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Sensors and Sensing Technology Applied in Parkinson Disease
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Sensors and Sensing Technology Applied in Parkinson Disease

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 108885

Special Issue Editor

Dr. Markos Tsipouras

E-Mail Website
Guest Editor
University of Western Macedonia, Kozani, Greece
Interests: biomedical signal processing; medical informatics; IoT in healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, many technologies, methodologies, and systems for the management of patients with Parkinson’s disease have emerged. Wearable sensors, in-house monitoring, mobile device sensors, active sensing technologies, and IoT devices merge in a holistic and versatile environment for monitoring and assessment of PD patients’ overall condition and motor symptoms, such as bradykinesia, tremor, freezing of gait (FoG), postural instability, and rigidity, along with drug-related symptoms, such as Levodopa-induced dyskinesias (LIDs), and countless additional clinically manifested comorbidities. Technological advances in sensors and sensing technologies, wireless communications, and textile industry, combined with state-of-the-art signal processing and machine learning algorithms, establish the ecosystem for a future patient-oriented, personalized, ubiquitous healthcare system.  

This Special Issue aims to highlight the latest advances in sensing technologies, monitoring systems, and analysis methodologies for Parkinson’s disease. Our goal is to call on researchers who are engaged in the area of PD patient monitoring and assessment, based on sensing technologies.

Dr. Markos Tsipouras
Guest Editor

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Keywords

  • PD monitoring
  • Wearable sensors for PD
  • IoT devices for PD
  • PD motor symptoms assessment
  • Ubiquitous PD monitoring
  • Mobile devices for PD monitoring

Published Papers (25 papers)

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16 pages, 1923 KiB  
Article
Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors
by Roozbeh Atri, Kevin Urban, Barbara Marebwa, Tanya Simuni, Caroline Tanner, Andrew Siderowf, Mark Frasier, Magali Haas and Lee Lancashire
Sensors 2022, 22(18), 6831; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186831 - 09 Sep 2022
Cited by 2 | Viewed by 3997
Abstract
Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson’s disease (PD). However, the unsupervised and “open world” nature of this type [...] Read more.
Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson’s disease (PD). However, the unsupervised and “open world” nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these “walk-like” events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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11 pages, 944 KiB  
Article
Validation of Pressure-Sensing Insoles in Patients with Parkinson’s Disease during Overground Walking in Single and Cognitive Dual-Task Conditions
by Monica Parati, Matteo Gallotta, Manuel Muletti, Annalisa Pirola, Alice Bellafà, Beatrice De Maria and Simona Ferrante
Sensors 2022, 22(17), 6392; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176392 - 25 Aug 2022
Cited by 7 | Viewed by 2377
Abstract
There is a need for unobtrusive and valid tools to collect gait parameters in patients with Parkinson’s disease (PD). The novel promising tools are pressure-sensing insoles connected to a smartphone app; however, few studies investigated their measurement properties during simple or challenging conditions [...] Read more.
There is a need for unobtrusive and valid tools to collect gait parameters in patients with Parkinson’s disease (PD). The novel promising tools are pressure-sensing insoles connected to a smartphone app; however, few studies investigated their measurement properties during simple or challenging conditions in PD patients. This study aimed to examine the validity and reliability of gait parameters computed by pressure-sensing insoles (FeetMe® insoles, Paris, France). Twenty-five PD patients (21 males, mean age: 69 (7) years) completed two walking assessment sessions. In each session, participants walked on an electronic pressure-sensitive walkway (GaitRite®, CIR System Inc., Franklin, NJ, USA) without other additional instructions (i.e., single-task condition) and while performing a concurrent cognitive task (i.e., dual-task condition). Spatiotemporal gait parameters were measured simultaneously using the pressure-sensing insoles and the electronic walkway. Concurrent validity was assessed by correlation coefficients and Bland–Altman methodology. Test–retest reliability was examined by intraclass correlation coefficients (ICC) and minimal detectable changes (MDC). The validity results showed moderate to excellent correlations and good agreement between the two systems. Concerning test–retest reliability, moderate-to-excellent ICC values and acceptable MDC demonstrated the repeatability of the measured gait parameters. Our findings support the use of these insoles as complementary instruments to conventional tools during single and dual-task conditions. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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17 pages, 702 KiB  
Article
A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease
by Luca Mesin, Paola Porcu, Debora Russu, Gabriele Farina, Luigi Borzì, Wei Zhang, Yuzhu Guo and Gabriella Olmo
Sensors 2022, 22(7), 2613; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072613 - 29 Mar 2022
Cited by 9 | Viewed by 2552
Abstract
Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of [...] Read more.
Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2–3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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12 pages, 882 KiB  
Article
Fear of Falling Does Not Influence Dual-Task Gait Costs in People with Parkinson’s Disease: A Cross-Sectional Study
by Tino Prell, Manuela Uhlig, Steffen Derlien, Walter Maetzler and Hannah M. Zipprich
Sensors 2022, 22(5), 2029; https://0-doi-org.brum.beds.ac.uk/10.3390/s22052029 - 05 Mar 2022
Cited by 3 | Viewed by 2060
Abstract
Cognitive deficits and fear of falling (FOF) can both influence gait patterns in Parkinson’s disease (PD). While cognitive deficits contribute to gait changes under dual-task (DT) conditions, it is unclear if FOF also influences changes to gait while performing a cognitive task. Here, [...] Read more.
Cognitive deficits and fear of falling (FOF) can both influence gait patterns in Parkinson’s disease (PD). While cognitive deficits contribute to gait changes under dual-task (DT) conditions, it is unclear if FOF also influences changes to gait while performing a cognitive task. Here, we aimed to explore the association between FOF and DT costs in PD, we additionally describe associations between FOF, cognition, and gait parameters under single-task and DT. In 40 PD patients, motor symptoms (MDS-revised version of the Unified Parkinson’s Disease Rating Scale, Hoehn and Yahr), FOF (Falls Efficacy Scale International), and Montreal Cognitive Assessment (MoCA) were assessed. Spatiotemporal gait parameters were recorded with a validated mobile gait analysis system with inertial measurement units at each foot while patients walked in a 50 m hallway at their preferred speed under single-task and DT conditions. Under single-task conditions, stride length (β = 0.798) and spatial variability (β = 0.202) were associated with FOF (adjusted R2 = 0.19, p < 0.001) while the MoCA was only weakly associated with temporal variability (adjusted R2 = 0.05, p < 0.001). Under DT conditions, speed, stride length, and cadence decreased, while spatial variability, temporal variability, and stride duration increased with the largest effect size for speed. DT costs of stride length (β = 0.42) and age (β = 0.58) explained 18% of the MoCA variance. However, FOF was not associated with the DT costs of gait parameters. Gait difficulties in PD may exacerbate when cognitive tasks are added during walking. However, FOF does not appear to have a relevant effect on dual-task costs of gait. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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16 pages, 2714 KiB  
Article
Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease
by Alfonso Maria Ponsiglione, Carlo Ricciardi, Francesco Amato, Mario Cesarelli, Giuseppe Cesarelli and Giovanni D’Addio
Sensors 2022, 22(5), 1708; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051708 - 22 Feb 2022
Cited by 7 | Viewed by 2161
Abstract
The impact of neurodegenerative disorders is twofold; they affect both quality of life and healthcare expenditure. In the case of Parkinson’s disease, several strategies have been attempted to support the pharmacological treatment with rehabilitation protocols aimed at restoring motor function. In this scenario, [...] Read more.
The impact of neurodegenerative disorders is twofold; they affect both quality of life and healthcare expenditure. In the case of Parkinson’s disease, several strategies have been attempted to support the pharmacological treatment with rehabilitation protocols aimed at restoring motor function. In this scenario, the study of upper limb control mechanisms is particularly relevant due to the complexity of the joints involved in the movement of the arm. For these reasons, it is difficult to define proper indicators of the rehabilitation outcome. In this work, we propose a methodology to analyze and extract an ensemble of kinematic parameters from signals acquired during a complex upper limb reaching task. The methodology is tested in both healthy subjects and Parkinson’s disease patients (N = 12), and a statistical analysis is carried out to establish the value of the extracted kinematic features in distinguishing between the two groups under study. The parameters with the greatest number of significances across the submovements are duration, mean velocity, maximum velocity, maximum acceleration, and smoothness. Results allowed the identification of a subset of significant kinematic parameters that could serve as a proof-of-concept for a future definition of potential indicators of the rehabilitation outcome in Parkinson’s disease. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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11 pages, 2413 KiB  
Article
A Non-Invasive IR Sensor Technique to Differentiate Parkinson’s Disease from Other Neurological Disorders Using Autonomic Dysfunction as Diagnostic Criterion
by Brindha Anbalagan, Sunitha Karnam Anantha, Sridhar P. Arjunan, Venkatraman Balasubramanian, Menaka Murugesan and Kalpana R
Sensors 2022, 22(1), 266; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010266 - 30 Dec 2021
Cited by 3 | Viewed by 2903
Abstract
Early diagnosis of Parkinson’s disease (PD) plays a critical role in effective disease management and delayed disease progression. This study reports a technique that could diagnose and differentiate PD from essential tremor (ET) in its earlier stage using a non-motor phenotype. Autonomic dysfunction, [...] Read more.
Early diagnosis of Parkinson’s disease (PD) plays a critical role in effective disease management and delayed disease progression. This study reports a technique that could diagnose and differentiate PD from essential tremor (ET) in its earlier stage using a non-motor phenotype. Autonomic dysfunction, an early symptom in PD patients, is caused by α-synuclein pathogenesis in the central nervous system and can be diagnosed using skin vasomotor response to cold stimuli. In this study, the investigations were performed using data collected from 20 PD, 20 ET and 20 healthy subjects. Infrared thermography was used for the cold stress test to observe subjects’ hand temperature before and after cold stimuli. The results show that the recovery rate of hand temperature was significantly different between the groups. The data obtained in the cold stress test were verified using Pearson’s cross-correlation technique, which showed that few disease parameters like medication and motor rating score had an impact on the recovery rate of hand temperature in PD subjects. The characteristics of the three groups were compared and classified using the k-means clustering algorithm. The sensitivity and specificity of these techniques were analyzed using an Receiver Operating Characteristic (ROC) curve analyzer. These results show that this non-invasive technique can be used as an effective tool in the diagnosis and differentiation of PD in its early stage. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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15 pages, 753 KiB  
Article
Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test
by Tomas Mendoza, Chia-Hsuan Lee, Chien-Hua Huang and Tien-Lung Sun
Sensors 2021, 21(17), 5930; https://doi.org/10.3390/s21175930 - 03 Sep 2021
Cited by 4 | Viewed by 1907
Abstract
Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple [...] Read more.
Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple clinical tests, including the timed-up and go test (TUG), short form berg balance scale (SFBBS), and short portable mental status questionnaire (SPMSQ) to measure different factors related to postural stability that have been found to increase the risk of falling. We attached an inertial sensor to the lower back of a group of elderly subjects while they performed the TUG test, providing us with a tri-axial acceleration signal, which we used to extract a set of features, including multi-scale entropy (MSE), permutation entropy (PE), and statistical features. Using the score for each clinical test, we classified our participants into fallers or non-fallers in order to (1) compare the features calculated from the inertial sensor data, and (2) compare the screening capabilities of the multifactor clinical test against each individual test. We use random forest to select features and classify subjects across all scenarios. The results show that the combination of MSE and statistic features overall provide the best classification results. Meanwhile, PE is not an important feature in any scenario in our study. In addition, a t-test shows that the multifactor test of TUG and BBS is a better classifier of subjects in this study. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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17 pages, 9845 KiB  
Article
Feasibility of a Mobile-Based System for Unsupervised Monitoring in Parkinson’s Disease
by Raquel Bouça-Machado, Filipa Pona-Ferreira, Mariana Leitão, Ana Clemente, Diogo Vila-Viçosa, Linda Azevedo Kauppila, Rui M. Costa, Ricardo Matias and Joaquim J. Ferreira
Sensors 2021, 21(15), 4972; https://0-doi-org.brum.beds.ac.uk/10.3390/s21154972 - 21 Jul 2021
Cited by 9 | Viewed by 2858
Abstract
Mobile health (mHealth) has emerged as a potential solution to providing valuable ecological information about the severity and burden of Parkinson’s disease (PD) symptoms in real-life conditions. Objective: The objective of our study was to explore the feasibility and usability of an [...] Read more.
Mobile health (mHealth) has emerged as a potential solution to providing valuable ecological information about the severity and burden of Parkinson’s disease (PD) symptoms in real-life conditions. Objective: The objective of our study was to explore the feasibility and usability of an mHealth system for continuous and objective real-life measures of patients’ health and functional mobility, in unsupervised settings. Methods: Patients with a clinical diagnosis of PD, who were able to walk unassisted, and had an Android smartphone were included. Patients were asked to answer a daily survey, to perform three weekly active tests, and to perform a monthly in-person clinical assessment. Feasibility and usability were explored as primary and secondary outcomes. An exploratory analysis was performed to investigate the correlation between data from the mKinetikos app and clinical assessments. Results: Seventeen participants (85%) completed the study. Sixteen participants (94.1%) showed a medium-to-high level of compliance with the mKinetikos system. A 6-point drop in the total score of the Post-Study System Usability Questionnaire was observed. Conclusions: Our results support the feasibility of the mKinetikos system for continuous and objective real-life measures of a patient’s health and functional mobility. The observed correlations of mKinetikos metrics with clinical data seem to suggest that this mHealth solution is a promising tool to support clinical decisions. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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18 pages, 1338 KiB  
Article
Multimodal Classification of Parkinson’s Disease in Home Environments with Resiliency to Missing Modalities
by Farnoosh Heidarivincheh, Ryan McConville, Catherine Morgan, Roisin McNaney, Alessandro Masullo, Majid Mirmehdi, Alan L. Whone and Ian Craddock
Sensors 2021, 21(12), 4133; https://0-doi-org.brum.beds.ac.uk/10.3390/s21124133 - 16 Jun 2021
Cited by 11 | Viewed by 3793
Abstract
Parkinson’s disease (PD) is a chronic neurodegenerative condition that affects a patient’s everyday life. Authors have proposed that a machine learning and sensor-based approach that continuously monitors patients in naturalistic settings can provide constant evaluation of PD and objectively analyse its progression. In [...] Read more.
Parkinson’s disease (PD) is a chronic neurodegenerative condition that affects a patient’s everyday life. Authors have proposed that a machine learning and sensor-based approach that continuously monitors patients in naturalistic settings can provide constant evaluation of PD and objectively analyse its progression. In this paper, we make progress toward such PD evaluation by presenting a multimodal deep learning approach for discriminating between people with PD and without PD. Specifically, our proposed architecture, named MCPD-Net, uses two data modalities, acquired from vision and accelerometer sensors in a home environment to train variational autoencoder (VAE) models. These are modality-specific VAEs that predict effective representations of human movements to be fused and given to a classification module. During our end-to-end training, we minimise the difference between the latent spaces corresponding to the two data modalities. This makes our method capable of dealing with missing modalities during inference. We show that our proposed multimodal method outperforms unimodal and other multimodal approaches by an average increase in F1-score of 0.25 and 0.09, respectively, on a data set with real patients. We also show that our method still outperforms other approaches by an average increase in F1-score of 0.17 when a modality is missing during inference, demonstrating the benefit of training on multiple modalities. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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14 pages, 1156 KiB  
Article
Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data
by Scott Pardoel, Gaurav Shalin, Julie Nantel, Edward D. Lemaire and Jonathan Kofman
Sensors 2021, 21(6), 2246; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062246 - 23 Mar 2021
Cited by 29 | Viewed by 4012
Abstract
Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would [...] Read more.
Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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15 pages, 2448 KiB  
Article
Relationship between Muscular Activity and Postural Control Changes after Proprioceptive Focal Stimulation (Equistasi®) in Middle-Moderate Parkinson’s Disease Patients: An Explorative Study
by Fabiola Spolaor, Marco Romanato, Guiotto Annamaria, Antonella Peppe, Leila Bakdounes, Duc-Khanh To, Daniele Volpe and Zimi Sawacha
Sensors 2021, 21(2), 560; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020560 - 14 Jan 2021
Cited by 12 | Viewed by 2720
Abstract
The aim of this study was to investigate the effects of Equistasi®, a wearable device, on the relationship between muscular activity and postural control changes in a sample of 25 Parkinson’s disease (PD) subjects. Gait analysis was carried out through a [...] Read more.
The aim of this study was to investigate the effects of Equistasi®, a wearable device, on the relationship between muscular activity and postural control changes in a sample of 25 Parkinson’s disease (PD) subjects. Gait analysis was carried out through a six-cameras stereophotogrammetric system synchronized with two force plates, an eight-channel surface electromyographic system, recording the activity of four muscles bilaterally: Rectus femoris, tibialis anterior (TA), biceps femoris, and gastrocnemius lateralis (GL). The peak of the envelope (PoE) and its occurrence within the gait cycle (position of the peak of the envelope, PPoE) were calculated. Frequency-domain posturographic parameters were extracted while standing still on a force plate in eyes open and closed conditions for 60 s. After the treatment with Equistasi®, the mid-low (0.5–0.75) Hz and mid-high (0.75–1 Hz) components associated with the vestibular and somatosensory systems, PoE and PPoE, displayed a shift toward the values registered on the controls. Furthermore, a correlation was found between changes in proprioception (power spectrum frequencies during the Romberg Test) and the activity of GL, BF (PoE), and TA (PPoE). Results of this study could provide a quantitative estimation of the effects of a neurorehabilitation device on the peripheral and central nervous system in PD. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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29 pages, 4385 KiB  
Article
Automatic Resting Tremor Assessment in Parkinson’s Disease Using Smartwatches and Multitask Convolutional Neural Networks
by Luis Sigcha, Ignacio Pavón, Nélson Costa, Susana Costa, Miguel Gago, Pedro Arezes, Juan Manuel López and Guillermo De Arcas
Sensors 2021, 21(1), 291; https://0-doi-org.brum.beds.ac.uk/10.3390/s21010291 - 04 Jan 2021
Cited by 40 | Viewed by 6106
Abstract
Resting tremor in Parkinson’s disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients’ quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, [...] Read more.
Resting tremor in Parkinson’s disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients’ quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients’ daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients’ daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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13 pages, 3325 KiB  
Article
A Wearable Sensor System to Measure Step-Based Gait Parameters for Parkinson’s Disease Rehabilitation
by Niveditha Muthukrishnan, James J. Abbas and Narayanan Krishnamurthi
Sensors 2020, 20(22), 6417; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226417 - 10 Nov 2020
Cited by 15 | Viewed by 3859
Abstract
Spatiotemporal parameters of gait serve as an important biomarker to monitor gait impairments as well as to develop rehabilitation systems. In this work, we developed a computationally-efficient algorithm (SDI-Step) that uses segmented double integration to calculate step length and step time from wearable [...] Read more.
Spatiotemporal parameters of gait serve as an important biomarker to monitor gait impairments as well as to develop rehabilitation systems. In this work, we developed a computationally-efficient algorithm (SDI-Step) that uses segmented double integration to calculate step length and step time from wearable inertial measurement units (IMUs) and assessed its ability to reliably and accurately measure spatiotemporal gait parameters. Two data sets that included simultaneous measurements from wearable sensors and from a laboratory-based system were used in the assessment. The first data set utilized IMU sensors and a GAITRite mat in our laboratory to monitor gait in fifteen participants: 9 young adults (YA1) (5 females, 4 males, age 23.6 ± 1 years), and 6 people with Parkinson’s disease (PD) (3 females, 3 males, age 72.3 ± 6.6 years). The second data set, which was accessed from a publicly-available repository, utilized IMU sensors and an optoelectronic system to monitor gait in five young adults (YA2) (2 females, 3 males, age 30.5 ± 3.5 years). In order to provide a complete representation of validity, we used multiple statistical analyses with overlapping metrics. Gait parameters such as step time and step length were calculated and the agreement between the two measurement systems for each gait parameter was assessed using Passing–Bablok (PB) regression analysis and calculation of the Intra-class Correlation Coefficient (ICC (2,1)) with 95% confidence intervals for a single measure, absolute-agreement, 2-way mixed-effects model. In addition, Bland–Altman (BA) plots were used to visually inspect the measurement agreement. The values of the PB regression slope were close to 1 and intercept close to 0 for both step time and step length measures. The results obtained using ICC (2,1) for step length showed a moderate to excellent agreement for YA (between 0.81 and 0.95) and excellent agreement for PD (between 0.93 and 0.98), while both YA and PD had an excellent agreement in step time ICCs (>0.9). Finally, examining the BA plots showed that the measurement difference was within the limits of agreement (LoA) with a 95% probability. Results from this preliminary study indicate that using the SDI-Step algorithm to process signals from wearable IMUs provides measurements that are in close agreement with widely-used laboratory-based systems and can be considered as a valid tool for measuring spatiotemporal gait parameters. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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13 pages, 2084 KiB  
Article
Instrumental Assessment of Stepping in Place Captures Clinically Relevant Motor Symptoms of Parkinson’s Disease
by Karen Otte, Tobias Ellermeyer, Tim-Sebastian Vater, Marlen Voigt, Daniel Kroneberg, Ludwig Rasche, Theresa Krüger, Hanna Maria Röhling, Bastian Kayser, Sebastian Mansow-Model, Fabian Klostermann, Alexander Ulrich Brandt, Friedemann Paul, Axel Lipp and Tanja Schmitz-Hübsch
Sensors 2020, 20(19), 5465; https://0-doi-org.brum.beds.ac.uk/10.3390/s20195465 - 23 Sep 2020
Cited by 7 | Viewed by 2721
Abstract
Fluctuations of motor symptoms make clinical assessment in Parkinson’s disease a complex task. New technologies aim to quantify motor symptoms, and their remote application holds potential for a closer monitoring of treatment effects. The focus of this study was to explore the potential [...] Read more.
Fluctuations of motor symptoms make clinical assessment in Parkinson’s disease a complex task. New technologies aim to quantify motor symptoms, and their remote application holds potential for a closer monitoring of treatment effects. The focus of this study was to explore the potential of a stepping in place task using RGB-Depth (RGBD) camera technology to assess motor symptoms of people with Parkinson’s disease. In total, 25 persons performed a 40 s stepping in place task in front of a single RGBD camera (Kinect for Xbox One) in up to two different therapeutic states. Eight kinematic parameters were derived from knee movements to describe features of hypokinesia, asymmetry, and arrhythmicity of stepping. To explore their potential clinical utility, these parameters were analyzed for their Spearman’s Rho rank correlation to clinical ratings, and for intraindividual changes between treatment conditions using standard response mean and paired t-test. Test performance not only differed between ON and OFF treatment conditions, but showed moderate correlations to clinical ratings, specifically ratings of postural instability (pull test). Furthermore, the test elicited freezing in some subjects. Results suggest that this single standardized motor task is a promising candidate to assess an array of relevant motor symptoms of Parkinson’s disease. The simple technical test setup would allow future use by patients themselves. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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13 pages, 476 KiB  
Article
Effect of a Concurrent Cognitive Task, with Stabilizing Visual Information and Withdrawal, on Body Sway Adaptation of Parkinsonian’s Patients in an Off-Medication State: A Controlled Study
by Arnaud Delafontaine, Clint Hansen, Iris Marolleau, Stefan Kratzenstein and Arnaud Gouelle
Sensors 2020, 20(18), 5059; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185059 - 06 Sep 2020
Cited by 8 | Viewed by 2400
Abstract
Background: In persons with Parkinson’s disease (pwPD) any additional somatosensory or distractor interference can influence the posture. When deprivation of vision and dual-task are associated, the effect on biomechanical performance is less consistent. The aim of this study was to evaluate the [...] Read more.
Background: In persons with Parkinson’s disease (pwPD) any additional somatosensory or distractor interference can influence the posture. When deprivation of vision and dual-task are associated, the effect on biomechanical performance is less consistent. The aim of this study was to evaluate the role of the visual deprivation and a cognitive task on the static balance in earlier stage PD subjects. Methods: Fifteen off-medication state pwPD (9 women and 6 men), 67.7 ± 7.3 years old, diagnosed PD since 5.4 ± 3.4 years, only Hoehn and Yahr state 2 and fifteen young control adults (7 women and 8 men) aged 24.9 ± 4.9 years, performed semi-tandem task under four randomized experimental conditions: eyes opened single-task, eyes closed single-task, eyes opened dual-task and eyes closed dual-task. The center of pressure (COP) was measured using a force plate and electromyography signals (EMG) of the ankle/hip muscles were recorded. Traditional parameters, including COP pathway length, ellipse area, mediolateral/anteroposterior root-mean-square and non-linear measurements were computed. The effect of vision privation, cognitive task, and vision X cognitive was investigated by a 2 (eyes opened/eyes closed) × 2 (postural task alone/with cognitive task) repeated-measures ANOVA after application of a Bonferroni pairwise correction for multiple comparisons. Significant interactions were further analyzed using post-hoc tests. Results: In pwPD, both COP pathway length (p < 0.01), ellipse area (p < 0.01) and mediolateral/anteroposterior root-mean-square (p < 0.01) were increased with the eyes closed, while the dual-task had no significant effect when compared to the single-task condition. Comparable results were observed in the control group for who COP pathway was longer in all conditions compared to eyes opened single-task (p < 0.01) and longer in conditions with eyes closed compared to eyes opened dual-task (p < 0.01). Similarly, all differences in EMG activity of pwPD were exclusively observed between eyes opened vs. eyes closed conditions, and especially for the forward leg’s soleus (p < 0.01) and backward tibialis anterior (p < 0.01). Conclusions: These results in pwPD without noticeable impairment of static balance encourage the assessment of both visual occlusion and dual-task conditions when the appearance of significant alteration during the dual-task could reveal the subtle worsening onset of the balance control. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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24 pages, 3182 KiB  
Article
An FPGA Based Tracking Implementation for Parkinson’s Patients
by Giuseppe Conti, Marcos Quintana, Pedro Malagón and David Jiménez
Sensors 2020, 20(11), 3189; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113189 - 04 Jun 2020
Cited by 3 | Viewed by 3088
Abstract
This paper presents a study on the optimization of the tracking system designed for patients with Parkinson’s disease tested at a day hospital center. The work performed significantly improves the efficiency of the computer vision based system in terms of energy consumption and [...] Read more.
This paper presents a study on the optimization of the tracking system designed for patients with Parkinson’s disease tested at a day hospital center. The work performed significantly improves the efficiency of the computer vision based system in terms of energy consumption and hardware requirements. More specifically, it optimizes the performances of the background subtraction by segmenting every frame previously characterized by a Gaussian mixture model (GMM). This module is the most demanding part in terms of computation resources, and therefore, this paper proposes a method for its implementation by means of a low-cost development board based on Zynq XC7Z020 SoC (system on chip). The platform used is the ZedBoard, which combines an ARM Processor unit and a FPGA. It achieves real-time performance and low power consumption while performing the target request accurately. The results and achievements of this study, validated in real medical settings, are discussed and analyzed within. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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17 pages, 4977 KiB  
Article
Validity of a Fully-Immersive VR-Based Version of the Box and Blocks Test for Upper Limb Function Assessment in Parkinson’s Disease
by Edwin Daniel Oña, Alberto Jardón, Alicia Cuesta-Gómez, Patricia Sánchez-Herrera-Baeza, Roberto Cano-de-la-Cuerda and Carlos Balaguer
Sensors 2020, 20(10), 2773; https://0-doi-org.brum.beds.ac.uk/10.3390/s20102773 - 13 May 2020
Cited by 37 | Viewed by 6197
Abstract
In recent decades, gaming technology has been accepted as a feasible method for complementing traditional clinical practice, especially in neurorehabilitation; however, the viability of using 3D Virtual Reality (VR) for the assessment of upper limb motor function has not been fully explored. For [...] Read more.
In recent decades, gaming technology has been accepted as a feasible method for complementing traditional clinical practice, especially in neurorehabilitation; however, the viability of using 3D Virtual Reality (VR) for the assessment of upper limb motor function has not been fully explored. For that purpose, we developed a VR-based version of the Box and Blocks Test (BBT), a clinical test for the assessment of manual dexterity, as an automated alternative to the classical procedure. Our VR-based BBT (VR-BBT) integrates the traditional BBT mechanics into gameplay using the Leap Motion Controller (LMC) to capture the user’s hand motion and the Oculus Rift headset to provide a fully immersive experience. This paper focuses on evaluating the validity of our VR-BBT to reliably measure the manual dexterity in a sample of patients with Parkinson’s Disease (PD). For this study, a group of twenty individuals in a mild to moderate stage of PD were recruited. Participants were asked to perform the physical BBT (once) and our proposed VR-BBT (twice) system, separately. Correlation analysis of collected data was carried out. Statistical analysis proved that the performance data collected by the VR-BBT significantly correlated with the conventional assessment of the BBT. The VR-BBT scores have shown a significant association with PD severity measured by the Hoehn and Yahr scale. This fact suggests that the VR-BBT could be used as a reliable indicator for health improvements in patients with PD. Finally, the VR-BBT system presented high usability and acceptability rated by clinicians and patients. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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Graphical abstract

27 pages, 2443 KiB  
Article
A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson’s Disease Diagnosis
by Erika Rovini, Carlo Maremmani and Filippo Cavallo
Sensors 2020, 20(9), 2630; https://0-doi-org.brum.beds.ac.uk/10.3390/s20092630 - 05 May 2020
Cited by 32 | Viewed by 3703
Abstract
Objective assessment of the motor evaluation test for Parkinson’s disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system [...] Read more.
Objective assessment of the motor evaluation test for Parkinson’s disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system composed of four inertial devices (two SensHand and two SensFoot), and related processing algorithms for extracting parameters from limbs motion was tested on 40 healthy subjects and 40 PD patients. Seventy-eight and 96 kinematic parameters were measured from lower and upper limbs, respectively. Statistical and correlation analysis allowed to define four datasets that were used to train and test five supervised learning classifiers. Excellent discrimination between the two groups was obtained with all the classifiers (average accuracy ranging from 0.936 to 0.960) and all the datasets (average accuracy ranging from 0.953 to 0.966), over three conditions that included parameters derived from lower, upper or all limbs. The best performances (accuracy = 1.00) were obtained when classifying all the limbs with linear support vector machine (SVM) or gaussian SVM. Even if further studies should be done, the current results are strongly promising to improve this system as a support tool for clinicians in objectifying PD diagnosis and monitoring. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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12 pages, 944 KiB  
Article
Trunk Range of Motion Is Related to Axial Rigidity, Functional Mobility and Quality of Life in Parkinson’s Disease: An Exploratory Study
by Roberto Cano-de-la-Cuerda, Lydia Vela-Desojo, Marcos Moreno-Verdú, María del Rosario Ferreira-Sánchez, Yolanda Macías-Macías and Juan Carlos Miangolarra-Page
Sensors 2020, 20(9), 2482; https://0-doi-org.brum.beds.ac.uk/10.3390/s20092482 - 27 Apr 2020
Cited by 17 | Viewed by 7634
Abstract
Background: People with Parkinson’s disease (PD) present deficits of the active range of motion (ROM), prominently in their trunk. However, if these deficits are associated with axial rigidity, the functional mobility or health related quality of life (HRQoL), remains unknown. The aim of [...] Read more.
Background: People with Parkinson’s disease (PD) present deficits of the active range of motion (ROM), prominently in their trunk. However, if these deficits are associated with axial rigidity, the functional mobility or health related quality of life (HRQoL), remains unknown. The aim of this paper is to study the relationship between axial ROM and axial rigidity, the functional mobility and HRQoL in patients with mild to moderate PD. Methods: An exploratory study was conducted. Non-probabilistic sampling of consecutive cases was used. Active trunk ROM was assessed by a universal goniometer. A Biodex System isokinetic dynamometer was used to measure the rigidity of the trunk. Functional mobility was determined by the Get Up and Go (GUG) test, and HRQoL was assessed with the PDQ-39 and EuroQol-5D questionnaires. Results: Thirty-six mild to moderate patients with PD were evaluated. Significant correlations were observed between trunk extensors rigidity and trunk flexion and extension ROM. Significant correlations were observed between trunk flexion, extension and rotation ROM and GUG. Moreover, significant correlations were observed between trunk ROM for flexion, extension and rotations (both sides) and PDQ-39 total score. However, these correlations were considered poor. Conclusions: Trunk ROM for flexion and extension movements, measured by a universal goniometer, were correlated with axial extensors rigidity, evaluated by a technological device at 30°/s and 45°/s, and functional mobility. Moreover, trunk ROM for trunk flexion, extension and rotations were correlated with HRQoL in patients with mild to moderate PD. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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20 pages, 1961 KiB  
Article
The Impact of a Novel Immersive Virtual Reality Technology Associated with Serious Games in Parkinson’s Disease Patients on Upper Limb Rehabilitation: A Mixed Methods Intervention Study
by Patricia Sánchez-Herrera-Baeza, Roberto Cano-de-la-Cuerda, Edwin Daniel Oña-Simbaña, Domingo Palacios-Ceña, Jorge Pérez-Corrales, Juan Nicolas Cuenca-Zaldivar, Javier Gueita-Rodriguez, Carlos Balaguer-Bernaldo de Quirós, Alberto Jardón-Huete and Alicia Cuesta-Gomez
Sensors 2020, 20(8), 2168; https://0-doi-org.brum.beds.ac.uk/10.3390/s20082168 - 11 Apr 2020
Cited by 30 | Viewed by 8966
Abstract
Background: Parkinson’s disease is a neurodegenerative disorder that causes impaired motor functions. Virtual reality technology may be recommended to optimize motor learning in a safe environment. The objective of this paper was to evaluate the effects of a novel immersive virtual reality technology [...] Read more.
Background: Parkinson’s disease is a neurodegenerative disorder that causes impaired motor functions. Virtual reality technology may be recommended to optimize motor learning in a safe environment. The objective of this paper was to evaluate the effects of a novel immersive virtual reality technology used for serious games (Oculus Rift 2 plus leap motion controller—OR2-LMC) for upper limb outcomes (muscle strength, coordination, speed of movements, fine and gross dexterity). Another objective was to obtain qualitative data for participants’ experiences related to the intervention. Methods: A mixed methods intervention (embedded) study was used, with a qualitative design after a technology intervention (quantitative design). The intervention and qualitative design followed international guidelines and were integrated into the method and reporting subheadings. Results: Significant improvements were observed in strength (p = 0.028), fine (p = 0.026 to 0.028) and gross coordination dexterity, and speed movements (p = 0.039) in the affected side, with excellent compliance (100%) and a high level of satisfaction (3.66 ± 0.18 points out of the maximum of 4). No adverse side effects were observed. Qualitative findings described patients’ perspectives regarding OR2-LMC treatment, facilitators and barriers for adherence, OR2-LMC applications, and treatment improvements. Conclusions: The intervention showed positive results for the upper limbs, with elements of discordance, expansion, and confirmation between qualitative and quantitative results. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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26 pages, 2208 KiB  
Article
Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors
by Luis Sigcha, Nélson Costa, Ignacio Pavón, Susana Costa, Pedro Arezes, Juan Manuel López and Guillermo De Arcas
Sensors 2020, 20(7), 1895; https://0-doi-org.brum.beds.ac.uk/10.3390/s20071895 - 29 Mar 2020
Cited by 64 | Viewed by 8010
Abstract
Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson’s disease (PD). The occurrence of FOG reduces the patients’ quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be [...] Read more.
Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson’s disease (PD). The occurrence of FOG reduces the patients’ quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms’ evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients’ homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system). Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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24 pages, 16652 KiB  
Article
Automatic, Qualitative Scoring of the Interlocking Pentagon Drawing Test (PDT) Based on U-Net and Mobile Sensor Data
by Ingyu Park, Yun Joong Kim, Yeo Jin Kim and Unjoo Lee
Sensors 2020, 20(5), 1283; https://0-doi-org.brum.beds.ac.uk/10.3390/s20051283 - 27 Feb 2020
Cited by 9 | Viewed by 5276
Abstract
We implemented a mobile phone application of the pentagon drawing test (PDT), called mPDT, with a novel, automatic, and qualitative scoring method for the application based on U-Net (a convolutional network for biomedical image segmentation) coupled with mobile sensor data obtained with the [...] Read more.
We implemented a mobile phone application of the pentagon drawing test (PDT), called mPDT, with a novel, automatic, and qualitative scoring method for the application based on U-Net (a convolutional network for biomedical image segmentation) coupled with mobile sensor data obtained with the mPDT. For the scoring protocol, the U-Net was trained with 199 PDT hand-drawn images of 512 × 512 resolution obtained via the mPDT in order to generate a trained model, Deep5, for segmenting a drawn right or left pentagon. The U-Net was also trained with 199 images of 512 × 512 resolution to attain the trained model, DeepLock, for segmenting an interlocking figure. Here, the epochs were iterated until the accuracy was greater than 98% and saturated. The mobile senor data primarily consisted of x and y coordinates, timestamps, and touch-events of all the samples with a 20 ms sampling period. The velocities were then calculated using the primary sensor data. With Deep5, DeepLock, and the sensor data, four parameters were extracted. These included the number of angles (0–4 points), distance/intersection between the two drawn figures (0–4 points), closure/opening of the drawn figure contours (0–2 points), and tremors detected (0–1 points). The parameters gave a scaling of 11 points in total. The performance evaluation for the mPDT included 230 images from subjects and their associated sensor data. The results of the performance test indicated, respectively, a sensitivity, specificity, accuracy, and precision of 97.53%, 92.62%, 94.35%, and 87.78% for the number of angles parameter; 93.10%, 97.90%, 96.09%, and 96.43% for the distance/intersection parameter; 94.03%, 90.63%, 92.61%, and 93.33% for the closure/opening parameter; and 100.00%, 100.00%, 100.00%, and 100.00% for the detected tremor parameter. These results suggest that the mPDT is very robust in differentiating dementia disease subtypes and is able to contribute to clinical practice and field studies. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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Review

Jump to: Research, Other

21 pages, 1610 KiB  
Review
Muscle Synergies in Parkinson’s Disease
by Ilaria Mileti, Alessandro Zampogna, Alessandro Santuz, Francesco Asci, Zaccaria Del Prete, Adamantios Arampatzis, Eduardo Palermo and Antonio Suppa
Sensors 2020, 20(11), 3209; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113209 - 05 Jun 2020
Cited by 20 | Viewed by 5575
Abstract
Over the last two decades, experimental studies in humans and other vertebrates have increasingly used muscle synergy analysis as a computational tool to examine the physiological basis of motor control. The theoretical background of muscle synergies is based on the potential ability of [...] Read more.
Over the last two decades, experimental studies in humans and other vertebrates have increasingly used muscle synergy analysis as a computational tool to examine the physiological basis of motor control. The theoretical background of muscle synergies is based on the potential ability of the motor system to coordinate muscles groups as a single unit, thus reducing high-dimensional data to low-dimensional elements. Muscle synergy analysis may represent a new framework to examine the pathophysiological basis of specific motor symptoms in Parkinson’s disease (PD), including balance and gait disorders that are often unresponsive to treatment. The precise mechanisms contributing to these motor symptoms in PD remain largely unknown. A better understanding of the pathophysiology of balance and gait disorders in PD is necessary to develop new therapeutic strategies. This narrative review discusses muscle synergies in the evaluation of motor symptoms in PD. We first discuss the theoretical background and computational methods for muscle synergy extraction from physiological data. We then critically examine studies assessing muscle synergies in PD during different motor tasks including balance, gait and upper limb movements. Finally, we speculate about the prospects and challenges of muscle synergy analysis in order to promote future research protocols in PD. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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26 pages, 765 KiB  
Review
Quantitative Measurement of Rigidity in Parkinson’s Disease: A Systematic Review
by María del Rosario Ferreira-Sánchez, Marcos Moreno-Verdú and Roberto Cano-de-la-Cuerda
Sensors 2020, 20(3), 880; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030880 - 06 Feb 2020
Cited by 30 | Viewed by 8749
Abstract
Rigidity is one of the cardinal symptoms of Parkinson’s disease (PD). Present in up 89% of cases, it is typically assessed with clinical scales. However, these instruments show limitations due to their subjectivity and poor intra- and inter-rater reliability. To compile all of [...] Read more.
Rigidity is one of the cardinal symptoms of Parkinson’s disease (PD). Present in up 89% of cases, it is typically assessed with clinical scales. However, these instruments show limitations due to their subjectivity and poor intra- and inter-rater reliability. To compile all of the objective quantitative methods used to assess rigidity in PD and to study their validity and reliability, a systematic review was conducted using the Web of Science, PubMed, and Scopus databases. Studies from January 1975 to June 2019 were included, all of which were written in English. The Strengthening the Reporting of observational studies in Epidemiology Statement (STROBE) checklist for observational studies was used to assess the methodological rigor of the included studies. Thirty-six studies were included. Rigidity was quantitatively assessed in three ways, using servomotors, inertial sensors, and biomechanical and neurophysiological study of muscles. All methods showed good validity and reliability, good correlation with clinical scales, and were useful for detecting rigidity and studying its evolution. People with PD exhibit higher values in terms of objective muscle stiffness than healthy controls. Rigidity depends on the angular velocity and articular amplitude of the mobilization applied. There are objective, valid, and reliable methods that can be used to quantitatively assess rigidity in people with PD. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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Other

Jump to: Research, Review

9 pages, 561 KiB  
Brief Report
Step Length Is a Promising Progression Marker in Parkinson’s Disease
by Julius Welzel, David Wendtland, Elke Warmerdam, Robbin Romijnders, Morad Elshehabi, Johanna Geritz, Daniela Berg, Clint Hansen and Walter Maetzler
Sensors 2021, 21(7), 2292; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072292 - 25 Mar 2021
Cited by 10 | Viewed by 2230
Abstract
Current research on Parkinson’s disease (PD) is increasingly concerned with the identification of objective and specific markers to make reliable statements about the effect of therapy and disease progression. Parameters from inertial measurement units (IMUs) are objective and accurate, and thus an interesting [...] Read more.
Current research on Parkinson’s disease (PD) is increasingly concerned with the identification of objective and specific markers to make reliable statements about the effect of therapy and disease progression. Parameters from inertial measurement units (IMUs) are objective and accurate, and thus an interesting option to be included in the regular assessment of these patients. In this study, 68 patients with PD (PwP) in Hoehn and Yahr (H&Y) stages 1–4 were assessed with two gait tasks—20 m straight walk and circular walk—using IMUs. In an ANCOVA model, we found a significant and large effect of the H&Y scores on step length in both tasks, and only a minor effect on step time. This study provides evidence that from the two potentially most important gait parameters currently accessible with wearable technology under supervised assessment strategies, step length changes substantially over the course of PD, while step time shows surprisingly little change in the progression of PD. These results show the importance of carefully evaluating quantitative gait parameters to make assumptions about disease progression, and the potential of the granular evaluation of symptoms such as gait deficits when monitoring chronic progressive diseases such as PD. Full article
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
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