The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia— we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction

Fifteen years of wireless sensors for balance assessment in neurological disorders

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined

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

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

High-tech aid tool to monitor postural stability in Parkinson’s Disease

Dissertação de mestrado integrado em Engenharia BiomédicaParkinson’s disease (PD) is a neurodegenerative disease that affects around 1% of the population over 65 and has increased in prevalence in recent years. One of the most disabling motor symptoms and a major contributor to falls is postural instability, which threatens the independence and well-being of people with PD. Usually, physicians assess this symptom with a traditional clinical examination named pull test, which, although easy to administer without requiring any instruments, it is a difficult test to standardize and lacks sensitivity to small but significant changes. Thus, other approaches based on high technologies have emerged to provide objective metrics and long-term data on postural stability, complementing clinical assessment. Wearable sensors appeared as a promising tech-based solution to better capture postural instability and eliminate the subjectivity of postural-associated clinical examinations. This dissertation proposes the design, development and validation of a postural assessment tool to perform more objective evaluations of postural instability during basic dynamic day-to-day activities. To achieve this goal, the following steps were accomplished: (i) create a dataset based on 3D motion data of PD patients performing the pull test and dynamic activities using an inertial measurement unit (IMU); (ii) extract relevant features from the data collected, conduct an extensive statistical search, and find correlations to clinical scales; (iii) implement a tool based in artificial intelligence (AI) to classify the level of postural instability through the data collected. Different deep learning models were designed and several combinations of data input were considered in order to find the best model to predict the pull test score. Overall, satisfactory results were achieved as the statistical analysis revealed that many features were considered relevant to distinguish between the scores of the pull test, for diagnostic purposes and also to differentiate the several stages of the disease and levels of motor disability. Regarding the AI-based tool, the results suggest that the combination of IMU-based data with deep learning may be a promising solution for postural instability assessment. The model that achieved the best performance in the testing phase with unseen data presented an accuracy, precision, recall and F1-score of approximately 0.86. The results also show that when fewer daily activities are included in the dataset, the complexity of the model reduces, making it more efficient. Despite the promising results, more data should be collected to assess the actual performance of the model as a postural assessment tool.A doença de Parkinson (DP) é uma doença neurodegenerativa que afeta cerca de 1% da população acima de 65 anos e cuja prevalência tem aumentado nos últimos anos. Um dos sintomas motores mais incapacitantes e um dos principais contribuintes para quedas é a instabilidade postural, que ameaça a independência e o bem-estar das pessoas com a DP. Normalmente, o teste utilizado para avaliar a instabilidade postural é o pull test, que, embora fácil de executar e não necessitando de qualquer instrumento, é um teste difícil de padronizar e com falta de sensibilidade para detetar pequenas alterações que podem ser significativas. Assim, os sensores vestíveis surgiram como uma solução promissora para capturar a instabilidade postural e eliminar a subjetividade dos exames clínicos associados à postura. Esta dissertação tem como objetivo o idealizar, desenvolver e validar um instrumento para realizar avaliações mais objetivas da instabilidade postural durante atividades dinâmicas básicas do dia-a-dia. Para atingir esse objetivo, as seguintes etapas foram realizadas: (i) criar um dataset baseado em dados de movimento 3D de pacientes com a DP enquanto executam o pull test e atividades dinâmicas através de uma unidade de medida inercial; (ii) extrair características relevantes dos dados adquiridos, realizar uma extensa pesquisa estatística e encontrar correlações com escalas clínicas; (iii) implementar uma ferramenta baseada em inteligência artificial (IA) para classificar o nível de instabilidade postural através dos dados recolhidos. É de notar que diferentes frameworks de deep learning foram projetados e vários datasets foram considerados de modo a encontrar o melhor modelo para prever a pontuação da escala do pull test. No geral, os resultados alcançados foram satisfatórios, pois o estudo estatístico revelou que muitas das características extraidas dos sinais recolhidos foram consideradas relevantes para distinguir entre as pontuações do pull test, para fins diagnósticos e também para diferenciar os estágios da doença e os níveis de incapacidade motora. Em relação à ferramenta baseada em IA, os resultados apresentados sugerem que o deep learning pode ser promissor na área de avaliação de instabilidade postural através de IMUs. O modelo que obteve o melhor desempenho apresentou uma exatidão, precisão, sensibilidade e F1-score no teste de aproximadamente 0.86. Os resultados também mostram que dataset com um menor número de actividades diferentes incluídas leva a que o modelo se torne menos complexo, tornando-o mais eficiente. Apesar dos resultados promissores, mais dados devem ser recolhidos para avaliar o real desempenho do modelo como ferramenta de avaliação postural

Diagnosis of Neurogenetic Disorders: Contribution of Next Generation Sequencing and Deep Phenotyping

The contribution of genomic variants to the aetiopathogenesis of both paediatric and adult neurological disease is being increasingly recognized. The use of next-generation sequencing has led to the discovery of novel neurodevelopmental disorders, as exemplified by the deciphering developmental disorders (DDD) study, and provided insight into the aetiopathogenesis of common adult neurological diseases. Despite these advances, many challenges remain. Correctly classifying the pathogenicity of genomic variants from amongst the large number of variants identified by next-generation sequencing is recognized as perhaps the major challenge facing the field. Deep phenotyping (e.g., imaging, movement analysis) techniques can aid variant interpretation by correctly classifying individuals as affected or unaffected for segregation studies. The lack of information on the clinical phenotype of novel genetic subtypes of neurological disease creates limitations for genetic counselling. Both deep phenotyping and qualitative studies can capture the clinical and patient’s perspective on a disease and provide valuable information. This Special Issue aims to highlight how next-generation sequencing techniques have revolutionised our understanding of the aetiology of brain disease and describe the contribution of deep phenotyping studies to a variant interpretation and understanding of natural history

The use of inertial measurement units for the determination of gait spatio-temporal parameters

The aim of this work was to develop a methodology whereby inertial measurement units (IMUs) could be used to obtain accurate and objective gait parameters within typical developed adults (TDA) and Parkinson’s disease (PD). The thesis comprised four studies, the first establishing the validity of the IMU method when measuring the vertical centre of mass (CoM) acceleration, velocity and position versus an optical motion capture system (OMCS) in TDA. The second study addressed the validity of the IMU and inverted pendulum model measurements within PD and also explored the inter-rater reliability of the measurement. In the third study the optimisation of the inverted pendulum model driven by IMU data was explored when comparing to standardised clinical tests within TDA and PD, and the fourth explored a novel phase plot analysis applied to CoM movement to explore gait in more detail. The validity study showed no significant difference for vertical acceleration and position between IMU and OMCS measurements within TDA. Vertical velocity however did show a significant difference, but the error was still less than 2.5%. ICCs for all three parameters ranged from 0.782 to 0.952, indicating an adequate test-retest reliability. Within PD there was no significant difference found for vertical CoM acceleration, velocity and position. ICCs for all three parameters ranged from 0.77 to 0.982. In addition, the reliability calculations found no difference for step time, stride length and walking speed for people with PD. Inter-rater reliability was found not to be different for the same parameters. The optimisation of the correction factor when using the inverted pendulum model showed no significant difference between TDA and PD. Furthermore the correction factor was found not to be related to walking speed. The fourth and final study found that phase plot analysis of variability could be performed on CoM vertical excursion. TDA and PD were shown to have, on average, different characteristics. This thesis demonstrated that CoM motion can be objectively measured within a clinical setting in people with PD by utilizing IMUs. Furthermore, in depth gait variability analysis can be performed by utilizing a phase plot method

The influence of the context on mobility in neurological disorders: a wearable technology approach

The evaluation of mobility of patients with neurodegenerative diseases is crucial for healthcare professionals to tailor individual treatments and track disease progression. More individualized treatment has high potential to improve quality of life and mobility, and decrease fall risk. Nowadays mobility is mainly assessed during clinical examinations. However, with the rise of digital wearable technology, it has become possible to quantify mobility objectively in different settings. It is however unclear how mobility data collected in different settings, or more general different contexts, are associated with each other. Therefore, the aim of this dissertation was to understand the influence of context on mobility in older adults and patients with neurodegenerative disorders. The first study revealed that supervised capacity and unsupervised performance measures can substantially differ from each other. Consequently, both measures provide complementary information that can be used to gain a better understanding of daily function. In the second study an algorithm to quantify arm swing was developed and validated. This algorithm was used in the third study, which showed that the effect of dopaminergic medication on arm swing in patients with PD is influenced by medication state and task complexity. We therefore highly recommend to assess patients in different contexts to get a better understanding of the effect of treatment or the disease progression. To be able to assess mobility of patients in different context more wearable sensor-based algorithms are required. With the dataset introduced in the last paper, an indefinite number of additional movement and mobility algorithms can be developed and validated. The development and validation of these algorithms can further move our understanding of the influence of context on mobility forward

Motor, cognitive and mobility deficits in 1000 geriatric patients : protocol of a quantitative observational study before and after routine clinical geriatric treatment – the ComOn-study

© The Author(s). 2020 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Background: Motor and cognitive deficits and consequently mobility problems are common in geriatric patients. The currently available methods for diagnosis and for the evaluation of treatment in this vulnerable cohort are limited. The aims of the ComOn (COgnitive and Motor interactions in the Older populatioN) study are (i) to define quantitative markers with clinical relevance for motor and cognitive deficits, (ii) to investigate the interaction between both motor and cognitive deficits and (iii) to assess health status as well as treatment outcome of 1000 geriatric inpatients in hospitals of Kiel (Germany), Brescia (Italy), Porto (Portugal), Curitiba (Brazil) and Bochum (Germany). Methods: This is a prospective, explorative observational multi-center study. In addition to the comprehensive geriatric assessment, quantitative measures of reduced mobility and motor and cognitive deficits are performed before and after a two week's inpatient stay. Components of the assessment are mobile technology-based assessments of gait, balance and transfer performance, neuropsychological tests, frailty, sarcopenia, autonomic dysfunction and sensation, and questionnaires to assess behavioral deficits, activities of daily living, quality of life, fear of falling and dysphagia. Structural MRI and an unsupervised 24/7 home assessment of mobility are performed in a subgroup of participants. The study will also investigate the minimal clinically relevant change of the investigated parameters. Discussion: This study will help form a better understanding of symptoms and their complex interactions and treatment effects in a large geriatric cohort.info:eu-repo/semantics/publishedVersio

Consensus Paper: Neurophysiological Assessments of Ataxias in Daily Practice

The purpose of this consensus paper is to review electrophysiological abnormalities and to provide a guideline of neurophysiological assessments in cerebellar ataxias. All authors agree that standard electrophysiological methods should be systematically applied in all cases of ataxia to reveal accompanying peripheral neuropathy, the involvement of the dorsal columns, pyramidal tracts and the brainstem. Electroencephalography should also be considered, although findings are frequently non-specific. Electrophysiology helps define the neuronal systems affected by the disease in an individual patient and to understand the phenotypes of the different types of ataxia on a more general level. As yet, there is no established electrophysiological measure which is sensitive and specific of cerebellar dysfunction in ataxias. The authors agree that cerebellar brain inhibition (CBI), which is based on a paired-pulse transcranial magnetic stimulation (TMS) paradigm assessing cerebellar-cortical connectivity, is likely a useful measure of cerebellar function. Although its role in the investigation and diagnoses of different types of ataxias is unclear, it will be of interest to study its utility in this type of conditions. The authors agree that detailed clinical examination reveals core features of ataxia (i.e., dysarthria, truncal, gait and limb ataxia, oculomotor dysfunction) and is sufficient for formulating a differential diagnosis. Clinical assessment of oculomotor function, especially saccades and the vestibulo-ocular reflex (VOR) which are most easily examined both at the bedside and with quantitative testing techniques, is of particular help for differential diagnosis in many cases. Pure clinical measures, however, are not sensitive enough to reveal minute fluctuations or early treatment response as most relevant for pre-clinical stages of disease which might be amenable to study in future intervention trials. The authors agree that quantitative measures of ataxia are desirable as biomarkers. Methods are discussed that allow quantification of ataxia in laboratory as well as in clinical and real-life settings, for instance at the patients' home. Future studies are needed to demonstrate their usefulness as biomarkers in pharmaceutical or rehabilitation trials