hand movement analyses using the
Smart Device System (SDS) that is currently part of a prospective human subject trial .
The study has recruited and measured >350 participants and has generated one of the
largest databases for PD, differential diagnoses and healthy subjects with acceleration
data from a neurological examination including left and right side of the body and struc-
tured clinical data on non-motor symptoms (e.g. sleep disturbances, loss of smell, depres-
sion). The system includes simple consumer devices by Apple, utilizing smartwatches to
capture acceleration and a paired smartphone for clinical data. To our knowledge, official
information on the smartwatch raw measurement accuracy is not publicly available.
Therefore, the devices were evaluated by a systematic comparison with a gold standard
utilizing a broadband seismometer.
Apart from this sensor validation, the SDS is integrated into a neurological examina-
tion. It consists of ten steps to monitor and provoke specific movement characteristics such
as tremor or slowness of movements. While she study is still running till the end of 2021
and includes further smart devices data as tablet-based drawing and voice analyses, this
manuscript aims to focus on following research aims:
• Sensor validation to measure the precision of smartwatches regarding acceleration
amplitudes and tremor frequencies. As goldstandard, we conducted a comparison
experiment utilizing a seismometer and a high-precision shaker. As a result, we as-
sessed the level of precision regarding the smartwatches. This is particularly useful
in case of subtle tremors, which have acceleration amplitudes of <0.05 g and are hard
to capture by human vision.
• Timeseries features were extracted based on expert-based feature engineering and
literature data. A broad range of Machine Learning models was trained and cross-
validated to assess classification performances. To complement the expert-based fea-
ture engineering by a pure automatic feature extraction method, a Deep-Learning
Neural Networks with the raw time series data as input were trained and cross-vali-
dated as well.
2. Materials and Methods
2.1. Study setting
The prospective study started in 2018 and was extended till the end of 2021. It received
approval by the ethical board of the University of Münster and the physician's chamber of West-
phalia-Lippe (Reference number: 2018-328-f-S). It is being conducted at the outpatient clinic of
Movement Disorders at the University Hospital Münster in Germany. The details of the study
design and the protocol have been published previously . Study registration ID on Clinical-
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 22 March 2021 doi:10.20944/preprints202103.0542.v1
Table 1 lists participants population characteristics. Further information on demographics,
differential diagnoses is provided for each sample in the supplement Patient-Population. All
diagnoses were confirmed by neurologists and finally reviewed by one senior movement disor-
Each participant wore two smartwatches, one on each wrist, while seated in an armchair
and following a pre-defined neurological examination, which was instructed by a study-nurse.
This examination was designed by movement disorder experts with the primary aim to establish
a simple to follow examination in order to capture most-relevant acceleration characteristics. The
data included smartwatch-acceleration data and further clinical data encompassing non-motor
symptoms based on the Parkinson's Non-motor Symptoms Questionnaire  – on the paired
smartphone . Each examination took 15 minutes per participant on average. Each assessment
step is summarized is Table 2.
Table 1. Participant population. DD: Differential diagnoses including movement disorders other
than PD as Essential Tremor, Atypical Parkinsonism, secondary causes of Parkinsonism and Dys-
tonia, Multiple Sclerosis.
Average Age (SD)
Table 2. Smartwatch-based Examination steps.
Rest tremor. Participant is seated with his eyes closed in resting
position, positioning standardized to Zhang et al. 
Rest tremor in a stressful situation. Participant starts from 100,
subtracts 7, and stops after five answers.
Lift and extend arms according to Zhang et al. 
Remain arms lifted.
Hold 1kg weight in every hand for 5 seconds. Start with the
right hand. Then, have the participant’s arm rested again as in
Finger pointing. Participant should point with his index fin-
gers to examiner’s lifted hand. Start with participant’s right in-
dex, then left, then repeat.
Drink from glass. Have the participant grasp an empty glass
with his right hand as if he/she would drink from it. Then re-
peat with his/her left hand.
Cross and extend both arms.
Bring both index fingers to each other.
Let participant’s both index fingers tap his/her nose. Start
with the right, then with left index. Then extend the arms.
Entrainment. While leaving the arms extended, have the partic-
ipant stamp with his/her right foot according to the stamp fre-
quency of the examiner. Then have him/her repeat with the left
2.2. Smartwatch sensor validation
A Seismometer is a device that captures weak ground motion caused by seismic
sources, e.g., earthquakes, explosions or ambient noise . These instruments generally
have a large bandwidth and dynamic range . The Nanometrics Trillium Compact is a
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 22 March 2021 doi:10.20944/preprints202103.0542.v1
triaxial seismometer, measuring ground velocity and classified as a broadband instru-
ment with -3dB points at 120s and 108 Hz. The self noise level is below -140dB and the
clip level at 26mm/s up to 10Hz and 0.17g above 10Hz . We combined the Trillium
Compact with a Taurus 24-bit Digital Recorder  , that digitizes the motion that the
seismometer measures. This combinations allows for accurate measurements of ground
motion  and is therefore considered as a Gold standard instrument for raw measure-
ments of acceleration.
We conducted a shaker table experiment, where two Apple watches, Series 3 and 4,
and the Trillium Compact seismometer were simultaneously accelerated by oscillatory
motions with tremor-typical frequencies and amplitudes. Because tremor is an oscillatory
movement, the use of a shaker table provides a means of testing accuracy of the method.
The setup of the validation experiment is shown in Figure 1, where the seismometer,
smartwatches were placed on a shaking table.
The watches were further attached with tape to prevent unwanted movement due to
the slightly curved backside of the watches. The shaker table is placed on a decoupled
platform to reduce ambient noise and oscillates vertically with a range of frequencies and
amplitudes. Due to the experimental setup and since the vibration table moves in the ver-
tical direction, only the z-axis of the watches and the seismometer was examined here.
However, a significant difference in measurement accuracy between all three sensor com-
ponents of the seismometer is not to be expected since the device records on three orthog-
onal axes U.V.W, which are then rotated into vertical and two horizontal components
North and East .
Figure 1. Experimental setup of the sensor valida-
tion experiment. Apple Watches Series 3 and 4 and
a Nanometrics Trillium Compact seismometer are
placed on a vertical vibration table. The table sim-
ultaneously accelerates the devices by oscillatory
motions with tremor-typical frequencies and am-
plitudes. Both watches are connected to Apple
iPhones (not in this figure) via Bluetooth, where
the measurement data were stored. The seismome-
ter data are collected on a digitizer (not in this fig-
ure), that the device is connected to.
The smartwatches are described to have a sampling rate of 100 Hz and we set the
sapling rate of the seismometer to 100 Hz as well. A total of 43 measurements were per-
formed on two different days. The duration of each measurement was set to 20 seconds
for the watches, similar to the assessment steps performed with patients.
For each test, the table oscillated with a set amplitude and frequency that was kept
constant during the measurement period. One test was carried out without vibration, to
measure the difference in self noise of the watches and the seismometer. For the remaining
tests we varied the frequency of the oscillation between 3Hz and 15Hz as this range covers
tremor-typical frequencies . The oscillation amplitude was varies between 0.002g and
0.1g, which is considered as high-resolution for tremor amplitudes as values < 0.01g are
barely visible by human vision but still clinically relevant to measure subtle tremor in
The data had to be processed after the experiments: First, the data of the seismometer
were deconvolved with the instrument response. During the deconvolution the counts
per Volts scaling factor of the raw data and the frequency-dependent sensor response are
removed. Since the seismometer records velocity while the watch records acceleration, the
seismometer data were differentiated, scaled to SI units and divided by 9.81m/s^2, such
that the output is in multiples of g, the Earth's acceleration.
To determine the oscillation frequency for each 20s measurement for both the seis-
mometer and watches, the data were analyzed in the spectral domain, by applying the
Fast Fourier Transform (FFT). The dominant frequency of each dataset was identified
and compared. Prior to the FFT, all data were zero-padded to reach a frequency bin spac-
ing of 0.01Hz because the frequency scale of the shaker table were graduated in 0.01 Hz
The oscillation amplitude was calculated in the time domain on the pre-processed
datasets. For 20 consecutive periods, the maxima and minima of the signal were identified
and used to calculate the peak-to-peak amplitudes. The resulting 20 peak-to-peak ampli-
tudes were averaged and divided by 2. Subsequently, the results of the watches were com-
pared to those of the seismometer in order to assesses the accuracy of the watches.
2.3. Machine Learning Pipeline and Features
Three relevant classification tasks were trained and cross-validated:
1. PD vs Healthy
2. Movement Disorders (PD+DD) vs Healthy
3. PD vs DD
It is assumed, that the first two tasks are of lower classification difficulty as the sys-
tem only needs to be trained for non-healthy characteristics. Such a system could still be
helpful in home-based settings or at general practices e.g. to indicate whether certain ab-
normal movement characteristics (e.g. hand tremor) is pathologic or still normal (e.g.
physiological tremor). The third one requires more advanced and differential features
analyses in order to distinguish movement disorders with similar phenotypical character-
istics from each other.
Figure 2. Overview of data analytics pipeline. SVM= Support Vector Machine with radial basis function. CatB = CatBoost, MLP =
Multi Layer Perceptrons with two hidden layers, DL = Deep Learning Architecture.
Table 3. Machine Learning Features.
Average Age (SD)
Medical History Question-
Information on demographics, height, weight, family his-
tory. Further details provided in Varghese et al. . Medi-
cation is captured but not used as training-feature as it is
too closely linked to the target classes.
The number of items answered with 'yes' in the Parkin-
son's Disease Non-Motor Scale by the Movement Disorder
Create an Amplitude-Histogram of acceleration data. Ap-
ply Euclidean norm on all three axes to generate 1-dimen-
sional time-series vector, and pick the 30th to 70th percen-
tile in 5 percent steps. Applied for all assessment steps.
Tremor Side Dominance
Use the 90th percentile of the left and right arm accelera-
tion and calculate the ratio. Applied for all assessment
Standard Deviation of Ac-
Calculate the Standard Deviation of the acceleration data.
Applied for all assessment steps.
Fast Fourier Transforma-
Calculate the 3-dimensional FFT for the assessment step
and use a polynomial regression to reduce the output di-
mensinoality. Polynomials of degree 3 are used. Applied
for all assessment steps.
The extracted features are listed in Table 3. We provide further details and pseudo-
code of feature extraction in the Machine-Learning Supplement. A previously developed
Python-based data analytics pipeline is re-utilized . The entire analytics process is
summarized and illustrated in Figure 2. The different Machine Learning classifiers were
Support Vector Machines (SVM), a modern gradient boosting decision-tree model
called CatBoost (CatB)  and Multilayer Perceptrons (MLP), which are a classical type
of Artificial Neural Networks. These were trained and validated within the framework of
nested cross-validation  using 5 outer and 5 nested inner data folds to ensure unbiased
training and testing, as well as unbiased optimization of hyperparameters. While the inner
folds are used to train each model and to optimize its hyperparameters in a grid-search
(m different hyperparameter values results in m different models configurations), the
outer folds evaluates the test performance of trained and hyperparameter-optimized
models. Before each inner fold model training, we apply the random undersampler from
Scikit Learn 0.21.3  in order to remove the bias towards the majority class by randomly
removing samples of that set. Moreover, the standard scaler from Scikit Learn subtracts
the mean and scales to unit variance for every feature. The Principal Component Analysis
(PCA) reduces the dimensionality, the Scikit Learn-based 'Select Percentile' step randomly
selects a subset of features, which are then used for training the classifier. We optimize
the hyperparameters for the PCA, the Select Percentile and the specific classifiers.
The Multi-Layer Perceptron and the Deep Learning architecture is implemented us-
ing the KERAS 2.2.4 package and Google’s Tensorflow 1.3.1, which provides full GPU
support . We considered various state-of-the-art architectures including Convolu-
tional Neural Networks in ResNets and Long-Short-Term Memories (LSTM) . Detailed
architectures are provided in the Machine-Learning Supplement.
To evaluate their performance for automatic time-series feature extraction from ac-
celeration data, they only received the raw acceleration data and the questionnaire data
(medical history + symptoms) as input, but not the engineered time-series features in table
Test performances for all three classification tasks are reported as mean values for
Precision, Recall and F1-measure based on the outer fold validations including standard
deviations. Due to the imbalance of the three disease classes, balanced accuracies [28,29]
are provided as well. As such, the baseline performance of all binary classification tasks
is 50%, which corresponds to random guessing.
3.1. Smartwatch sensor validation
Figure 3a shows the differences between dominant frequencies of the seismometer
(used as the gold standard device) and Apple Watches Series 3 and 4 data (consumer
grade device). Overall, Apple watches Series 3 and 4 seemed to measure higher frequen-
cies than the seismometer, however, deviations were in the low milli-Hertz range (up to
10 mHz). With increasing frequencies, there was an increase in frequency deviation for
both watches and for all experiments.
Figure 3. Differences between the dominant frequency measured by the Trillium Compact Seismometer and Apple Smartwatches
Series 3 and 4 in a shaker table experiment. The experiment was conducted on two different days with the Apple Watch Series 3.
The figure shows the difference in dominant frequency (a) using the pre-defined watches sample rate and (b) using the watches
actual sample rate (calculated with watch-specific time vectors) for spectral calculations
As mentioned above, the watches' sampling rates were set to 100 Hz. When calculat-
ing the watches actual sampling rate using the watch-specific time vectors, we found that
the sampling rates of the watches were non-uniform and up to 0.6 Hz smaller than speci-
fied 100 Hz. The increasing deviations with increasing frequency therefore resulted from
the differences in programmed sampling rate of 100 Hz and the actual sampling rate for
spectral calculations. Figure 3b shows corrected data based on the actual sampling rates
of the watches. In the considered range, no clear increase in deviation with increasing
frequency is recognizable anymore. Approximately 55% of the Series 3 and 59% of Series
4 dominant frequencies did not deviate from the seismometer up to the second decimal
place. The remaining measurements deviated by up to 0.01Hz for both Series 3 and 4,
which still provides high precision of tremor frequency capture, as clinical tremor docu-
mentation is documented in the rage of 4-18 Hz and step sizes of full Hz units .
We measured the self noise of the seismometer and the watches on the non-vibrating
table. The results are depicted in Figure 4 and show that the watches had a higher noise
compared with the seismometer but the RMS self-noise level was still below 0.001g for
both watches. The 0g-offset was found to be below 2*10^-4 g. The power spectral density
shows, that the noise of the smartwatches had a similar intensity at different frequencies.
Figure 4. (a) Self Noise of watches and seismometer and (b) Power Spectral Density (PSD) of watches, captured during a 20 sec
period without vibration of the shaker table.
Figure 5 depicts the difference in measured oscillation amplitude for the seismometer
and the smartwatches. For all measurements, smartwatch Series 3 and 4 measured higher
amplitudes than the seismometer. Up to 0.04g oscillation amplitudes, the amplitude dif-
ferences between the watches and the seismometer showed no trend and were below
0.002g. Oscillation amplitudes > 0.05g lead to a larger deviations for both Series 3 and 4
and a trend is visible. We found the maximum deviation of 0.005g. The amplitude meas-
urements of the watches and seismometer agree within their corresponding standard de-
Figure 5. Measured oscillation amplitude of the seismometer and the watches are plotted against each other. The standard devia-
tions of the amplitude mean values (of the watches) are plotted as error bars. The grey line corresponds to a perfect agreement be-
tween the oscillation amplitude measured by the watches and the seismometer.
3.2. Classification Performances and Feature Importance
Tables 4-6 list model performances for all three classification tasks. Apart from the
Deep Learning model, the other three classical machine learning models performed simi-
lar in respect to their standard deviations, with balanced accuracies above 80%, precision
and recall above 90% in the two simpler classification task. Regarding the most difficult
task, which required separation of Parkinson’s disease from similar movement disorders,
all three models performed lower with balanced accuracies between 67% and 74%. The
MLP performed best in two of three tasks (PD+DD vs Healthy, PD vs DD) in terms of
Table 4. Performances for Classification Task 1, PD vs Healthy.
SVM - rbf
Table 5. Performances for Classification Task 2, Movement Disorders (PD+DD) vs Healthy
SVM - rbf
Deep Learning 5
Table 6. Performances for Classification Task 3, PD vs DD.
SVM - rbf
Deep Learning 5
Among the different combinations of DL architectures, the best performing architec-
ture included a light ResNet that could only reach balanced accuracies lower than 60%.
Noteworthy, the inclusion of LSTMs consistently weakened the classification perfor-
mance and therefore did not participate in our final DL architecture. Though the DL com-
ponents underperformed in this complex task of diagnosis classification, a post-hoc anal-
yses was conducted to figure out whether DL can correctly classify the assessment steps
(e.g. does time-series belong to assessment step 6, “drinking glass” ?). This tasks would
represent a typical time-series classification problem without requiring disease-depend-
ent feature engineering. Here, the best DL model performed with an accuracy of 78,6%
with the ResNet. The same tasks reduced to the assessment steps 'drink glass' and 'point
finger' even performed with an accuracy of 94,6% using DL architecture with simple
Dense Neural Networks. The detailed architecture for DL models and their performances
is provided in the Machine-Learning Supplement.
The SDS is an app-based mobile system that connects consumer devices for high-
resolution monitoring of acceleration characteristics in different neurological disorders
and questionnaire-based data capture of patient symptoms.
The seismological sensor validation showed high agreement between the smart-
watches and the gold standard setting. While clinical tremor documentation ranges be-
tween 4-18 Hz with step sizes of 0.1 - 1Hz, the watches differed slightly from the gold
standard at around 0.01 Hz. While human tremor amplitude threshold can be estimated
at < 0.01-0.05g , the smartwatch amplitude-deviations were within the range of 0.001g
and 0.005g. This shows the watches are capable of measuring movement subtleties or
hand-tremor amplitudes and frequencies with much greater precision than clinical docu-
mentation or even human vision does. We reproduced these findings with multiple meas-
urements and two Apple-based smartwatch models of different build years.
When integrating two smartwatches and paired smartphone to the SDS coupled with
different AI-based classifiers we could show high diagnostic accuracies, above 80%, par-
tially with precision and recall above 90% for simple classification tasks. Related work
shows even higher performances, consistently above 90% accuracy when using other data
modalities, e.g. voice-analyses . However, while these findings doubtlessly show some
diagnostic potential, they have to be interpreted with high caution as we believe these
results are easily overestimated due to three key reasons: First, the overall sample size of
almost all related studies were limited (n<<100). Second, model hyperparameters were not
optimized in a separate nested set. Third, multiple measures of the same individual were
taken, which can lead to identity confounding. To address these frequent drawbacks and
provide higher degree of generalizability, we have generated – to the best of our
knowledge - the largest database on this topic with more than 400 individually measured
participants using nested cross-validation for all models and hyperparameters. In addi-
tion, we included the important control group of differential diagnoses. As expected, the
most difficult task to separate PD from similar Movement Disorders was evaluated with
much lower balanced accuracies of around 70%. This shows that further feature engineer-
ing and further integration of other promising modalities (acceleration, speech, voice or
finger-tapping are needed. All these data modalities were studied in isolation with prom-
ising findings [4,8,30] and could be integrated within one system consisting of consumer
devices. The results of our deep-learning architecture clearly show that automatic feature
extraction is underperforming in this sample size dimension (n < 1000) and there is a
strong need for engineering clinically relevant features in raw acceleration data.
A common limitation with related work, which is also not addressed by this study is
the missing evaluation of real predictive capabilities for early diagnosis as we can only
include patients, which have already been diagnosed or healthy participants, for which
we don’t know if they will develop a disease condition. Our study included a broad range
of different disease progress states according to Hoehn & Yahr  or years from disease
onset, but an observational epidemiological study with healthy to PD transformation data
would be ideal to test disease prediction. Nevertheless, our work can provide potential
features and methods, which need to be further studied in future study designs to evalu-
ate on prediction performance. Moreover, our work contributes to new digital and objec-
tive biomarkers, which have the potential for disease stratification or disease monitoring
of PD patients to provide personalized care and treatment optimization.
Supplementary Materials: The following are available online at www.mdpi.com/xxx/s1, Figure S1:
title, Table S1: title, Video S1: title. Patient-Population Supplement. Machine-Learning Supplement.
Author Contributions: Conceptualization, J.V. and C.T.; methodology, J.V and C.T.; software, M.F.;
validation, J.V., C.T.; formal analysis, C.M.A and M.F.; investigation, J.V.; resources, C.T., T.W.; data
curation, C.M.A.; writing—original draft preparation, J.V.; writing—review and editing,
J.V.,C.M.A., M.F., T.W., C.T.; visualization, J.V. and C.M.A.; supervision, C.T.; project administra-
tion, J.V. and T.W.; funding acquisition, J.V. All authors have read and agreed to the published
version of the manuscript.
Funding: This work is funded by the Innovative Medical Research Fund (Innovative Medizinische
Forschung, I-VA111809) of the University of Münster..
Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki, and approved by the Ethics Committee of the University of Münster and
the physician's chamber of Westphalia-Lippe (Reference num-ber: 2018-328-f-S, Approval date: Sept
Informed Consent Statement: Informed consent was obtained from all subjects involved in the
Data Availability Statement: In this section, please provide details regarding where data support-
ing reported results can be found, including links to publicly archived datasets analyzed or gener-
ated during the study. Please refer to suggested Data Availability Statements in section “MDPI Re-
search Data Policies” at https://www.mdpi.com/ethics. You might choose to exclude this statement
if the study did not report any data.
Acknowledgments: In this section, you can acknowledge any support given which is not covered
by the author contribution or funding sections. This may include administrative and technical sup-
port, or donations in kind (e.g., materials used for experiments).
Conflicts of Interest: The authors declare no conflict of interest.
The appendix is an optional section that can contain details and data supplemental
to the main text—for example, explanations of experimental details that would disrupt
the flow of the main text but nonetheless remain crucial to understanding and reproduc-
ing the research shown; figures of replicates for experiments of which representative data
is shown in the main text can be added here if brief, or as Supplementary data. Mathemat-
ical proofs of results not central to the paper can be added as an appendix.
All appendix sections must be cited in the main text. In the appendices, Figures, Ta-
bles, etc. should be labeled starting with “A”—e.g., Figure A1, Figure A2, etc.
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