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Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders 2022

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

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 13225

Special Issue Editors

Prof. Dr. Antonio Suppa

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

E-Mail Website
Guest Editor
Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy
Interests: wearable electronics; More-than-Moore integration; nanoelectronics; CMOS device reliability; CMOS image sensors; innovative non-volatile memories
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Joan Cabestany

E-Mail Website
Guest Editor
ISSET Research Group (Integrated Smart Sensors and Health Technologies), Department of Electronic Engineering, Universitat Politcnica de Catalunya, 08034 Barcelona, Spain
Interests: movement diseases; people with gait problems; the application of electronic and communication engineering in Parkinson Disease; identification and measurement of Parkinson Disease-related symptoms and falls
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The COVID-19-related pandemic is boosting relevant advances in the field of telemedicine owing to the increasing application of new health technologies for remote recording of specific biological variables. In this frame, the automatic recognition through wearable sensors of specific neurological disorders is gaining tremendous advances in teleneurology. We believe that shortly, wearable technologies will likely help the clinicians in the follow-up evaluation and in tailoring therapeutic strategies for people manifesting gait and balance abnormalities in the context of neurological disorders such as Parkinson’s disease. The present Special Issue entitled “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders 2022” can be considered an updated version of our previously published collection of research articles. Again, we welcome research studies as well as review manuscripts focusing on the application of wearables for the objective recognition of gait and balance abnormalities in people with various neurological disorders including Parkinson’s disease, stroke, multiple sclerosis, neuromuscular disorders etc. We particularly warrant studies concerning relevant methodological advances in the field including those based on the application of artificial intelligence for remote and objective recognition of gait and balance abnormalities in the context of neurological disorders.

Prof. Dr. Antonio Suppa
Prof. Dr. Fernanda Irrera
Prof. Dr. Joan Cabestany
Guest Editors

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 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wearable sensors
  • IMU
  • gait
  • balance
  • Parkinson’s disease
  • movement disorders
  • neurology
  • real time monitoring
  • longitudinal monitoring
  • home monitoring

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Published Papers (4 papers)

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Research

20 pages, 1048 KiB  
Article
Can Gait Features Help in Differentiating Parkinson’s Disease Medication States and Severity Levels? A Machine Learning Approach
by Chariklia Chatzaki, Vasileios Skaramagkas, Zinovia Kefalopoulou, Nikolaos Tachos, Nicholas Kostikis, Foivos Kanellos, Eleftherios Triantafyllou, Elisabeth Chroni, Dimitrios I. Fotiadis and Manolis Tsiknakis
Sensors 2022, 22(24), 9937; https://doi.org/10.3390/s22249937 - 16 Dec 2022
Cited by 8 | Viewed by 3301
Abstract
Parkinson’s disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a [...] Read more.
Parkinson’s disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society—Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively. Full article
(This article belongs to the Special Issue Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders 2022)
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15 pages, 2307 KiB  
Article
A Digital Architecture for the Real-Time Tracking of Wearing off Phenomenon in Parkinson’s Disease
by Giovanni Mezzina and Daniela De Venuto
Sensors 2022, 22(24), 9753; https://doi.org/10.3390/s22249753 - 13 Dec 2022
Viewed by 1884
Abstract
Levodopa administration is currently the most common treatment to alleviate Parkinson’s Disease (PD) symptoms. Nevertheless, prolonged use of Levodopa leads to a wearing-off (WO) phenomenon, causing symptoms to reappear. To build a personalized treatment plan aiming to manage PD and its symptoms effectively, [...] Read more.
Levodopa administration is currently the most common treatment to alleviate Parkinson’s Disease (PD) symptoms. Nevertheless, prolonged use of Levodopa leads to a wearing-off (WO) phenomenon, causing symptoms to reappear. To build a personalized treatment plan aiming to manage PD and its symptoms effectively, there is a need for a technological system able to continuously and objectively assess the WO phenomenon during daily life. In this context, this paper proposes a WO tracker able to exploit neuromuscular data acquired by a dedicated wireless sensor network to discriminate between a Levodopa benefit phase and the reappearance of symptoms. The proposed architecture has been implemented on a heterogeneous computing platform, that statistically analyzes neural and muscular features to identify the best set of features to train the classifier model. Eight models among shallow and deep learning approaches are analyzed in terms of performance, timing and complexity metrics to identify the best inference engine. Experimental results on five subjects experiencing WO, showed that, in the best case, the proposed WO tracker can achieve an accuracy of ~84%, providing the inference in less than 41 ms. It is possible by employing a simple fully-connected neural network with 1 hidden layer and 32 units. Full article
(This article belongs to the Special Issue Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders 2022)
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21 pages, 2194 KiB  
Article
Balance Impairments in People with Early-Stage Multiple Sclerosis: Boosting the Integration of Instrumented Assessment in Clinical Practice
by Ilaria Carpinella, Denise Anastasi, Elisa Gervasoni, Rachele Di Giovanni, Andrea Tacchino, Giampaolo Brichetto, Paolo Confalonieri, Marco Rovaris, Claudio Solaro, Maurizio Ferrarin and Davide Cattaneo
Sensors 2022, 22(23), 9558; https://doi.org/10.3390/s22239558 - 6 Dec 2022
Cited by 11 | Viewed by 3469
Abstract
The balance of people with multiple sclerosis (PwMS) is commonly assessed during neurological examinations through clinical Romberg and tandem gait tests that are often not sensitive enough to unravel subtle deficits in early-stage PwMS. Inertial sensors (IMUs) could overcome this drawback. Nevertheless, IMUs [...] Read more.
The balance of people with multiple sclerosis (PwMS) is commonly assessed during neurological examinations through clinical Romberg and tandem gait tests that are often not sensitive enough to unravel subtle deficits in early-stage PwMS. Inertial sensors (IMUs) could overcome this drawback. Nevertheless, IMUs are not yet fully integrated into clinical practice due to issues including the difficulty to understand/interpret the big number of parameters provided and the lack of cut-off values to identify possible abnormalities. In an attempt to overcome these limitations, an instrumented modified Romberg test (ImRomberg: standing on foam with eyes closed while wearing an IMU on the trunk) was administered to 81 early-stage PwMS and 38 healthy subjects (HS). To facilitate clinical interpretation, 21 IMU-based parameters were computed and reduced through principal component analysis into two components, sway complexity and sway intensity, descriptive of independent aspects of balance, presenting a clear clinical meaning and significant correlations with at least one clinical scale. Compared to HS, early-stage PwMS showed a 228% reduction in sway complexity and a 63% increase in sway intensity, indicating, respectively, a less automatic (more conscious) balance control and larger and faster trunk movements during upright posture. Cut-off values were derived to identify the presence of balance abnormalities and if these abnormalities are clinically meaningful. By applying these thresholds and integrating the ImRomberg test with the clinical tandem gait test, balance impairments were identified in 58% of PwMS versus the 17% detected by traditional Romberg and tandem gait tests. The higher sensitivity of the proposed approach would allow for the direct identification of early-stage PwMS who could benefit from preventive rehabilitation interventions aimed at slowing MS-related functional decline during neurological examinations and with minimal modifications to the tests commonly performed. Full article
(This article belongs to the Special Issue Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders 2022)
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15 pages, 2922 KiB  
Article
Parkinson’s Disease Wearable Gait Analysis: Kinematic and Dynamic Markers for Diagnosis
by Lazzaro di Biase, Luigi Raiano, Maria Letizia Caminiti, Pasquale Maria Pecoraro and Vincenzo Di Lazzaro
Sensors 2022, 22(22), 8773; https://doi.org/10.3390/s22228773 - 13 Nov 2022
Cited by 15 | Viewed by 3585
Abstract
Introduction: Gait features differ between Parkinson’s disease (PD) and healthy subjects (HS). Kinematic alterations of gait include reduced gait speed, swing time, and stride length between PD patients and HS. Stride time and swing time variability are increased in PD patients with respect [...] Read more.
Introduction: Gait features differ between Parkinson’s disease (PD) and healthy subjects (HS). Kinematic alterations of gait include reduced gait speed, swing time, and stride length between PD patients and HS. Stride time and swing time variability are increased in PD patients with respect to HS. Additionally, dynamic parameters of asymmetry of gait are significantly different among the two groups. The aim of the present study is to evaluate which kind of gait analysis (dynamic or kinematic) is more informative to discriminate PD and HS gait features. Methods: In the present study, we analyzed gait dynamic and kinematic features of 108 PD patients and 88 HS from four cohorts of two datasets. Results: Kinematic features showed statistically significant differences among PD patients and HS for gait speed and time Up and Go test and for selected kinematic dispersion indices (standard deviation and interquartile range of swing, stance, and double support time). Dynamic features did not show any statistically significant difference between PD patients and HS. Discussion: Despite kinematics features like acceleration being directly proportional to dynamic features like ground reaction force, the results of this study showed the so-called force/rhythm dichotomy since kinematic features were more informative than dynamic ones. Full article
(This article belongs to the Special Issue Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders 2022)
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