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

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 9053

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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio Suppa

E-Mail Website
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

E-Mail Website
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

E-Mail Website
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (5 papers)

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Research

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29 pages, 4394 KiB  
Article
Analysis of Voice, Speech, and Language Biomarkers of Parkinson’s Disease Collected in a Mixed Reality Setting
by Milosz Dudek, Daria Hemmerling, Marta Kaczmarska, Joanna Stepien, Mateusz Daniol, Marek Wodzinski and Magdalena Wojcik-Pedziwiatr
Sensors 2025, 25(8), 2405; https://doi.org/10.3390/s25082405 - 10 Apr 2025
Viewed by 461
Abstract
This study explores an innovative approach to early Parkinson’s disease (PD) detection by analyzing speech data collected using a mixed reality (MR) system. A total of 57 Polish participants, including PD patients and healthy controls, performed five speech tasks while using an MR [...] Read more.
This study explores an innovative approach to early Parkinson’s disease (PD) detection by analyzing speech data collected using a mixed reality (MR) system. A total of 57 Polish participants, including PD patients and healthy controls, performed five speech tasks while using an MR head-mounted display (HMD). Speech data were recorded and analyzed to extract acoustic and linguistic features, which were then evaluated using machine learning models, including logistic regression, support vector machines (SVMs), random forests, AdaBoost, and XGBoost. The XGBoost model achieved the best performance, with an F1-score of 0.90 ± 0.05 in the story-retelling task. Key features such as MFCCs (mel-frequency cepstral coefficients), spectral characteristics, RASTA-filtered auditory spectrum, and local shimmer were identified as significant in detecting PD-related speech alterations. Additionally, state-of-the-art deep learning models (wav2vec2, HuBERT, and WavLM) were fine-tuned for PD detection. HuBERT achieved the highest performance, with an F1-score of 0.94 ± 0.04 in the diadochokinetic task, demonstrating the potential of deep learning to capture complex speech patterns linked to neurodegenerative diseases. This study highlights the effectiveness of combining MR technology for speech data collection with advanced machine learning (ML) and deep learning (DL) techniques, offering a non-invasive and high-precision approach to PD diagnosis. The findings hold promise for broader clinical applications, advancing the diagnostic landscape for neurodegenerative disorders. 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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14 pages, 1518 KiB  
Article
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
Cited by 2 | Viewed by 1388
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. [...] Read more.
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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14 pages, 2167 KiB  
Article
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 3 | Viewed by 1665
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. [...] Read more.
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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Review

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21 pages, 15779 KiB  
Review
The Role of Virtual Reality on Parkinson’s Disease Management: A Bibliometric and Content Analysis
by Qiang Wu, Mengli Qiu, Xiaomei Liu, WanJiaAaron He, Ting Yang and Chengsen Jia
Sensors 2025, 25(5), 1432; https://doi.org/10.3390/s25051432 - 26 Feb 2025
Viewed by 1183
Abstract
The management of Parkinson’s disease (PD) has increasingly focused on innovative technologies, particularly virtual reality (VR), which has emerged as a significant tool for addressing neurological disorders. This bibliometric analysis summarizes current research trends and hotspots regarding VR applications in PD management. A [...] Read more.
The management of Parkinson’s disease (PD) has increasingly focused on innovative technologies, particularly virtual reality (VR), which has emerged as a significant tool for addressing neurological disorders. This bibliometric analysis summarizes current research trends and hotspots regarding VR applications in PD management. A comprehensive search of the Science Citation Index Expanded (SCIE) within the Web of Science Core Collection (WoSCC) identified 475 publications from 2000 to 2024. Key findings indicate a substantial increase in publication output, especially after 2013, driven by technological advancements and investments from major IT companies. Prominent research institutions and scholars from Australia, Israel, Italy, and Spain have led this field, exploring various VR applications for PD patients. The focus of VR therapy research has evolved from primarily addressing freezing of gait (FOG) to a broader range of functional impairments, including balance, postural control, upper limb motor, and cognitive function. This study provides valuable insights into the evolving landscape of clinical research on VR in PD management, highlighting global trends and potential areas for future investigation and application of VR therapies. 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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26 pages, 8959 KiB  
Review
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 3 | Viewed by 3946
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 [...] Read more.
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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