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Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 25152

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Special Issue Editors

Prof. Dr. Gabriella Olmo

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Guest Editor

Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
Interests: biomedical signal processing; body area networks; neurodegenerative diseases; machine learning; artificial intelligence; telemedicine; diagnostics and follow-up; translational medicine

Dr. Florenc Demrozi

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Guest Editor

Department of Electrical Engineering and Computer Science, University of Stavanger, 41, 4021 Stavanger, Norway
Interests: human recognition; IoT; ambient intelligence; IoMT

Dr. Luigi Borzì

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Guest Editor

Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
Interests: wearable sensors; artificial intelligence; biomedical signal processing; digital health; telemonitoring; machine learning; deep learning; gait analysis; movement analysis; classification; neural networks

Special Issue Information

Dear Colleagues,

Neurodegenerative diseases (such as Alzheimer’s and Parkinson's disease, the most prevalent examples) are continuously on the rise, one of the reasons being the progressive ageing of the population. They represent a human drama for patients and their families, and the economic costs, both for the public health systems and for society in general, are extremely high.

At present, there is no cure for these diseases, and the therapeutic target is to act on the patient's quality of life, prolonging the period of good disease control and preserving the residual autonomy. However, the current follow-up is based on periodically scheduled outpatient visits, 12–18 months apart, and several aspects of the disease are difficult to appreciate in the short time lapse devoted to the visit. The assessment of (at least) motor fluctuations, autonomic dysfunctions, sleep disorders, and cognitive impairments could greatly benefit with a long-time monitoring of the patient at home. The availability of a large amount of patient data measured in their living environment could help identify environmental risk factors, prognostic markers, therapeutic options, and differential diagnosis issues, and lead to each patient being able to receive personalized care and rehabilitation. In this context, electronics represents an essential tool. A plethora of low-cost, low-weight, and low-power wearable sensors could be configured in terms of a Body Area Network (BAN), encompassing sensing, processing, transmission, and the long-term analysis of diverse data.

The aim of this Special Issue is to collect high-quality studies related to recent developments and applications in the field of wearable sensors for neurodegenerative diseases. The topics of interest include, but are not limited to:

The implementation and assessment of wearable sensors (including inertial measurement units, photoplethysmography, electromyography, electrocardiography, electroencephalography, etc.)
Sensor networking, Body Area Networks
Signal acquisition and data collection
Machine/deep learning algorithms for data processing
Practical implementations related to the evaluation of motor and non-motor symptoms of neurodegenerative diseases (voice impairment, sleep disorders, autonomic disfunctions, and behavioral defects), possibly during activities of daily living
Usability issues related to the application of wearable sensor networks on fragile/elderly populations

Dr. Gabriella Olmo
Dr. Florenc Demrozi
Dr. Luigi Borzì
Guest Editors

The technical program committee member is as follow:

Dr. Luigi Borzì
Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy

Dr. Alessandro Gumiero
STMicroelectronics, Agrate Brianza, 20864 MB, Italy

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Keywords

wearable sensors
movement disorders
neurodegenerative disorders
computer-assisted decision support
artificial intelligence
machine/deep learning in healthcare
biomedical signal processing
mobile/remote monitoring of gait, balance, fall risk, sleep disorders, and speech impairment
body area networks

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

4 pages, 177 KiB

Open AccessEditorial

Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases

by Florenc Demrozi, Luigi Borzì and Gabriella Olmo

Electronics 2023, 12(6), 1269; https://doi.org/10.3390/electronics12061269 - 7 Mar 2023

Cited by 3 | Viewed by 1190

Abstract

The incidence of neurodegenerative disorders (NDs) is increasing in an aging population [...] Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

Research

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19 pages, 2572 KiB

Open AccessArticle

Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRI

by Sambath Kumar Sethuraman, Nandhini Malaiyappan, Rajakumar Ramalingam, Shakila Basheer, Mamoon Rashid and Nazir Ahmad

Electronics 2023, 12(4), 1031; https://doi.org/10.3390/electronics12041031 - 19 Feb 2023

Cited by 12 | Viewed by 2666

Abstract

Resting-state functional connectivity has been widely used for the past few years to forecast Alzheimer’s disease (AD). However, the conventional correlation calculation does not consider different frequency band features that may hold the brain atrophies’ original functional connectivity relationships. Previous works focuses on low-order neurodynamics and precisely manipulates the mono-band frequency span of resting-state functional magnetic imaging (rs-fMRI). They specifically use the mono-band frequency span of rs-fMRI, leaving out the high-order neurodynamics. By creating a high-order neuro-dynamic functional network employing several levels of rs-fMRI time-series data, such as slow4, slow5, and full-band ranges of (0.027 to 0.08 Hz), (0.01 to 0.027 Hz), and (0.01 to 0.08 Hz), we suggest an automated AD diagnosis system to address these challenges. It combines multiple customized deep learning models to provide unbiased evaluation, and a tenfold cross-validation is observed We have determined that to differentiate AD disorders from NC, the entire band ranges and slow4 and slow5, referred to as higher and lower frequency band approaches, are applied. The first method uses the SVM and KNN to deal with AD diseases. The second method uses the customized Alexnet and Inception blocks with rs-fMRI datasets from the ADNI organizations. We also tested the other machine learning and deep learning approaches by modifying various parameters and attained good accuracy levels. Our proposed model achieves good performance using three bands without any external feature selection. The results show that our system performance of accuracy (96.61%)/AUC (0.9663) is achieved in differentiating the AD subjects from normal controls. Furthermore, the good accuracies in classifying multiple stages of AD show the potentiality of our method for the clinical value of AD prediction. Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

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21 pages, 10860 KiB

Open AccessArticle

Towards Posture and Gait Evaluation through Wearable-Based Biofeedback Technologies

by Paola Cesari, Matteo Cristani, Florenc Demrozi, Francesco Pascucci, Pietro Maria Picotti, Graziano Pravadelli, Claudio Tomazzoli, Cristian Turetta, Tewabe Chekole Workneh and Luca Zenti

Electronics 2023, 12(3), 644; https://doi.org/10.3390/electronics12030644 - 28 Jan 2023

Cited by 3 | Viewed by 2074

Abstract

In medicine and sport science, postural evaluation is an essential part of gait and posture correction. There are various instruments for quantifying the postural system’s efficiency and determining postural stability which are considered state-of-the-art. However, such systems present many limitations related to accessibility, economic cost, size, intrusiveness, usability, and time-consuming set-up. To mitigate these limitations, this project aims to verify how wearable devices can be assembled and employed to provide feedback to human subjects for gait and posture improvement, which could be applied for sports performance or motor impairment rehabilitation (from neurodegenerative diseases, aging, or injuries). The project is divided into three parts: the first part provides experimental protocols for studying action anticipation and related processes involved in controlling posture and gait based on state-of-the-art instrumentation. The second part provides a biofeedback strategy for these measures concerning the design of a low-cost wearable system. Finally, the third provides algorithmic processing of the biofeedback to customize the feedback based on performance conditions, including individual variability. Here, we provide a detailed experimental design that distinguishes significant postural indicators through a conjunct architecture that integrates state-of-the-art postural and gait control instrumentation and a data collection and analysis framework based on low-cost devices and freely accessible machine learning techniques. Preliminary results on 12 subjects showed that the proposed methodology accurately recognized the phases of the defined motor tasks (i.e., rotate, in position, APAs, drop, and recover) with overall F1-scores of 89.6% and 92.4%, respectively, concerning subject-independent and subject-dependent testing setups. Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

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11 pages, 885 KiB

Open AccessArticle

Age-Associated Changes on Gait Smoothness in the Third and the Fourth Age

by Massimiliano Pau, Giuseppina Bernardelli, Bruno Leban, Micaela Porta, Valeria Putzu, Daniela Viale, Gesuina Asoni, Daniela Riccio, Serena Cerfoglio, Manuela Galli and Veronica Cimolin

Electronics 2023, 12(3), 637; https://doi.org/10.3390/electronics12030637 - 27 Jan 2023

Cited by 3 | Viewed by 1323

Abstract

Although gait disorders represent a highly prevalent condition in older adults, the alterations associated with physiologic aging are often not easily differentiable from those originated by concurrent neurologic or orthopedic conditions. Thus, the detailed quantitative assessment of gait patterns represents a crucial issue. In this context, the study of trunk accelerations may represent an effective proxy of locomotion skills in terms of symmetry. This can be carried out by calculating the Harmonic Ratio (HR), a parameter obtained through the processing of trunk accelerations in the frequency domain. In this study, trunk accelerations during level walking of 449 healthy older adults (of age > 65) who were stratified into three groups (Group 1: 65–74 years, n = 175; Group 2: 75–85 years, n = 227; Group 3: >85 years, n = 47) were acquired by means of a miniaturized Inertial Measurement Unit located in the low back and processed to obtain spatio-temporal parameters of gait and HR, in antero-posterior (AP), medio-lateral (ML) and vertical (V) directions. The results show that Group 3 exhibited a 16% reduction in gait speed and a 10% reduction in stride length when compared with Group 1 (p < 0.001 in both cases). Regarding the cadence, Group 3 was characterized by a 5% reduction with respect to Groups 1 and 2 (p < 0.001 in both cases). The analysis of HR revealed a general trend of linear decrease with age in the three groups. In particular, Group 3 was characterized by HR values significantly lower (−17%) than those of Group 1 in all three directions and significantly lower than Group 2 in ML and V directions (−10%). Taken together, such results suggest that HR may represent a valid measure to quantitatively characterize the progressive deterioration of locomotor abilities associated with aging, which seems to occur until the late stages of life. Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

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14 pages, 2861 KiB

Open AccessFeature PaperArticle

Harmonic Distortion Aspects in Upper Limb Swings during Gait in Parkinson’s Disease

by Luca Pietrosanti, Alexandre Calado, Cristiano Maria Verrelli, Antonio Pisani, Antonio Suppa, Francesco Fattapposta, Alessandro Zampogna, Martina Patera, Viviana Rosati, Franco Giannini and Giovanni Saggio

Electronics 2023, 12(3), 625; https://doi.org/10.3390/electronics12030625 - 27 Jan 2023

Cited by 3 | Viewed by 1298

Abstract

Parkinson’s disease (PD) is responsible for a broad spectrum of signs and symptoms, including relevant motor impairments generally rated by clinical experts. In recent years, motor measurements gathered by technology-based systems have been used more and more to provide objective data. In particular, wearable devices have been adopted to evidence differences in the gait capabilities between PD patients and healthy people. Within this frame, despite the key role that the upper limbs’ swing plays during walking, no studies have been focused on their harmonic content, to which this work is devoted. To this end, we measured, by means of IMU sensors, the walking capabilities of groups of PD patients (both de novo and under-chronic-dopaminergic-treatment patients when in an off-therapy state) and their healthy counterparts. The collected data were FFT transformed, and the frequency content was analyzed. According to the results obtained, PD determines upper limb rigidity objectively evidenced and correlated to lower harmonic contents. Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

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20 pages, 1860 KiB

Open AccessArticle

EdgeTrust: A Lightweight Data-Centric Trust Management Approach for IoT-Based Healthcare 4.0

by Kamran Ahmad Awan, Ikram Ud Din, Ahmad Almogren, Hasan Ali Khattak and Joel J. P. C. Rodrigues

Electronics 2023, 12(1), 140; https://doi.org/10.3390/electronics12010140 - 28 Dec 2022

Cited by 8 | Viewed by 1479

Abstract

Internet of Things (IoT) is bringing a revolution in today’s world where devices in our surroundings become smart and perform daily-life activities and operations with more precision. The architecture of IoT is heterogeneous, providing autonomy to nodes so that they can communicate with other nodes and exchange information at any time. IoT and healthcare together provide notable facilities for patient monitoring. However, one of the most critical challenges is the identification of malicious and compromised nodes. In this article, we propose a machine learning-based trust management approach for edge nodes to identify nodes with malicious behavior. The proposed mechanism utilizes knowledge and experience components of trust, where knowledge is further based on several parameters. To prevent the successful execution of good and bad-mouthing attacks, the proposed approach utilizes edge clouds, i.e., local data centers, to collect recommendations to evaluate indirect and aggregated trust. The trustworthiness of nodes is ranked between a certain limit, and only those nodes that satisfy the threshold value can participate in the network. To validate the performance of the proposed approach, we have performed extensive simulations in comparison with existing approaches. The results show the effectiveness of the proposed approach against several potential attacks. Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

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19 pages, 1069 KiB

Open AccessArticle

Bradykinesia Detection in Parkinson’s Disease Using Smartwatches’ Inertial Sensors and Deep Learning Methods

by Luis Sigcha, Beatriz Domínguez, Luigi Borzì, Nélson Costa, Susana Costa, Pedro Arezes, Juan Manuel López, Guillermo De Arcas and Ignacio Pavón

Electronics 2022, 11(23), 3879; https://doi.org/10.3390/electronics11233879 - 24 Nov 2022

Cited by 8 | Viewed by 2986

Abstract

Bradykinesia is the defining motor symptom of Parkinson’s disease (PD) and is reflected as a progressive reduction in speed and range of motion. The evaluation of bradykinesia severity is important for assessing disease progression, daily motor fluctuations, and therapy response. However, the clinical evaluation of PD motor signs is affected by subjectivity, leading to intra- and inter-rater variability. Moreover, the clinical assessment is performed a few times a year during pre-scheduled follow-up visits. To overcome these limitations, objective and unobtrusive methods based on wearable motion sensors and machine learning (ML) have been proposed, providing promising results. In this study, the combination of inertial sensors embedded in consumer smartwatches and different ML models is exploited to detect bradykinesia in the upper extremities and evaluate its severity. Six PD subjects and seven age-matched healthy controls were equipped with a consumer smartwatch and asked to perform a set of motor exercises for at least 6 weeks. Different feature sets, data representations, data augmentation methods, and ML models were implemented and combined. Data recorded from smartwatches’ motion sensors, properly augmented and fed to a combination of Convolutional Neural Network and Random Forest model, provided the best results, with an accuracy of 0.86 and an area under the curve (AUC) of 0.94. Results suggest that the combination of consumer smartwatches and ML classification methods represents an unobtrusive solution for the detection of bradykinesia and the evaluation of its severity. Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

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20 pages, 2093 KiB

Open AccessArticle

An Efficient Machine Learning Approach for Diagnosing Parkinson’s Disease by Utilizing Voice Features

by Arti Rana, Ankur Dumka, Rajesh Singh, Mamoon Rashid, Nazir Ahmad and Manoj Kumar Panda

Electronics 2022, 11(22), 3782; https://doi.org/10.3390/electronics11223782 - 17 Nov 2022

Cited by 12 | Viewed by 2917

Abstract

Parkinson’s disease (PD) is a neurodegenerative disease that impacts the neural, physiological, and behavioral systems of the brain, in which mild variations in the initial phases of the disease make precise diagnosis difficult. The general symptoms of this disease are slow movements known as ‘bradykinesia’. The symptoms of this disease appear in middle age and the severity increases as one gets older. One of the earliest signs of PD is a speech disorder. This research proposed the effectiveness of using supervised classification algorithms, such as support vector machine (SVM), naïve Bayes, k-nearest neighbor (K-NN), and artificial neural network (ANN) with the subjective disease where the proposed diagnosis method consists of feature selection based on the filter method, the wrapper method, and classification processes. Since just a few clinical test features would be required for the diagnosis, a method such as this might reduce the time and expense associated with PD screening. The suggested strategy was compared to PD diagnostic techniques previously put forward and well-known classifiers. The experimental outcomes show that the accuracy of SVM is 87.17%, naïve Bayes is 74.11%, ANN is 96.7%, and KNN is 87.17%, and it is concluded that the ANN is the most accurate one with the highest accuracy. The obtained results were compared with those of previous studies, and it has been observed that the proposed work offers comparable and better results. Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

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17 pages, 2163 KiB

Open AccessArticle

Classical FE Analysis to Classify Parkinson’s Disease Patients

by Nestor Rafael Calvo-Ariza, Luis Felipe Gómez-Gómez and Juan Rafael Orozco-Arroyave

Electronics 2022, 11(21), 3533; https://doi.org/10.3390/electronics11213533 - 29 Oct 2022

Cited by 2 | Viewed by 1283

Abstract

Parkinson’s disease (PD) is a neurodegenerative condition that affects the correct functioning of the motor system in the human body. Patients exhibit a reduced capability to produce facial expressions (FEs) among different symptoms, namely hypomimia. Being a disease so hard to be detected in its early stages, automatic systems can be created to help physicians in assessing and screening patients using basic bio-markers. In this paper, we present several experiments where features are extracted from images of FEs produced by PD patients and healthy controls. Classical machine learning methods such as local binary patterns and histograms of oriented gradients are used to model the images. Similarly, a well-known classification method, namely support vector machine is used for the discrimination between PD patients and healthy subjects. The most informative regions of the faces are found with a principal component analysis algorithm. Three different FEs were modeled: angry, happy, and surprise. Good results were obtained in most of the cases; however, happiness was the one that yielded better results, with accuracies of up to 80.4%. The methods used in this paper are classical and well-known by the research community; however, their main advantage is that they provide clear interpretability, which is valuable for many researchers and especially for clinicians. This work can be considered as a good baseline such that motivates other researchers to propose new methodologies that yield better results while keep the characteristic of providing interpretability. Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

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11 pages, 2136 KiB

Open AccessArticle

Systolic Blood Pressure Estimation from PPG Signal Using ANN

by Benedetta C. Casadei, Alessandro Gumiero, Giorgio Tantillo, Luigi Della Torre and Gabriella Olmo

Electronics 2022, 11(18), 2909; https://doi.org/10.3390/electronics11182909 - 14 Sep 2022

Cited by 8 | Viewed by 2332

Abstract

High blood pressure is one of the most important precursors for Cardiovascular Diseases (CVDs), the most common cause of death in 2020, as reported by the World Health Organization (WHO). Moreover, many patients affected by neurodegenerative diseases (e.g., Parkinson’s Disease) exhibit impaired autonomic control, with inversion of the normal circadian arterial pressure cycle, and consequent augmented cardiovascular and fall risk. For all these reasons, a continuous pressure monitoring of these patients could represent a significant prognostic factor, and help adjusting their therapy. However, the existing cuff-based methods cannot provide continuous blood pressure readings. Our work is inspired by the newest approaches based on the photoplethysmographic (PPG) signal only, which has been used to continuously estimate systolic blood pressure (SP), using artificial neural networks (ANN), in order to create more compact and wearable devices. Our first database was derived from the PhysioNet resource; we extracted PPG and arterial blood pressure (ABP) signals, collected at a sampling frequency of 125 Hz, in a hospital environment. It consists of 249,672 PPG periods and the relative SP values. The second database was collected at STMicroelectronics s.r.l., in Agrate Brianza, using the MORFEA3 wearable device and a digital cuff-based sphygmomanometer, as reference. The pre-processing phase, in order to remove noise and motion artifacts and to segment the signal into periods, was carried out on Matlab R2019b. The noise removal was one of the challenging parts of the study because of the inaccuracy of the PPG signal during everyday-life activity, and this is the reason why the MORFEA3 dataset was acquired in a controlled environment in a static position. Different solutions were implemented to choose the input features that best represent the period morphology. The first database was used to train the multilayer feed-forward neural network with a back-propagation model, whereas the second one was used to test it. The results obtained in this project are promising and match the Association for the Advancement of Medical Instruments (AAMI) and the British Hypertension Society (BHS) standards. They show a Mean Absolute Error of 3.85 mmHg with a Standard Deviation of 4.29 mmHg, under the AAMI standard, and reach the grade A under the BHS standard. Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

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17 pages, 36065 KiB

Open AccessArticle

Classification of Parkinson’s Disease Patients—A Deep Learning Strategy

by Helber Andrés Carvajal-Castaño, Paula Andrea Pérez-Toro and Juan Rafael Orozco-Arroyave

Electronics 2022, 11(17), 2684; https://doi.org/10.3390/electronics11172684 - 27 Aug 2022

Cited by 5 | Viewed by 1765

Abstract

(1) Background and objectives: Parkinson’s disease (PD) is one of the most prevalent neurodegenerative diseases whose typical symptoms include bradykinesia, abnormal gait and posture, shortened strides, and other movement disorders. In this study, we present a novel framework to evaluate PD gait patterns using state of the art deep learning algorithms. A comparative analysis with three different approaches is presented and evaluated upon three groups of subjects: PD patients, Young Healthy Controls (YHC), and Elderly Healthy Controls (EHC). (2) Methods: The three approaches used in the study include: (i) The energy content of the gait signals in the frequency domain is captured with spectrograms that are used to feed a CNN model, (ii) Temporal information is incorporated by creating GRU networks, (iii) Temporal and spectral information is simultaneously captured by creating a new architecture based on CNNs and GRUs. (3) Results: Accuracies of up to 83.7% and 92.7% are found for the classification between PD vs. EHC and PD vs. YHC, respectively. According to our observations, the proposed approach based on the combination of temporal and spectral information, yields better results than others reported in the state of the art. (4) Conclusions: The results obtained in this study suggest that the combination of temporal and spectral information is more accurate than individual approaches used to classify and evaluate gait patterns in PD patients. To the best of our knowledge, this is the first study in gait analysis where temporal and spectral information is combined in an architecture of deep learning. Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

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Review

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25 pages, 781 KiB

Open AccessReview

New Perspectives in Nonintrusive Sleep Monitoring for Neurodegenerative Diseases—A Narrative Review

by Giulia Masi, Gianluca Amprimo, Lorenzo Priano and Claudia Ferraris

Electronics 2023, 12(5), 1098; https://doi.org/10.3390/electronics12051098 - 22 Feb 2023

Cited by 3 | Viewed by 2103

Abstract

Good sleep quality is of primary importance in ensuring people’s health and well-being. In fact, sleep disorders have well-known adverse effects on quality of life, as they influence attention, memory, mood, and various physiological regulatory body functions. Sleep alterations are often strictly related to age and comorbidities. For example, in neurodegenerative diseases, symptoms may be aggravated by alterations in sleep cycles or, vice versa, may be the cause of sleep disruption. Polysomnography is the primary instrumental method to investigate sleep diseases; however, its use is limited to clinical practice. This review aims to provide a comprehensive overview of the available innovative technologies and methodologies proposed for less invasive sleep-disorder analysis, with a focus on neurodegenerative disorders. The paper intends to summarize the main studies, selected between 2010 and 2022, from different perspectives covering three relevant contexts, the use of wearable and non-wearable technologies, and application to specific neurodegenerative diseases. In addition, the review provides a qualitative summary for each selected article concerning the objectives, instrumentation, metrics, and impact of the results obtained, in order to facilitate the comparison among methodological approaches and overall findings. Full article

(This article belongs to the Special Issue Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases)

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