From Controlled to Free-Living Contexts: Expanding the Monitoring

of Motor Symptoms in Parkinson’s Disease with Wearable mHealth

Technologies

María Centeno-Cerrato

, Carlos Polvorinos-Fernández

, Luis Sigcha

Guillermo de Arcas

, César Asensio

, Juan Manuel López

and Ignacio Pavón

Instrumentation and Applied Acoustics Research Group, Mechanical Engineering Department,

ETS Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid, Spain

Department of Physical Education and Sports Science, Health Research Institute, & Data-Driven

Computer Engineering (D2iCE) Group, University of Limerick, Limerick, Ireland

Instrumentation and Applied Acoustics Research Group, Department of Audiovisual Engineering and Communications,

ETS. de Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain

Instrumentation and Applied Acoustics Research Group, Department of Physical Electronics, Electrical Engineering and

Applied Physics, ETS. de Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain

Keywords: Wearables, Supervised Monitoring, Free-Living Monitoring,

Accelerometer, Gyroscope.

Abstract: This study examines the application of wearable mobile health (mHealth) technologies, specifically

smartwatches equipped with inertial sensors, for the monitoring of Parkinson’s disease (PD). The aim is to

investigate how the integration of the Monipar tool, designed to monitor supervised exercises, with the

BioCliTe system, which continuously collects data during free-living activities, can improve the assessment

of motor fluctuations and disease progression. The study proposes a set of free-living activities which can

serve as characteristic indicators for assessing motor symptoms. By combining structured exercises with

everyday tasks, this approach provides a more comprehensive evaluation of PD, capturing motor symptoms

in both controlled and real-world environments. The research seeks to advance disease monitoring and patient

care through more accurate tracking and the development of personalized treatment strategies.

1 INTRODUCTION

Parkinson's disease (PD) is a chronic, progressive

neurological disorder caused by the loss of

dopaminergic neurons, resulting in a significant

reduction in the production of dopamine —a key

neurotransmitter involved in the regulation of

movement and motor control. PD manifests through

a wide range of symptoms, categorized into two main

groups: motor symptoms (e.g., resting tremor,

bradykinesia, muscle rigidity, postural and gait

disturbances or dyskinesias) and non-motor

https://orcid.org/0009-0007-0113-3007

https://orcid.org/0000-0002-4594-9477

https://orcid.org/0000-0002-9968-5024

https://orcid.org/0000-0003-1699-7389

https://orcid.org/0000-0003-3265-3244

https://orcid.org/0000-0001-7847-8707

https://orcid.org/0000-0003-0970-0452

symptoms (e.g., sleep disorders, depression,

cognitive impairment, and dementia in advanced

stages). Despite advances in research, PD remains

incurable, and its progression is inevitable.

(Armstrong & Okun, 2020).

The most used scale to measure PD progression is

the Movement Disorder Society-Sponsored Revision

of the Unified Parkinson's Disease Rating Scale

(MDS-UPDRS), which evaluates symptoms and

mental health through questionnaires and clinician-

scored tests (Goetz et al., 2008).

Centeno-Cerrato, M., Polvorinos-Fernández, C., Sigcha, L., de Arcas, G., Asensio, C., López, J. M. and Pavón, I.

From Controlled to Free-Living Contexts: Expanding the Monitoring of Motor Symptoms in Parkinson’s Disease with Wearable mHealth Technologies.

DOI: 10.5220/0013386000003911

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

In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2025) - Volume 2: HEALTHINF, pages 1037-1044

ISBN: 978-989-758-731-3; ISSN: 2184-4305

1037

The clinical management of PD presents a

significant challenge due to the fluctuating nature of

its symptoms. Medical consultations are commonly

scheduled at intervals of six to twelve months,

resulting in long gaps without detailed patient

evaluation and treatment adjustment. Consequently,

many patients can experience a deterioration of

symptoms before their next clinical appointment

(Rodríguez-Martín et al., 2022). There is a growing

need for objective and continuous monitoring

systems to assess disease progression and refine

treatment strategies. This has led to the integration of

technological tools aimed at improving both short-

and long-term monitoring, as well as optimizing

overall disease management.

Mobile health (mHealth) technologies and

wearable devices allow continuous, accurate data

collection in a simple manner without causing

discomfort to the user. These devices, often equipped

with sensors (typically inertial or bioelectrical),

enhance monitoring quality while offering patients an

accessible and convenient solution (Polvorinos-

Fernández et al., 2024).

It is essential to ensure these sensors reliably operate

within the relevant amplitude and frequency ranges

for accurate treatment evaluation of the patients (Ru

et al., 2022).

To obtain a more comprehensive and objective

view, it is important to collect data from patients in a

wide range of environments: during the performance

of pre-defined guided exercises and during the

execution of activities of daily living.

Furthermore,

these data can be used to develop digital biomarkers

that facilitate the quantification of patients' motor

status (Polvorinos-Fernández et al., 2024).

These biomarkers could play a crucial role in

personalizing treatment and improving patients'

quality of life (Mahadevan et al., 2020). The

acquisition of data under both laboratory and free-

living conditions is crucial for evaluating the validity

and utility of novel biomarkers. Controlled laboratory

environments enable the generation of robust and

reliable biomarkers, whereas data collected in free-

living settings are indispensable for determining

whether these biomarkers retain their reliability and

relevance in real-world contexts.

This study explores the use of inertial sensors to

monitor patients with PD. All smartwatches

employed for the measurements conducted in the

study are equipped with an LSM6DS0 sensor, which

incorporates a triaxial accelerometer and a triaxial

gyroscope. The sampling frequency was configured

in every device at 50 Hz.

The precise number and placement of sensors on

the body remain subjects of debate; however, it is

generally recommended to minimize the number of

sensors to optimize usability and comfort, while

ensuring the integrity of the data (Monje et al., 2019).

In accordance with this, a decision was made to

sacrifice some data quantity to enhance usability by

placing the smartwatch on only one hand, specifically

on the wrist most affected by symptoms. This

approach ensures the capture of data related to motor

performance and physical activity, while prioritizing

user comfort and ease of use.

The study highlights the importance of collecting

movement data in both supervised and unsupervised

contexts.

For this purpose, the mHealth tool BioCliTe

(Digital Biomarkers for Motor Status Assessment of

Parkinson's Disease Patients for Clinical and

Therapeutic Application) was used to continuously

capture motion signals during free-living activities

and guided exercises. The guided exercises were

performed using the Monipar tool (Sigcha et al.,

2023), which provides instructions via a smartphone,

while the smartwatch synchronously records data

from the embedded inertial sensors.

2 MONITORING GUIDED

ACTIVITIES: Monipar

Monipar is a technological solution developed as part

of the TECAPARK project (TECAPARK),

specifically designed to monitor the execution of

specific guided activities selected from the MDS-

UPDRS. These activities are intended to be

performed in supervised contexts, ensuring controlled

and accurate execution.

Monipar consists of two modules: a smartphone

application that guides the user, and a wearable

module in the form of a smartwatch, designed for

real-time data collection. The mobile application,

which uses an interactive interface, provides

comprehensive guidance through both visual

elements and audio prompts. This approach ensures

that users execute the exercises accurately, reducing

performance variability.

In addition, the application transmits activation

and status data from the smartphone to the

smartwatch, facilitating the automatic labelling of the

recorded signals. Simultaneously, the smartwatch

collects data via the integrated inertial sensors during

each exercise, which is then stored in the device's

local database. (Sigcha et al., 2023).

Monipar is structured around a set of exercises

based on Part III of the MDS-UPDRS scale (Goetz et

al., 2008), which assess different aspects of motor

function. These exercises are strategically sequenced

to ensure a comprehensive assessment (Sigcha et al.,

2023). Figure 1 illustrates the instructions displayed

in Monipar's exercise routine.

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1038

Figure 1: Visual Instructions for Monipar's exercise routine.

 Exercise 1, Resting Tremor Assessment (Item

3.17), quantifies the amplitude of tremor when

the limbs are at rest.

 Exercise 2, Postural Tremor Assessment (Item

3.15), assesses the presence of tremor when the

patient maintains a fixed posture.

 Exercise 3, Repetitive Arm Extension

Movement. To complement the items of the

MDS-UPDRS scale, an additional exercise was

incorporated, involving repeated forward arm

extensions and bringing the hands to the chest.

 Exercise 4, Finger Tapping (Thumb-Index)

(Item 3.4), assesses bradykinesia and fine

motor coordination by repetitively tapping the

thumb and index finger.

 Exercise 5, Rapid Hand Movements (Item 3.5),

measures the patient’s ability to perform rapid,

repetitive hand movements, providing data on

motor agility and bradykinesia.

 Exercise 6, Upper Limb Pronation-Supination

(Item 3.6), assesses bradykinesia and motor

symptoms, by measuring the pronation and

supination movements of the hands.

 Exercise 7, Rising from a Chair (Item 3.9),

assesses the patient’s ability to rise from a

seated position, reflecting postural control.

 Exercise 8, Gait Analysis (Item 3.10), assesses

gait quality, enabling the identification of

typical symptoms, such as freezing of gait.

Strategically timed rest periods were integrated

between each exercise, ensuring optimal performance

and minimizing the impact of fatigue on the data

collected. The Monipar protocol has a total duration

of 8 minutes. The duration of each exercise within the

protocol varies, depending on factors such as the

nature of the activity, its level of difficulty, and other

relevant considerations, all aimed at achieving the

most effective results for each specific task.

Figure 2: Example of an accelerometer signal recorded by

Monipar, including labelled data. The upper section shows

the temporal signal obtained from the three axes (time vs

amplitude), while the lower section shows the scalogram of

the combined signal from all three axes (time vs frequency)

(Sigcha et al., 2023).

3 TRANSITION FROM Monipar

TO BioCLiTe

Currently, Monipar provides valuable data on PD

motor status in controlled, supervised environments.

However, to ensure a comprehensive and precise

evaluation of the disease progression and treatment

efficacy, monitoring guided exercises must be

combined with monitoring free-living activities,

which do not require supervision. This integration

offers several significant advantages:

 More Realistic Representation of Motor

Status. Guided exercises in controlled

environments, such as those performed with

Monipar, allow for the evaluation of specific

From Controlled to Free-Living Contexts: Expanding the Monitoring of Motor Symptoms in Parkinson’s Disease with Wearable mHealth

Technologies

1039

movements under standardized conditions,

which is useful for measuring specific

parameters of motor function. However, these

exercises do not always reflect the demands

and variations encountered in daily life. Free-

living activities, such as walking, eating, or

dressing, provide a more realistic perspective

on how the disease affects functionality in

everyday contexts. The integration of both

types of monitoring can provide a holistic

assessment of motor function, capturing both

the accuracy of movements in defined

exercises and the motor performance in real-

life situations.

 Detection of Motor Fluctuations and "Off"

Periods. PD is characterized by motor

fluctuations, particularly in advanced stages,

where patients experience "on" periods (with

good motor control) and "off" periods (with

significant impairment). Continuous

monitoring during free-living activities permits

the tracking of these fluctuations, information

that may not be apparent during guided

exercises. This allows for a more accurate

assessment of motor function, facilitating

better adjustment to medication and other

treatments (Mantri et al., 2021).

 More Comprehensive Data for Longitudinal

Analysis. Collecting data in different contexts

(guided and free-living) provides a richer and

more diverse dataset for longitudinal analysis.

This allows the observation of long-term

patterns, a more detailed assessment of disease

progression, and the effectiveness of

treatments. In addition, the diversity of data can

help to develop more robust predictive models

regarding disease progression.

In summary, the combination of guided exercises

and free-living activities in the monitoring of PD

patients provides a more comprehensive, accurate and

personalized view of their motor status.

Consequently, there is a need to move from Monipar

(monitoring under controlled conditions) to BioCliTe

(monitoring also under free-living conditions).

4 MONITORING OF

FREE-LIVING AND GUIDED

ACTIVITIES: BioCLiTe

BioCliTe provides a technological solution that

evolves from the Monipar tool, adapting and

extending its functionalities to increase both the scope

and accuracy of monitoring in contexts beyond the

clinical environment.

While Monipar was limited to recording data

exclusively during guided exercises in a controlled

environment, BioCliTe provides continuous

movement data collection, extending monitoring

throughout the entire day (without the need for

supervision). This approach facilitates the collection

of movement data while patients are performing tasks

that reflect their daily routines.

In addition to providing data from the

accelerometer and gyroscope, BioCliTe also supports

the real-time tracking of the participant’s physical

activity, including total steps, walking steps, running

steps, speed, distance, and calories burned.

BioCliTe not only focuses on monitoring free-

living activities but also preserves the core

functionality of Monipar by integrating guided

exercises into daily tasks. This allows patients to

carry out these exercises autonomously in their home

environment, without the need for direct supervision.

Previously, the Monipar mobile application

activated the smartwatch to initiate activity

measurement; however, due to the transition to

BioCliTe, this functionality has been updated. The

mobile application now records the time intervals

during which activities are performed, generating a

time-stamped data file that tracks the signal's status

throughout the activity, enabling the automatic

labelling of movement data captured by the

smartwatch.

The battery life of the smartwatch is a limitation

to consider. Each smartwatch has a different battery

capacity, making it necessary to evaluate how many

hours of continuous data recording it can support.

According to the studies conducted, the commercial

smartwatches used in the experiments are capable of

measuring acceleration and angular velocity for up to

five consecutive hours. During the remaining hours

of the day, the smartwatch only recorded data related

to physical activity.

The system was designed to allow flexibility in

the initiation and termination of measurements taken

through the accelerometer and gyroscope, adapting to

the individual needs of the patient. The recording

period can be adjusted according to the patient's

schedule, allowing guided activities to be performed

at the most convenient times.

In Figure 3, acceleration data (recorded over a

period longer than 1 hour) shows clear regions where

tremor is present. This extended monitoring period

allows for a more comprehensive assessment

compared to the previous functionality of Monipar,

which only evaluated movement during guided

exercises. In addition to the Monipar exercises, the

participant was asked to perform a free-living activity

(cooking – beating a mixture).

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Figure 3: Example of an accelerometer signal recorded by

BioCliTe, including labelled data.

4.1 Proposed Free-Living Activities

Certain routine activities performed in daily living

have been recognized as effective in the monitoring

process, helping to characterize PD motor symptoms,

such as tremor and bradykinesia.

Due to the current limitations in recognizing

patterns associated with each specific free-living

activity, the first stage is to manually label these

exercises. To accomplish this, it is proposed to

develop a mobile application like Monipar, where the

user is guided through the task using visual and/or

auditory instructions, while also marking the precise

start and end points of each activity.

The data files would be stored locally on the

mobile device, and by cross-referencing this

information with the data recorded by the

smartwatch, it will be possible to identify which

sections of the signal correspond to each specific

activity. After having gained a thorough

understanding of the patterns and characteristics

associated with the signals from each proposed free-

living activity (using artificial intelligence

techniques) the objective is to eliminate the need for

manual labelling. In this context, the artificial

intelligence system will autonomously recognize the

activity occurring at any given moment.

Several daily activities have been identified as

suitable for monitoring motor symptoms, including

walking, standing up and sitting down, writing or

drawing, cooking, typing and brushing teeth.

A series of controlled laboratory experiments

were conducted, involving 20 healthy participants, to

thoroughly evaluate each of the proposed activities.

During these tests, participants performed the tasks

while their movements were tracked by the inertial

sensors embedded in a smartwatch. The data

collected allowed an in-depth analysis of how these

activities could function as reliable indicators of

motor symptom progression when monitored over

extended periods of time, in environments where

constant supervision is not required.

It is noteworthy that the experiments involving

patients with PD have not yet started, and the current

findings provide a preliminary foundation for

subsequent research involving PD individuals.

4.1.1 Standing up and Sitting down

Inertial sensors can capture the duration and fluidity

of transitions from a seated to a standing position,

providing valuable data to assess the impact of PD on

functional mobility in daily activities. Figure 4

illustrates a significant amplitude increase in

accelerometer and gyroscope signals corresponding

to the participant's standing up activity.

Figure 4: Signal recorded during the activity of standing up

(healthy participant). Upper panels show the time-domain

signals; lower panels the scalograms; left column

corresponds to accelerometer data; right column to

gyroscope data.

From Controlled to Free-Living Contexts: Expanding the Monitoring of Motor Symptoms in Parkinson’s Disease with Wearable mHealth

Technologies

1041

4.1.2 Gait

It is recommended to identify periods when the

participant is walking during free-living activities for

having observation in unsupervised environments.

There is no need to develop a new tool to manually

label walking events, as this can be achieved by cross-

referencing physical activity data (steps or speed)

with information from the accelerometer and

gyroscope. Gait is a key indicator of PD progression,

as difficulties such as "freezing of gait" can occur

outside of controlled environments. Measurements

taken during everyday activities like walking indoors

or outdoors allow for the detection of subtle changes

in mobility (Figure 5) (Borzì et al., 2023)

(Polvorinos-Fernández et al., 2024).

Figure 5: Signal recorded during the activity of gait (healthy

participant).

4.1.3 Writing or Drawing

Activities related to writing and drawing, particularly

the task of tracing a spiral, are common methods for

assessing motor symptoms in patients with PD. These

daily living tasks, which require continuous and fluid

movements of the hands and wrists, are effective for

detecting tremors, which appear as irregular or

discontinuous strokes. Inertial sensors placed on the

wrist can record deviations in acceleration and

angular velocity, providing accurate data on the

presence of tremors (Figure 6). Furthermore, these

activities are well-suited for assessing bradykinesia

symptoms, characterized by a progressive reduction

in the size of letters or strokes when drawing. With

these inertial sensors, it could be possible to capture

the decrease in movement amplitude and the

progressive slowing of motion of the upper limbs of

the patients (Thomas Kollamkulam, 2017).

Figure 6: Signal recorded during the activity of drawing a

spiral (healthy participant).

4.1.4 Brushing Teeth

Tooth brushing is a common daily activity that

involves repetitive and structured movements. PD

tremors may clearly manifest while holding the

toothbrush, leading to irregular oscillations that can

be detected by inertial sensors as fluctuations in

acceleration and angular velocity. In addition,

bradykinesia becomes evident in the reduced speed of

tooth brushing, reflecting a decrease in the amplitude

and speed of repetitive movements (Figure 7).

Figure 7: Signal recorded during the activity of brushing

teeth (healthy participant).

4.1.5 Cooking

Cooking activities present a valuable framework for

assessing motor function in people with PD because

they replicate the complex, coordinated movements

required in daily life. Routine tasks such as beating a

mixture, stirring a pot or a cup of coffee, or cutting

WHC 2025 - Special Session on Wearable HealthCare

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and chopping ingredients, involve rapid, repetitive,

and synchronized movements of the arm and wrist

(Figure 8). These tasks require controlled movements

and precise regulation of force, making them

particularly well-suited for assessing motor

impairments (tremor, bradykinesia and rigidity).

Figure 8: Signal recorded during the activity of beating a

mixture (healthy participant).

4.1.6 Typing

Patients with PD often experience motor symptoms

when typing, which can affect both the accuracy and

speed of their keystrokes. By analysing the variations

in hand movement, the sensors detect patterns

indicative of bradykinesia, such as reduced

movement amplitude or slower motion, as well as

tremor-related irregularities, like involuntary,

rhythmic motions. The changes in acceleration and

angular velocity can be valuable in assessing the

severity and progression of PD symptoms (Figure 9).

Figure 9: Signal recorded during the activity of typing

(healthy participant).

5 CONCLUSIONS

This study highlights the critical role of mHealth

technologies, such as Monipar and BioCliTe, in

improving the assessment of PD. Using wearable

devices, specifically smartwatches, can allow

accurate, continuous, and non-invasive monitoring of

motor symptoms, contributing to a deeper

understanding of the disease’s progression.

Monipar provides a structured framework for the

guided execution of standardized exercises derived

from the MDS-UPDRS scale, enabling controlled

assessments in supervised settings. Building on this

foundation, BioCliTe extends the monitoring

capabilities to free-living activities, allowing for

continuous data collection in unsupervised, real-

world environments. This dual approach—combining

guided exercises with daily activity monitoring—

provides a comprehensive assessment of motor

function, potentially bridging the gap between

clinical assessments and the challenges patients face

in their everyday lives.

To improve the understanding of motor

impairments in unsupervised settings, several free-

living activities have been proposed for analysis,

including gait, standing up and sitting down, writing

or drawing, cooking, typing, and brushing teeth.

These activities replicate hand movements commonly

performed in everyday life, allowing for the detection

of tremor, bradykinesia, rigidity, and other motor

symptoms under real life conditions (Polvorinos-

Fernández, Sigcha, Pablo, et al., 2024). Incorporating

data from these activities complements traditional

guided exercises and enriches the dataset available for

clinical evaluation. However, challenges remain in

accurately recognizing patterns of free-living

activities and ensuring reliable classification using

smartwatch data alone. To perform automatic

labeling effectively, a large and robust database is

required. Additionally, the complexity of monitoring

a wide range of daily activities necessitates the

development of advanced analytical tools, including

machine learning and data fusion techniques, to

improve activity recognition and symptom detection.

One of the key limitations of this study is the

absence of PD patients in the trial sample, which

restricts the ability to directly evaluate the

effectiveness of the technologies in monitoring PD-

specific motor symptoms, particularly in relation to

the proposed activities. Another limitation is the

reliance on smartwatches for data collection, as

factors such as device calibration, user behaviour, and

environmental conditions can introduce variability

and affect the accuracy of the measurements.

Additionally, the battery life of the smartwatch may

impact the continuous monitoring of motor

From Controlled to Free-Living Contexts: Expanding the Monitoring of Motor Symptoms in Parkinson’s Disease with Wearable mHealth

Technologies

1043

symptoms, which could influence the completeness

of the data collected throughout the day. These factors

must be considered in future studies to improve the

generalizability and applicability of the findings to

the PD population.

The integration of wearable devices into these

mHealth solutions can offer significant advantages,

including real-time detection of motor fluctuations,

improved tracking of disease progression, and more

personalized treatment strategies. These technologies

represent a transformative step in PD management,

providing clinicians with detailed, patient-specific

insights. Future research will focus on optimizing

data analysis algorithms to enhance the accuracy and

reliability of symptom detection in diverse real-world

scenarios.

ACKNOWLEDGEMENTS

This paper is part of the BIOCLITE research project

PID2021-123708OB-I00, funded by MCIN/AEI/

10.13039/501100011033/FEDER, EU.

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