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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: 31 December 2025 | Viewed by 31756

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Special Issue Editors

Dr. Massimo Marano

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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
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Prof. Dr. Antonio Suppa

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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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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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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

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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 (13 papers)

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21 pages, 3492 KB

Open AccessArticle

Wearable-Sensor-Based Analysis of Aerial Archimedean Spirals for Early Detection of Parkinson’s Disease

by Hao Shi, Sanyun Chen, Zhuoying Jiang and Yuting Wang

Sensors 2025, 25(24), 7579; https://doi.org/10.3390/s25247579 - 13 Dec 2025

Viewed by 192

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder whose early symptoms, especially mild tremor, are often clinically imperceptible. Early detection is crucial for initiating neuroprotective interventions to slow dopaminergic neuronal degeneration. Current PD diagnosis relies predominantly on subjective clinical assessments due to the absence of definitive biomarkers. This study proposes a novel approach for the early detection of PD through a custom-developed smart wristband equipped with an inertial measurement unit (IMU). Unlike previous paper-based or resting-tremor approaches, this study introduces a mid-air Archimedean spiral task combined with an attention-enhanced Long Short-Term Memory (LSTM) architecture, enabling substantially more sensitive detection of subtle early-stage Parkinsonian motor abnormalities. We propose LAFNet, a model based on an attention-enhanced LSTM network, which processes motion data that has been filtered using a Kalman algorithm for noise reduction, enabling rapid and accurate diagnosis. Clinical data evaluation demonstrated exceptional performance, with an accuracy of 99.02%. The proposed system shows significant potential for clinical translation as a non-invasive screening tool for early-stage Parkinson’s disease (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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12 pages, 1269 KB

Open AccessArticle

AI-Driven Analysis of Wrist-Worn Sensor Data for Monitoring Individual Treatment Response and Optimizing Levodopa Dosing in Parkinson’s Disease

by Mathias Sander, Moritz R. Messner, Sina K. Knapp, Franz M. J. Pfister and Urban M. Fietzek

Sensors 2025, 25(23), 7273; https://doi.org/10.3390/s25237273 - 28 Nov 2025

Viewed by 473

Abstract

Parkinson’s Disease is a progressive neurodegenerative disorder marked by motor fluctuations in later disease stages that complicate treatment with levodopa. Traditional approaches to dosing often fail to capture the complex and dynamic nature of these fluctuations. In this study, we present the PD9™ algorithm, a novel approach to continuous motor state monitoring using data from a wrist-worn inertial measurement unit sensor. The algorithm provides minute-by-minute assessments of motor state severity on a unified scale quantifying bradykinesia, dyskinesia, and ON states. Data collected from 67 patients over 55,482 min were analyzed to assess levodopa response cycles. Across 218 identified levodopa cycles, the algorithm revealed reproducible patterns of symptom development based on the motor state at the time of levodopa administration. In particular, levodopa doses administered during non-ideal motor states (e.g., during dyskinesia) highlighted the limitations of fixed, empirically determined dosing regimens and underscore the need for individualized therapy, based on motor state. These findings demonstrate how AI-enabled continuous monitoring could help realize a more personalized treatment of Parkinson’s disease and improve patient outcomes. 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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22 pages, 2412 KB

Open AccessArticle

Early Detection of Dysphagia Signs in Parkinson’s Disease: An Artificial Intelligence-Based Approach Using Non-Invasive Sensors

by Michele Antonio Gazzanti Pugliese di Cotrone, Nidà Farooq Akhtar, Martina Patera, Silvia Gallo, Umberto Mosca, Marco Ghislieri, Claudia Ferraris, Antonio Suppa, Carlo Alberto Artusi, Alessandro Zampogna, Gianluca Amprimo, Gabriele Imbalzano, Serena Cerfoglio, Veronica Cimolin, Luigi Borzì, Gabriella Olmo and Fernanda Irrera

Sensors 2025, 25(22), 6834; https://doi.org/10.3390/s25226834 - 8 Nov 2025

Viewed by 893

Abstract

The present study evaluates the effectiveness of a non-invasive wearable sensor system, combining accelerometers, surface electromyography, and artificial intelligence, to objectively characterize swallowing in elderly individuals affected by Parkinson’s Disease, without clinically manifested dysphagia. A cohort of patients and healthy control subjects performed the same swallowing test protocol, including tasks with different viscosity boluses, positioning a commercial adhesive grid of High-Density surface Electromyography (HD-sEMG) electrodes on the submental muscle and a triaxial accelerometer over the thyroid cartilage. Relevant temporal and spectral features were extracted from electromyography data. Proper filtering and processing by machine learning and Principal Component Analysis allowed identification of two distinct clusters of subjects, one predominantly composed of controls with just a few patients, the other mostly crowded by patients. Excellent classification performances were achieved (accuracy = 83.3%, precision = 79.0%, recall = 90.7%, F1-score = 84.5%, Cohen’s kappa = 0.67), revealing consistent differences in muscle activation patterns among subjects, even in the absence of clinically diagnosed dysphagia. These results support the feasibility of wearable sensor-based assessment as a reliable and non-invasive tool for the early detection of subclinical swallowing dysfunction in Parkinson’s Disease. 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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13 pages, 1060 KB

Open AccessArticle

Automated Shoulder Girdle Rigidity Assessment in Parkinson’s Disease via an Integrated Model- and Data-Driven Approach

by Fatemeh Khosrobeygi, Zahra Abouhadi, Ailar Mahdizadeh, Ahmad Ashoori, Negin Niksirat, Maryam S. Mirian and Martin J. McKeown

Sensors 2025, 25(19), 6019; https://doi.org/10.3390/s25196019 - 1 Oct 2025

Viewed by 644

Abstract

Parkinson’s disease (PD) is characterized by motor symptoms, with key diagnostic features, such as rigidity, traditionally assessed through subjective clinical scales. This study proposes a novel hybrid framework integrating model-driven biomechanical features (damping ratio, decay rate) and data-driven statistical features (maximum detail coefficient) from wearable sensor data during a modified pendulum test to quantify shoulder girdle rigidity objectively. Using weak supervision, these features were unified to generate robust labels from limited data, achieving a 10% improvement in PD/healthy control classification accuracy (0.71 vs. 0.64) over data-driven methods and matching model-driven performance (0.70). The damping ratio and decay rate, aligning with Wartenberg pendulum test metrics like relaxation index, revealed velocity-dependent aspects of rigidity, challenging its clinical characterization as velocity-independent. Outputs correlated strongly with UPDRS rigidity scores (r = 0.78, p < 0.001), validating their clinical utility as novel biomechanical biomarkers. This framework enhances interpretability and scalability, enabling remote, objective rigidity assessment for early diagnosis and telemedicine, advancing PD management through innovative sensor-based neurotechnology. 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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15 pages, 1075 KB

Open AccessArticle

Sympathetic Burden Measured Through a Chest-Worn Sensor Correlates with Spatiotemporal Gait Performances and Global Cognition in Parkinson’s Disease

by Gabriele Sergi, Ziv Yekutieli, Mario Meloni, Edoardo Bianchini, Giorgio Vivacqua, Vincenzo Di Lazzaro and Massimo Marano

Sensors 2025, 25(18), 5756; https://doi.org/10.3390/s25185756 - 16 Sep 2025

Cited by 1 | Viewed by 897

Abstract

Autonomic dysfunction is a key non-motor feature of Parkinson’s disease (PD) and may influence motor performance, particularly gait. While heart rate variability (HRV) has been associated with freezing of gait, its relationship with broader gait parameters remains unclear. The objective was to investigate correlations between resting-state HRV time-domain measures and spatiotemporal gait parameters during comfortable and fast walking in patients with idiopathic PD. Twenty-eight PD patients (mean age 68 ± 9 years) were evaluated at Campus Bio-Medico University Hospital. HRV was recorded at rest using the e-Sense pule™ portable sensor, including the Baevsky’s Stress Index a measure increasing with sympathetic burden. Gait parameters were assessed via the 10 m Timed Up and Go (TUG) test using the Mon4t™ smartphone app at comfortable and fast pace. Clinical data included UPDRS III, MoCA, and disease characteristics. Gait metrics significantly changed between walking conditions. HRV parameters clustered separately from gait metrics but intersected with significant correlations. Higher Stress Index values, reflecting sympathetic dominance, were associated with poorer gait performance, including prolonged transition times, shorter steps, and increased variability (p < 0.001, r = 0.57–0.61). MoCA scores inversely correlated with the Stress Index (r = −0.52, p = 0.004), linking cognitive and autonomic status. UPDRS III and MoCA were related to TUG metrics but not HRV. Time-domain HRV measures, particularly the Stress Index, are significantly associated with spatiotemporal gait features in PD, independent of gait speed. These findings suggest that impaired autonomic regulation contributes to functional mobility deficits in PD and supports the role of HRV as a biomarker in motor assessment. 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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15 pages, 4392 KB

Open AccessArticle

InfraRed Thermographic Measurements in Parkinson’s Disease Subjects: Preliminary Results

by Antonio Cannuli, Fabrizio Freni, Antonino Quattrocchi, Carmen Terranova, Andrea Venuto and Roberto Montanini

Sensors 2025, 25(17), 5243; https://doi.org/10.3390/s25175243 - 23 Aug 2025

Viewed by 1237

Abstract

In this preliminary study, the thermoregulatory response in individuals diagnosed with Parkinson’s disease was investigated by infrared thermography. Parkinson’s disease is a complex neurodegenerative disorder primarily known for motor impairments, significantly reducing the quality of life of affected people. However, in most cases, such disease is accompanied or preceded by non-motor symptoms, including autonomic dysfunction. As in the case of neurovegetative dysautonomia, this dysfunction involves a malfunction of the autonomic nervous system, which also plays a key role in thermoregulation. In general, such conditions are not always easy to detect; a valid method could be represented by the vasomotor response of the skin to cold stimuli. In this context, infrared thermography can provide insights into the thermoregulatory patterns associated with autonomic dysfunction, representing a valuable tool for non-invasive assessment of Parkinson’s research. Early biomarkers of the disease can be obtained through changes in skin temperature, allowing for timely intervention and management. The study was conducted on a cohort of 16 subjects (8 patients with Parkinson’s disease and 8 healthy controls), who were monitored with infrared images captured from their hands, following a specific protocol established by a preliminary analysis. Experimental results revealed that thermography can detect focal points and regions exhibiting either hyper- or hypothermia across the skin surface and muscular regions. This capability allows for extracting and categorizing precise medical data, which could inform future research aimed at identifying early markers of the disease. However, as this is a preliminary observational study, no diagnostic claims are made, and further investigations on larger cohorts with controlled comorbidities are needed. 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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12 pages, 1492 KB

Open AccessArticle

User Experiences of the Cue2walk Smart Cueing Device for Freezing of Gait in People with Parkinson’s Disease

by Matthijs van der Laan, Marc B. Rietberg, Martijn van der Ent, Floor Waardenburg, Vincent de Groot, Jorik Nonnekes and Erwin E. H. van Wegen

Sensors 2025, 25(15), 4702; https://doi.org/10.3390/s25154702 - 30 Jul 2025

Viewed by 1647

Abstract

Freezing of gait (FoG) impairs mobility and daily functioning and increases the risk of falls, leading to a reduced quality of life (QoL) in people with Parkinson’s disease (PD). The Cue2walk, a wearable smart cueing device, can detect FoG and hereupon provides rhythmic cues to help people with PD manage FoG in daily life. This study investigated the user experiences and device usage of the Cue2walk, and its impact on health-related QoL, FoG and daily activities. Twenty-five users of the Cue2walk were invited to fill out an online survey, which included a modified version of the EQ-5D-5L, tailored to the use of the Cue2walk, and its scale for health-related QoL, three FoG-related questions, and a question about customer satisfaction. Sixteen users of the Cue2walk completed the survey. Average device usage per day was 9 h (SD 4). Health-related QoL significantly increased from 5.2/10 (SD 1.3) to 6.2/10 (SD 1.3) (p = 0.005), with a large effect size (Cohen’s d = 0.83). A total of 13/16 respondents reported a positive effect on FoG duration, 12/16 on falls, and 10/16 on daily activities and self-confidence. Customer satisfaction was 7.8/10 (SD 1.7). This pilot study showed that Cue2walk usage per day is high and that 15/16 respondents experienced a variety of positive effects since using the device. To validate these findings, future studies should include a larger sample size and a more extensive set of questionnaires and physical measurements monitored over time. 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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29 pages, 4394 KB

Open AccessArticle

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

Cited by 8 | Viewed by 4593

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 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 KB

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

Cited by 3 | Viewed by 2148

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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14 pages, 2167 KB

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 7 | Viewed by 2279

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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Review

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23 pages, 517 KB

Open AccessReview

Associations Between Daily Step Counts and Sleep Parameters in Parkinson’s Disease: A Scoping Review

by Tracy Milane, Edoardo Bianchini, Matthias Chardon, Fabio Augusto Barbieri, Clint Hansen and Nicolas Vuillerme

Sensors 2025, 25(14), 4447; https://doi.org/10.3390/s25144447 - 17 Jul 2025

Viewed by 2161

Abstract

Background: People with Parkinson’s disease (PwPD) often experience sleep disturbances and reduced physical activity. Altered sleep behavior and lower daily steps have been linked to disease severity and symptom burden. Although physical activity may influence sleep, few studies have examined the relationship between sleep parameters and daily steps in PD. This scoping review aimed to review current knowledge on sleep parameters and daily steps collected concurrently in PwPD and their potential association. Methods: A systematic search was conducted in five databases, PubMed, Web of Science, Sport Discus, Cochrane Library, and Scopus. Methodological quality was assessed using a customized quality checklist developed by Zanardi and collaborators for observational studies, based on Downs and Black’s work. Results: Out of 1421 records, five studies met the eligibility criteria and were included in the review. Four studies reported wearable-based measurements of both step count and sleep parameters, while one study reported wearable-based measurements of step count and self-reported sleep measures. Two studies examined the association between sleep parameters and step count. One study did not find any correlation between sleep and step count, whereas one study reported a positive correlation between daytime sleepiness and step count. Conclusions: This review highlighted the lack of research investigating the relationship between sleep parameters and step count as an indicator of physical activity in PwPD. Findings are inconsistent with a potential positive correlation emerging between daytime sleepiness and step count. Findings also pointed toward lower step count and reduced sleep duration in PwPD, as measured with wearable devices. 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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21 pages, 15779 KB

Open AccessReview

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

Cited by 1 | Viewed by 3615

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 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 KB

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 10 | Viewed by 9342

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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