A Comprehensive Analysis of Parkinson’s Disease Detection Through

Inertial Signal Processing

Manuel Gil-Martín, Sergio Esteban-Romero, Fernando Fernández-Martínez

and Rubén San-Segundo

Grupo de Tecnología del Habla y Aprendizaje Automático (T.H.A.U. Group), Information Processing and

Telecommunications Center, E.T.S.I. de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain

Keywords: Parkinson’s Disease Detection, Inertial Signals, Fast Fourier Transform, Tremor Detection,

Convolutional Neural Networks, Window Size.

Abstract: When developing deep learning systems for Parkinson's Disease (PD) detection using inertial sensors, a

comprehensive analysis of some key factors, including data distribution, signal processing domain, number

of sensors, and analysis window size, is imperative to refine tremor detection methodologies. Leveraging

the PD-BioStampRC21 dataset with accelerometer recordings, our state-of-the-art deep learning architecture

extracts a PD biomarker. Applying Fast Fourier Transform (FFT) magnitude coefficients as a preprocessing

step improves PD detection in Leave-One-Subject-Out Cross-Validation (LOSO CV), achieving 66.90%

accuracy with a single sensor and 6.4-second windows, compared to 60.33% using raw samples. Integrating

information from all five sensors boosts performance to 75.10%. Window size analysis shows that 3.2-

second windows of FFT coefficients from all sensors outperform shorter or longer windows, with a

window-level accuracy of 80.49% and a user-level accuracy of 93.55% in a LOSO scenario.

1 INTRODUCTION

Research on biometrics has experienced notable

growth in recent years, witnessing a surge in various

applications, particularly in the field of healthcare.

The term healthcare biometrics is not only confined

to biometric applications for controlling access to

electronic medical records and patient identification

but also includes medical decision support tools for

patient care. These tools extract biomarkers that

define patient health and aid in illness detection,

medication response analysis, and the management

of chronic conditions such as Parkinson's Disease

(PD). PD is a neurodegenerative disorder

characterized by motor impairments like tremor,

bradykinesia, rigidity, and postural instability

(Jankovic, 2008). These impairments affect various

motor functions, including planning, programming,

sequencing, movement initiation, and execution.

Deep learning algorithms have being applied on

human motion recognition to model the evolution of

physical activities using wearables or cameras

(Manuel Gil-Martin, San-Segundo, Fernandez-

Martinez, & Ferreiros-Lopez, 2020, 2021; Gil-

Martín, San-Segundo, Fernández-Martínez, & de

Córdoba, 2020; Zhang et al., 2017). This way, the

tremor movement related to PD could be also model

by these technologies.

This work proposes a PD detection system based

on a deep learning architecture that allows analyzing

different important aspects to consider when using

tremor to distinguish between healthy people and PD

patients. The primary contributions of this research

are as follows:

 Analysis of the inertial signal domain for PD

detection.

 Assessment of different sensors to detect PD.

 Study of the window length on PD detection.

This paper is organized as follows. Section 2

reviews the literature of PD detection using inertial

sensors. Section 3 reviews the material and methods,

including a description of the dataset, the signal

processing, the deep neural network, and the

evaluation methodology. Section 4 describes the

experiments and the obtained results and section 5

summarizes the main conclusions of the paper.

462

Gil-Martín, M., Esteban-Romero, S., Fernández-Martínez, F. and San-Segundo, R.

A Comprehensive Analysis of Parkinson’s Disease Detection Through Inertial Signal Processing.

DOI: 10.5220/0012360100003636

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2024) - Volume 3, pages 462-469

ISBN: 978-989-758-680-4; ISSN: 2184-433X

2 RELATED WORKS

Many researchers have explored the use of machine

learning to detect motor symptoms of Parkinson's

disease using wearable sensors (Channa, Ifrim,

Popescu, & Popescu, 2021; Iakovakis et al., 2020;

Kubota, Chen, & Little, 2016). Some of these works

address the simultaneous detection of multiple

symptoms (Lang et al., 2019). Despite this

significant interest, there are still several aspects that

need improvement, such as overall accuracy in real-

world settings, the acquisition of clinically

significant metrics, and robust detection in patients

for whom there is no training data.

Regarding the extraction of features from inertial

signals, many feature sets have been proposed in the

literature for Parkinson's disease detection based on

tremor. The vast majority of these are based on

measurements in the time domain (such as mean,

range, or cross-correlation), in the frequency domain

(such as dominant frequency, energy content in a

particular band, or signal entropy) (Rigas et al.,

2012), or a combination of the two (Dai, Zhang, &

Lueth, 2015). Some authors have demonstrated that

features traditionally used for speech processing

(e.g., frequency analysis using the Mel scale,

cepstral coefficients) are also effective in classifying

human motion from accelerometer data (San-

Segundo, Manuel Montero, Barra-Chicote,

Fernandez, & Manuel Pardo, 2016; San-Segundo,

Navarro-Hellin, Torres-Sanchez, Hodgins, & De la

Torre, 2019; Vanrell, Milone, & Rufiner, 2018).

As for tremor classification or detection

algorithms, researchers have experimented with a

wide variety of machine learning algorithms, such as

decision trees (Garcia-Magarino, Medrano, Plaza, &

Olivan, 2016), random forests (Arora et al., 2015),

hidden Markov models (Rigas et al., 2012), and

neural networks (Cole, Roy, De Luca, Nawab, &

Ieee, 2010). For example a previous work (Hathaliya

et al., 2022) used a deep learning architecture to

model tremor obtaining a 92.4% of accuracy using

6.4-second windows of raw samples using a single

sensor on the left anterior forearm. However, the

data distribution used in this work seems to simulate

a too optimistic scenario since data from the same

subjects were included in both training and testing

subsets. In addition, there exists a lack of a deep

study of window sizes to select the most appropriate

one to predict the tremor.

This work proposes the use of a deep network for

both feature learning and tremor detection in a

realistic scenario and deeply analyse different

aspects that could affect the final performance, such

as the data distribution, the input signal domain, the

sensors used to feed the system and the size of the

analysis windows. Some of these aspects have been

analysed in activity recognition (Gil-Martín et al.,

2020) but not in PD detection.

3 MATERIALS AND METHODS

This section describes the dataset, the signal

processing, the deep neural network used for the PD

detection and the followed evaluation methodology.

3.1 Dataset

The PD-BioStampRC21 dataset (Adams et al., 2021)

includes tri-axial accelerometer obtained from five

wearable sensors, involving both PD and healthy

control participants. Lightweight MC 10 BioStamp

RC sensors were used to collect the data, with each

participant wearing five sensors attached to specific

body parts, including the chest, left anterior thigh,

right anterior thigh, left anterior forearm, and right

anterior forearm as observed in Figure 1. The

samples were obtained at a sampling rate of 31.25

Hz. Moreover, the dataset contains information

about the participants' medication status and the

Unified Parkinson’s Disease Rating Scale (UPDRS)

but they were not used in this work. The dataset

contains recordings from 34 subjects: 17 healthy

control and 17 PD participants. However, after

analysing the available dataset, it was found that

some sensors from control participants with IDs 007,

014, and 060 had missing data, so they were

removed from the study.

Figure 1: A study participant wearing the sensors at five

different locations on the chest and each limb (Adams et

al., 2021).

3.2 Signal Processing

In this work, two input formats of the inertial signals

were evaluated to feed a deep neural network: Raw

data and Fast Fourier Transform (FFT) magnitude

coefficients. As in the baseline system (Hathaliya et

al., 2022) we used 30,000 readings (16.13 minutes)

for each participant along with their status in order

A Comprehensive Analysis of Parkinson’s Disease Detection Through Inertial Signal Processing

463

to feed the classification system and analyse the

effect of particular aspects instead of using the

whole dataset (Igene, Alim, Imtiaz, & Schuckers,

2023).

First, we divided the recordings into overlapped

windows (a shift equivalent to half the window size

between consecutive windows). The system

classifies each window to healthy control or PD. All

the windows from each participant were labelled as

healthy control or PD depending on the participant’s

health status. In this work, we evaluated the

classification performance when considering

different window sizes of 0.8, 1.6, 3.2, 6.4, 12.8 and

16 seconds corresponding to 25, 50, 100, 200, 300

and 400 time samples, respectively.

Second, for each window size, we analysed both

time and frequency domain signals as inputs to a

deep neural network, considering two different

preprocessing steps depending on the signal domain.

In the first case, no preprocessing was done to the

original signal and the inputs to the deep neural

network were directly the time samples included in

each window (Raw data). In the second case, the

inputs were the coefficients of the FFT magnitude.

These coefficients were precomputed for each

analysis window and represented the spectrum from

0 Hz to half of the sampling frequency, 15.625 Hz

for the PD-BioStampRC21 dataset. As the energy in

tremor motion mostly concentrates in low

frequencies (M. Gil-Martin, Montero, & San-

Segundo, 2019), the obtained spectrogram could be

useful for the PD detection. This paper analyses and

compares both alternatives for the tremor modelling.

3.3 Deep Learning Architecture

The deep learning architecture used in this work was

a state-of-the-art Convolutional Neural Network

(CNN) composed of two main parts: a feature

learning subnet and a classification subnet. The first

subnet learnt features from the raw data or FFT

magnitude coefficients from the inertial signals

using two convolutional layers (32 kernels of (1, 5)

dimensions) and two max-pooling layers (kernels of

(1, 2) dimensions). The second subnet used fully

connected layers to classify the learned features as a

predicted class: healthy person (0) or PD patient (1).

The architecture included dropout layers (0.3) after

max-pooling and fully connected layers to avoid

overfitting during training. The last layer used a

SoftMax activation function to offer the predictions

of each class for every analysis frame, while

intermediate layers used ReLU for reducing the

impact of gradient vanishing effect. We used

categorical cross-entropy as loss metric and the

Adaptive Moment Estimation (Adam) optimizer,

which adaptively adjusts the learning rate

throughout training. We adjusted the epochs and

batch size of the deep learning structure to 30 and

100, respectively. Figure 2 represents the

architecture used in this work to model and classify

the analysis windows to healthy person or PD

patient. This architecture was implemented using the

Keras library and Python programming language.

As observed in the figure, the inputs of the CNN

were organized in a 2D matrix with N x M

dimensions. N corresponds to the number of input

signals: 3 when using a single sensor (X, Y and Z

signals) or 15 when using the five available sensors

in the dataset (3 x 5). M is the number of analyzed

samples from each sensor signal. This number

depends on the size of the analysis window and the

signal domain used in each experiment. M is equal

to the size of the analysis window when using raw

data as input data. Nevertheless, in the frequency

domain, M is the number of FFT coefficients

obtained from each window, and it is equal to the

half of the window size.

3.4 Evaluation Methodology

In this work, different data distributions have been

used to compare to a baseline system and highlight

the importance of correctly train and test a PD

detection system.

The first data distribution, called TrainTest,

consists of using an 80% of data for training and

20% of data for testing. This data distribution was

used by the baseline system (Hathaliya et al., 2022).

When randomly distribute overlapped windows, it is

possible to train and test the system with examples

that share information, which leads to a very

optimistic performance. In addition, examples from

the same subjects can be used for training and

testing. However, one of the main problems of this

data distribution is that the system is only evaluated

over a particular subset of the whole dataset.

To evaluate the system using the whole dataset,

it is possible to create a Cross-Validation (CV)

alternative for this data distribution: TrainTest_CV.

In this K-fold CV methodology, the given data are

divided into k groups or folds to train and test a

system with different data. This process is repeated

changing the training and testing folds and the

results are the average of the partial results obtained

for all repetitions.

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464

Figure 2: Convolutional Neural Network Architecture used in this work for PD detection where N denotes the number of

input signals and M denotes the number of samples for each analysis window or example.

However, to avoid recordings from the same

subjects in both training and testing subsets (a more

realistic scenario), we decided to consider a Leave-

One-Subject-Out (LOSO) CV, which is a specific

type of K-fold CV where the system is evaluated

with the data from one subject and is trained with

the data from the rest of the subjects. In this case, the

process is repeated several times leaving a different

subject for testing and the results are also the

average of the partial results obtained for all

repetitions. This methodology simulates a more

difficult and realistic scenario where the system is

evaluated with recordings from subjects different to

those used for training. Figure 3 shows examples for

the data distributions described above.

Figure 3: Data distributions for TrainTest, TrainTest_CV

and LOSO CV methodologies.

As evaluation metrics, we used accuracy, which

is defined as the ratio between the number of

correctly classified samples and the number of total

samples. This way, for a classification problem with

N testing examples and C classes, accuracy is

defined in Equation (1).

Accurac



P







(1)

Considering Ri as the sum of all examples in a

column of the confusion matrix, and Si as the sum of

all examples in a row, precision (Equation (2)),

recall (Equation (3)) and F1score (Equation (4))

metrics are defined as follows:

precision











(2)

recall











(3)

F1score2 

precision  recall

recision  recall

(4)

To show statistical significance values, we used

confidence intervals, which include plausible values

for a specific metric. We will assure that there exists

a significant difference between results of two

experiments when their confidence intervals do not

overlap. Equation (5) represents the computation of

confidence intervals attached to a specific metric

value and N samples for 95% confidence level.



95%



1.96



metric  100  metric 

(5)

In this work, we modelled the tremor at window-

level since the examples used to feed the deep neural

architecture were windows. However, we also

provided a performance at user-level, considering

the mode of the predictions for all the windows from

a subject as the user prediction. This way, it is

possible to integrate the information from all the

windows in a single prediction, which is useful from

a comprehensive medical perspective. This approach

provides overall health trends instead of focusing

exclusively on the presence or absence of tremors

during brief time intervals that could potentially lead

to incomplete or incorrect assessments.

4 RESULTS AND DISCUSSION

This section contains details about the experiments

performed in this work, including results and

discussion about the data distribution, the signal

A Comprehensive Analysis of Parkinson’s Disease Detection Through Inertial Signal Processing

465

domain, the sensors used to feed the system and the

window size of the examples.

4.1 Data Distribution

The first experiments that we performed consists of

using only the three signals from the left anterior

forearm and a window size of 6.4 seconds (200

samples) in order to compare to the baseline system

(Hathaliya et al., 2022). This previous work only

states that they split the dataset into training (80%)

and test (20%). They did not specify any aspect of

considering the subject distributions to avoid mixing

data from the same subject in training and testing

subsets and did not mention any CV approach. This

data distribution (TrainTest) leads to a very

optimistic scenario where the system is trained and

tested with examples from the same subjects that

could share information since the windows are

overlapped. This previous work obtained a 92.4% of

accuracy. Simulating this scenario setup, our

proposed system could easily reach the maximum

performance (100% of accuracy) because the

isolated experiment results would depend on the

final testing examples. In order to obtain a more

general performance evaluating the whole dataset, a

CV approach of this scenario (TrainTest_CV)

obtained 72.42 ± 0.91 % test accuracy.

Despite of this experiment, we considered a more

realistic approach through a LOSO CV. With this

scenario, the system obtained a test accuracy of

60.33 ± 1.0 %.

Table 1 summarizes the results for the different

CV data distributions using Raw data 6.4-second

windows. These approaches evaluated the same

number of examples but simulate very different

scenarios, where LOSO CV approach is a more

realistic scenario because data from testing subjects

were not included in the training process. For this

reason, the performance of the LOSO CV approach

decreased compared to the rest experiments.

To simulate a more realistic scenario, we decided

to keep the LOSO CV approach for the rest of

experiments of this study.

4.2 Signal Domain Analysis

Regarding the signal domain of the inputs, we

decided to compare Raw data windows against using

the FFT magnitude coefficients. Figure 4 shows a

comparison of performance at window-level when

using Raw data (60.33 ± 1.0 %) and FFT data (66.90

± 0.96 %) of 6.4-second windows and left anterior

forearm sensor. We observed a significant increment

of performance when using signals in the frequency

domain.

Table 1: Evaluation metrics for different CV data

distributions using Raw data 6.4-second windows and left

anterior forearm sensor.

Data distribution

Test Accuracy

(

)

Test F1-score

(

)

TrainTest

(Hathaliya et al.,

2022

)

92.40 -

TrainTest 100.00 100.00

TrainTest_CV 72.42 ± 0.91 71.56 ± 0.92

LOSO CV 60.33 ± 1.00 59.20 ± 1.00

Figure 4: Accuracy at window-level using 6.4-second

windows and left anterior forearm sensor depending on the

input signal domain.

One of the possible reasons of this increment

could be that PD tremor becomes more visible in the

frequency domain: information of energy

corresponding to the tremor frequency (between 3–9

Hz (Deuschl, Fietzek, Klebe, & Volkmann, 2003;

M. Gil-Martin et al., 2019)) and its harmonics can be

seen in the spectrum of the X, Y and Z signals of the

inertial sensor. This way, the use of a CNN with

FFT magnitude coefficients as inputs allowed

obtaining better results compared to using raw data

samples directly.

4.3 Sensors Analysis

Since the available dataset provides information

from several sensors distributed over different

locations in the body, we decided to analyse which

sensor provide more valuable information regarding

the tremor motion and combine the information from

all of them. Figure 5 shows the accuracy at window-

level using 6.4-second windows depending on the

input signal domain (Raw or FFT) and sensors used

to feed the deep learning architecture.

It is possible to observe that for all the systems

(using a single sensor or all sensors together), using

the FFT approach provides a significant

improvement compared to directly using the raw

samples. In addition, we observed that the systems

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466

Figure 5: Accuracy at window-level using 6.4-second

windows depending on the input signal domain and

sensors.

using only the chest, left anterior forearm and or

right anterior forearm sensors and the system using

all the sensors together offer similar performance

when using raw data (e.g., 60.60 ± 0.99 % test

accuracy with the chest sensor). This means that the

motion symptoms are more noticeable in the chest

and upper limbs. Moreover, when analysing the FFT

signal experiments, it is possible to observe that the

chest is the single sensor that offers better

performance (71.57 ± 0.92 %). However, using all

the sensors provides a significant improvement

compared to the rest of experiments (75.10 ± 0.88

%). Then, the CNN architecture is capable to

integrate the information from the different sensors

and learn more meaningful features to model the

tremor.

For these reasons, we decided to use all the

sensors for the rest of experiments of this study.

4.4 Window Size Analysis

To study the effect of the window size over the PD

detection, we used windows of 0.8, 1.6, 3.2, 6.4,

12.8 and 16 seconds in time and frequency domain.

As observed in Figure 6 and Figure 7, the binary

classification performance showed improvement

with an increase in the duration of the analysis

window from 0.8 seconds to 3.2 seconds. However,

it decreased after 3.2 seconds. When using long

windows in a PD detection system that relies on

deep learning algorithms, the performance tends to

either saturate or decline. This occurrence could be

explained based on two factors (Manuel Gil-Martin

et al., 2021). Firstly, the increase in the window size

raises the number of parameters that require training

in the deep learning architecture. This aspect could

affect the final performance, especially when the

dataset has a limited number of examples for

training. Secondly, long windows raise the risk of

overfitting. For example, the application of the FFT

on lengthy windows increments the frequency

resolution, leading each hertz to be represented with

a larger number of data points. This resolution

escalates the vulnerability to overfitting and

undermines the robustness in a LOSO CV scenario.

Consequently, generalizing the trained model for the

evaluation of data from unseen subjects becomes

challenging. In addition to these two factors, in the

PD detection case study it is important to select an

appropriate window size because long windows

could mix tremor events with motion not associated

to PD. When increasing the window size, the

analysis windows could include most motion

without PD glimpses. This could disturb the

modelling process because the long windows could

smooth the tremor peak and they could be classified

as control. Moreover, as observed before for the 6.4-

second windows analysis, the deep study through

different window size confirms that the FFT

coefficients provide significant higher performance

compared to the Raw data for all the windows at

window-level (Figure 6). Regarding, user-level

classification (Figure 7), since LOSO methodology

reduces the number of examples to the number of

users, the confidence intervals are higher in this

case, but there still exists a tendency of the

improvement provided by the FFT. Even in this

Figure 6: Window-level PD classification accuracy using

all sensors depending on the window size and input signal

domain.

Figure 7: User-level PD classification accuracy using all

sensors depending on the window size and input signal

domain.

55.00

65.00

75.00

85.00

0.8 1.6 3.2 6.4 9.6 12.8 16

Accuracy (%)

Window Size (s)

Raw FFT

35.00

55.00

75.00

95.00

0.8 1.6 3.2 6.4 9.6 12.8 16

Accuracy (%)

Window Size (s)

Raw FFT

A Comprehensive Analysis of Parkinson’s Disease Detection Through Inertial Signal Processing

467

case, we observed that for 3.2-second windows,

there exist significant difference between using Raw

data and FFT. This FFT approach using windows of

3.2 seconds from all sensors reaches a window-level

accuracy of 80.49 ± 0.57 % and a user-level

accuracy of 93.55 ± 8.65 % in a LOSO scenario.

5 CONCLUSIONS

A comprehensive analysis is required when

developing a deep learning system focused on PD

detection using inertial sensors in order to highlight

key factors to the refinement of tremor detection.

This work uses the PD-BioStampRC21 dataset

including healthy control and PD participants

wearing five inertial sensors to perform a

comprehensive study.

Ensuring an appropriate distribution of data is

crucial in PD detection to prevent data overlap

between training and testing subsets. The LOSO CV

technique emerges as a robust solution, effectively

mitigating the risk of data contamination and

enhancing the model generalizability.

The use of FFT magnitude coefficients, in

contrast to raw data samples, become helpful in

detecting PD, particularly due to the pronounced

visibility of tremor characteristics in the frequency

domain. We obtained a significant improvement of

performance when using FFT data (66.90 ± 0.96 %)

of 6.4-second windows and left anterior forearm

sensor compared to using directly Raw data (60.33 ±

1.0 %).

Incorporating multiple sensors located on the

chest and limbs in CNN architecture capable of

combining data exhibits the potential to increment

the overall PD detection performance (75.10 ± 0.88

% when using FFT 6.4-second windows).

An in-depth exploration of the optimal analysis

window size is imperative in enhancing the

performance of both window-level and user-level

evaluations. We observed that using 3.2-second

analysis windows provides a positive balance

between capturing intricate temporal patterns and

preventing mixing tremor events with motion not

associated to PD. This window size provided a

window-level accuracy of 80.49 ± 0.57 % and a

user-level accuracy of 93.55 ± 8.65 % in a LOSO

scenario using the frequency domain of the input

signals from all the sensors available in the dataset.

As future work, it would be possible to refine the

data analysis. Specifically, the selection of windows

with higher energy levels could aid in identifying

instances when tremors are more pronounced,

thereby improving the performance of PD detection.

In addition, the development of a robust regression

system capable of accurately estimating UPDRS

scores could offer valuable insights into disease

progression and facilitate more precise monitoring

of patients' motor symptoms. Moreover, it could be

possible to investigate the optimal duration for data

collection, beyond the current 16.13 minutes used in

this work, and study the effect of the posture while

collecting tremor data. Integrating these

advancements into the proposed system holds

substantial promise in advancing the field and

contributing to the development of more effective

diagnostic and monitoring tools for PD.

ACKNOWLEDGEMENTS

The work was supported by the project “TremorDetect -

Detección de la enfermedad de Parkinson a través de

señales inerciales”, funded by “Primeros Proyectos” call

from ETSIT, UPM, by projects AMIC-PoC (PDC2021-

120846-C42), GOMINOLA (PID2020-118112RB-C21

and PID2020-118112RB-C22) and BeWord (PID2021-

126061OB-C43), supported by the Spanish Ministry of

Science and Innovation (MCIN/AEI/10.13039/501

100011033) and by the European Union

“NextGenerationEU/PRTR”, and ASTOUND (101071191

HORIZON-EIC-2021-PATHFINDERCHALLENGES-01)

funded by the European Commission.

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