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Special Issue "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: 30 April 2021.

Special Issue Editor

Dr. Markos Tsipouras
Website
Guest Editor
University of Western Macedonia, Kozani, Greece
Interests: biomedical signal processing; medical informatics; IoT in healthcare
Special Issues and Collections 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

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (14 papers)

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Research

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Open AccessArticle
Relationship between Muscular Activity and Postural Control Changes after Proprioceptive Focal Stimulation (Equistasi®) in Middle-Moderate Parkinson’s Disease Patients: An Explorative Study
Sensors 2021, 21(2), 560; https://doi.org/10.3390/s21020560 - 14 Jan 2021
Viewed by 441
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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Open AccessArticle
Automatic Resting Tremor Assessment in Parkinson’s Disease Using Smartwatches and Multitask Convolutional Neural Networks
Sensors 2021, 21(1), 291; https://doi.org/10.3390/s21010291 - 04 Jan 2021
Viewed by 617
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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Open AccessArticle
A Wearable Sensor System to Measure Step-Based Gait Parameters for Parkinson’s Disease Rehabilitation
Sensors 2020, 20(22), 6417; https://doi.org/10.3390/s20226417 - 10 Nov 2020
Cited by 1 | Viewed by 513
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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Open AccessArticle
Instrumental Assessment of Stepping in Place Captures Clinically Relevant Motor Symptoms of Parkinson’s Disease
Sensors 2020, 20(19), 5465; https://doi.org/10.3390/s20195465 - 23 Sep 2020
Cited by 1 | Viewed by 703
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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Open AccessArticle
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
Sensors 2020, 20(18), 5059; https://doi.org/10.3390/s20185059 - 06 Sep 2020
Cited by 1 | Viewed by 628
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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Open AccessArticle
An FPGA Based Tracking Implementation for Parkinson’s Patients
Sensors 2020, 20(11), 3189; https://doi.org/10.3390/s20113189 - 04 Jun 2020
Cited by 1 | Viewed by 674
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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Open AccessArticle
Validity of a Fully-Immersive VR-Based Version of the Box and Blocks Test for Upper Limb Function Assessment in Parkinson’s Disease
Sensors 2020, 20(10), 2773; https://doi.org/10.3390/s20102773 - 13 May 2020
Cited by 4 | Viewed by 1319
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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Open AccessArticle
A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson’s Disease Diagnosis
Sensors 2020, 20(9), 2630; https://doi.org/10.3390/s20092630 - 05 May 2020
Cited by 4 | Viewed by 746
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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Open AccessArticle
Trunk Range of Motion Is Related to Axial Rigidity, Functional Mobility and Quality of Life in Parkinson’s Disease: An Exploratory Study
Sensors 2020, 20(9), 2482; https://doi.org/10.3390/s20092482 - 27 Apr 2020
Cited by 1 | Viewed by 958
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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Open AccessArticle
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
Sensors 2020, 20(8), 2168; https://doi.org/10.3390/s20082168 - 11 Apr 2020
Cited by 8 | Viewed by 1810
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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Open AccessArticle
Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors
Sensors 2020, 20(7), 1895; https://doi.org/10.3390/s20071895 - 29 Mar 2020
Cited by 6 | Viewed by 1780
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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Open AccessArticle
Automatic, Qualitative Scoring of the Interlocking Pentagon Drawing Test (PDT) Based on U-Net and Mobile Sensor Data
Sensors 2020, 20(5), 1283; https://doi.org/10.3390/s20051283 - 27 Feb 2020
Cited by 1 | Viewed by 833
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

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Open AccessReview
Muscle Synergies in Parkinson’s Disease
Sensors 2020, 20(11), 3209; https://doi.org/10.3390/s20113209 - 05 Jun 2020
Cited by 3 | Viewed by 928
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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Open AccessReview
Quantitative Measurement of Rigidity in Parkinson’s Disease: A Systematic Review
Sensors 2020, 20(3), 880; https://doi.org/10.3390/s20030880 - 06 Feb 2020
Cited by 3 | Viewed by 1423
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Automatic Resting-tremor Assessment in Parkinson Disease Through Accelerometer Sensors from Smartwatches
Luis Francisco Sigcha Guachamin, Ignacio Pavon Garcia

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