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Advanced Wearable Sensors and Other Sensing Technologies for Diagnosis and Treatment of Parkinson's Disease and Movement Disorders

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 6125

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Special Issue Editors

Dr. Massimo Marano

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Guest Editor

Neurology, Neurophysiology, Neurobiology and Psychiatry Unit, Fondazione Policlinico Universitario Campus Bio-Medico, Viale Alvaro del Portillo 200, 00128 Rome, Italy
Interests: movement disorders; Parkinson’s disease and parkinsonism; dystonia; tremor; Huntington’s disease; botulinum toxin; remote patient monitoring; gait analysis; deep brain stimulation; neurophysiology

Prof. Dr. Antonio Suppa

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Guest Editor

Department of Human Neuroscience, Sapienza University of Rome, 00185 Rome, Italy
Interests: pathophysiology of motor symptoms; Parkinson's disease (PD); human movement disorders; wireless and wearable technology; inertial measurement units (IMUs); early diagnosis; treatment of PD patients
Special Issues, Collections and Topics in MDPI journals

Dr. Luca Marsili

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Guest Editor

Department of Neurology, James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, USA
Interests: movement disorders; Parkinson's disease; atypical parkinsonism; genetics; tremor
Special Issues, Collections and Topics in MDPI journals

Dr. Andrea Pilotto

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Guest Editor

Dipartimento di Scienze Cliniche e Sperimentali, University of Brescia, 25121 Brescia, Italy
Interests: digital markers of neurological disease; gait and movement mobile health technologies; optic sensors; cognitive digital assessment; interfaces between sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Parkinson's disease is a degenerative neurological disorder that affects millions of people worldwide, posing significant challenges to healthcare systems and the quality of life for patients and caregivers. Given its high prevalence and the heterogeneity of its clinical motor and non-motor manifestations, Parkinson’s disease is a model for the study of other related movement disorders such as atypical parkinsonism, Huntington’s disease and other forms of chorea, degenerative and inheritable ataxia, and dystonia. The integration of advanced wearable sensors and biosensors into the management of Parkinson's disease and movement disorders marks a paradigm shift from traditional methods to more personalized, accurate, and early detection strategies, which also aim to improve the outcome of clinical trials.

This Special Issue will address the latest advancements in sensing technologies that aid in the diagnosis, monitoring, and treatment of Parkinson's disease and related movement disorders. We welcome original research papers or pilot studies on innovative methodologies focused on sensing technologies ranging from wearable devices to biosensors used to detect changes in motor and non-motor functions and other clinical or biological measures associated with Parkinson’s disease and related movement disorders. These technologies facilitate the timely detection, monitoring, and assessment of symptoms in routine care or in clinical trials, enabling healthcare professionals to design or prescribe personalized treatment plans tailored to each patient's needs.

Dr. Massimo Marano
Prof. Dr. Antonio Suppa
Dr. Luca Marsili
Dr. Andrea Pilotto
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Parkinson’s disease
movement disorders
parkinsonism
dystonia
ataxia
chorea
biosensors
wearable sensors
gait analysis
remote patient monitoring

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Published Papers (3 papers)

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Research

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14 pages, 1518 KiB

Open AccessArticle

Quantifying Changes in Dexterity as a Result of Piano Training in People with Parkinson’s Disease

by Hila Tamir-Ostrover, Sharon Hassin-Baer, Tsvia Fay-Karmon and Jason Friedman

Sensors 2024, 24(11), 3318; https://doi.org/10.3390/s24113318 - 22 May 2024

Viewed by 1202

Abstract

People with Parkinson’s disease often show deficits in dexterity, which, in turn, can lead to limitations in performing activities of daily life. Previous studies have suggested that training in playing the piano may improve or prevent a decline in dexterity in this population. In this pilot study, we tested three participants on a six-week, custom, piano-based training protocol, and quantified dexterity before and after the intervention using a sensor-enabled version of the nine-hole peg test, the box and block test, a test of finger synergies using unidimensional force sensors, and the Quantitative Digitography test using a digital piano, as well as selected relevant items from the motor parts of the MDS-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) and the Parkinson’s Disease Questionnaire (PDQ-39) quality of life questionnaire. The participants showed improved dexterity following the training program in several of the measures used. This pilot study proposes measures that can track changes in dexterity as a result of practice in people with Parkinson’s disease and describes a potential protocol that needs to be tested in a larger cohort. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors and Other Sensing Technologies for Diagnosis and Treatment of Parkinson's Disease and Movement Disorders)

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Figure 1

Figure 1
Comparison of overall performance in the behavioral tests. (a) Time taken to complete the nine-hole peg test (NHPT). (b) Number of blocks moved over the barrier in the box and block test. In both graphs, the solid line is the left hand and the dashed line is the right hand. Each color is a different participant. Full article ">Figure 2
Decomposition of the time taken to perform the nine-hole peg test into components. The definition of the different components is provided in the text. Full article ">Figure 3
Analyses of the finger pressing task: (a) the synergy index (Δv); (b) the amount of good variance (vgood), which does not affect the outcome variable; (c) the amount of bad variance (vbad), which does affect the outcome variable; and (d) straight line deviation—a measure of how well they controlled the total force (as shown on the screen). The colors indicate the participant (the same colors are used for each participant across the figures). Full article ">Figure 4
QDG summary: mean (left column) and coefficient of variance (CV) (right column) of the strike duration (first row) and interval between strikes (second row). The solid lines connecting the circles are for the left hand; the dashed lines connecting squares are for the right hand. Each color is a different participant. Full article ">

14 pages, 2167 KiB

Open AccessArticle

A New Wrist-Worn Tool Supporting the Diagnosis of Parkinsonian Motor Syndromes

by Luigi Battista and Antonietta Romaniello

Sensors 2024, 24(6), 1965; https://doi.org/10.3390/s24061965 - 19 Mar 2024

Cited by 2 | Viewed by 1510

Abstract

To date, clinical expert opinion is the gold standard diagnostic technique for Parkinson’s disease (PD), and continuous monitoring is a promising candidate marker. This study assesses the feasibility and performance of a new wearable tool for supporting the diagnosis of Parkinsonian motor syndromes. The proposed method is based on the use of a wrist-worn measuring system, the execution of a passive, continuous recording session, and a computation of two digital biomarkers (i.e., motor activity and rest tremor index). Based on the execution of some motor tests, a second step is provided for the confirmation of the results of passive recording. In this study, fifty-nine early PD patients and forty-one healthy controls were recruited. The results of this study show that: (a) motor activity was higher in controls than in PD with slight tremors at rest and did not significantly differ between controls and PD with mild-to-moderate tremor rest; (b) the tremor index was smaller in controls than in PD with mild-to-moderate tremor rest and did not significantly differ between controls and PD patients with slight tremor rest; (c) the combination of the said two motor parameters improved the performances in differentiating controls from PD. These preliminary findings demonstrate that the combination of said two digital biomarkers allowed us to differentiate controls from early PD. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors and Other Sensing Technologies for Diagnosis and Treatment of Parkinson's Disease and Movement Disorders)

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Figure 1

Figure 1
Results of passive continuous monitoring for each motor feature. (A,B) Box plot of motor activity (A) and tremor index (B) in healthy controls and PD patients. (C,D) Box plots of motor activity (C) and tremor index (D) in healthy subjects and PD patients with different severities of rest tremor. (E,F) The average temporal pattern of the motor activity (E) and tremor index (F) was determined during the 24-h recordings; for each recruited subject, values were sorted from the highest to the lowest value, and then the average value for each rest tremor severity was computed by considering all of the subjects with the same score of the item 3.17 of MDS-UPDRS. Full article ">Figure 2
Results of passive continuous monitoring based on combination and concurrent use of both parameters—motor activity and tremor index—in discriminating controls and PD subjects. The areas labelled I, II, and III (orange background) reference the values of the motor activity and the tremor index corresponding to test results, where the determined motor status is associated with the presence of the Parkinsonian motor syndrome. In contrast, area IV (light blue background) refers to test results where the determined motor status is related to the absence of motor conditions assimilable to Parkinsonian motor syndrome. The dark orange dots over the light orange background represent a true positive; the dark orange dots over the light blue background represent a false negative; the dark blue dots over the light orange background represent a false positive; and the dark blue dots over the light blue background represent a true negative. It should be noted that data on one PD subject of area III is not shown for a merely illustrative reason. Full article ">Figure 3
Results of active recording sessions. (A,B) Results related to the assessment of bradykinesia/slowness according to test 3.6 of the MDS-UPDRS executed with detecting the root mean square acceleration (A) of the tri-axial accelerometer and the frequency of the signal (B). (C–F) Results related to the assessment of wrist tremors at rest according to test 3.17 of the MDS-UPDRS executed with the detection of root mean square acceleration (C) of the tri-axial accelerometer, the frequency of the signal (D), the average value (E) and the maximum value (F) of the Fast Fourier Transforms of the acceleration signals between 3 Hz and 7 Hz. Full article ">Figure 4
Results of active recording sessions with combination of root mean square acceleration and the signal’s frequency. (A,B) Results on assessment of bradykinesia/slowness (A) and rest tremors (B) based on a combination and concurrent use of the tri-axial accelerometer’s root mean square acceleration and the signal’s frequency. It should be noted that data on one PD subject with tremor is not shown for a merely illustrative reason. Full article ">Figure 5
Active test. Temporal patterns of the motor activity aRMS and the Fast Fourier Transform of the axial acceleration signals determined during the execution of motor tests 3.6 on pronation-supination (A–D) and test 3.17 on rest tremor amplitude (E–H) in a normal subject (blue) and a subject with PD (orange). Full article ">

Review

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26 pages, 8959 KiB

Open AccessReview

A Review of Recent Advances in Cognitive-Motor Dual-Tasking for Parkinson’s Disease Rehabilitation

by Xiaohui Tan, Kai Wang, Wei Sun, Xinjin Li, Wenjie Wang and Feng Tian

Sensors 2024, 24(19), 6353; https://doi.org/10.3390/s24196353 - 30 Sep 2024

Cited by 1 | Viewed by 2804

Abstract

Background: Parkinson’s disease is primarily characterized by the degeneration of motor neurons, leading to significant impairments in movement. Initially, physical therapy was predominantly employed to address these motor issues through targeted rehabilitation exercises. However, recent research has indicated that cognitive training can enhance the quality of life for patients with Parkinson’s. Consequently, some researchers have posited that the simultaneous engagement in computer-assisted motor and cognitive dual-task (CADT) may yield superior therapeutic outcomes. Methods: A comprehensive literature search was performed across various databases, and studies were selected following PRISMA guidelines, focusing on CADT rehabilitation interventions. Results: Dual-task training enhances Parkinson’s disease (PD) rehabilitation by automating movements and minimizing secondary task interference. The inclusion of a sensor system provides real-time feedback to help patients make immediate adjustments during training. Furthermore, CADT promotes more vigorous participation and commitment to training exercises, especially those that are repetitive and can lead to patient boredom and demotivation. Virtual reality-tailored tasks, closely mirroring everyday challenges, facilitate more efficient patient adaptation post-rehabilitation. Conclusions: Although the current studies are limited by small sample sizes and low levels, CADT rehabilitation presents as a significant, effective, and potential strategy for PD. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors and Other Sensing Technologies for Diagnosis and Treatment of Parkinson's Disease and Movement Disorders)

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