Measurement of axial rigidity and postural instability using wearable sensors

Axial Bradykinesia is an important feature of advanced Parkinson\u27s disease (PD). The purpose of this study is to quantify axial bradykinesia using wearable sensors with the long-term aim of quantifying these movements, while the subject performs routine domestic activities. We measured back movements during common daily activities such as pouring, pointing, walking straight and walking around a chair with a test system engaging a minimal number of Inertial Measurement (IM) based wearable sensors. Participants included controls and PD patients whose rotation and flexion of the back was captured by the time delay between motion signals from sensors attached to the upper and lower back. PD subjects could be distinguished from controls using only two sensors. These findings suggest that a small number of sensors and similar analyses could distinguish between variations in bradykinesia in subjects with measurements performed outside of the laboratory. The subjects could engage in routine activities leading to progressive assessments of therapeutic outcomes

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

Quantifying Parkinson\u27s Disease Symptoms Using Mobile Devices

Current assessments for evaluating the progression of Parkinson’s Disease are largely qualitative and based on small sets of data obtained from occasional doctor-patient interactions. There is a clinical need to improve the techniques used for mitigating common Parkinson’s Disease symptoms. Available data sets for researching the disease are minimal, hindering advancement toward understanding the underlying causes and effectiveness of treatment and therapies. Mobile devices present an opportunity to continuously monitor Parkinson’s Disease patients and collect important information regarding the severity of symptoms. The evolution of digital technology has opened doors for clinical research to extend beyond the clinic by incorporating complex sensors in commonly used devices. Leveraging these sensors to quantify characteristic Parkinson’s Disease symptoms may drastically improve patient care and the reliability of symptom assessment. The goal of this project is to design and develop a system for measuring and analyzing the cardinal symptoms of Parkinson’s using mobile devices. An application for the iPhone and Apple Watch is developed, utilizing the sensors on the devices to collect data during the performance of motor tasks. Assessments for tremor, bradykinesia, and postural instability are implemented to mimic UPDRS evaluations normally performed by a neurologist. The application connects to a cloud-based server to transfer the collected data for remote access and analysis. Example MatLab analysis demonstrates potential approaches for extracting meaningful data to be used for monitoring the progression of Parkinson’s Disease and the effectiveness of treatment and therapies. High-level verification testing is performed to show general efficacy of the assessment tasks. The system design successfully lays the groundwork for a mobile device-based assessment tool to objectively measure Parkinson’s Disease symptom

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

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

Balance and gait in progressive supranuclear palsy: a narrative review of objective metrics and exercise interventions

BackgroundThe use of objective gait and balance metrics is rapidly expanding for evaluation of atypical parkinsonism, and these measures add to clinical observations. Evidence for rehabilitation interventions to improve objective measures of balance and gait in atypical parkinsonism is needed.AimOur aim is to review, with a narrative approach, current evidence on objective metrics for gait and balance and exercise interventions in progressive supranuclear palsy (PSP).MethodsLiterature searches were conducted in four computerized databases from the earliest record up to April 2023: PubMed, ISI’s Web of Knowledge, Cochrane’s Library, and Embase. Data were extracted for study type (cross-sectional, longitudinal, and rehabilitation interventions), study design (e.g., experimental design and case series), sample characteristics, and gait and balance measurements.ResultsEighteen gait and balance (16 cross-sectional and 4 longitudinal) and 14 rehabilitation intervention studies were included. Cross-sectional studies showed that people with PSP have impairments in gait initiation and steady-state gait using wearable sensors, and in static and dynamic balance assessed by posturography when compared to Parkinson’s disease (PD) and healthy controls. Two longitudinal studies observed that wearable sensors can serve as objective measures of PSP progression, using relevant variables of change in turn velocity, stride length variability, toe off angle, cadence, and cycle duration. Rehabilitation studies investigated the effect of different interventions (e.g., balance training, body-weight supported treadmill gait, sensorimotor training, and cerebellar transcranial magnetic stimulation) on gait, clinical balance, and static and dynamic balance assessed by posturography measurements. No rehabilitation study in PSP used wearable sensors to evaluate gait and balance impairments. Although clinical balance was assessed in 6 rehabilitation studies, 3 of these studies used a quasi-experimental design, 2 used a case series, only 1 study used an experimental design, and sample sizes were relatively small.ConclusionWearable sensors to quantify balance and gait impairments are emerging as a means of documenting progression of PSP. Robust evidence for improving balance and gait in PSP was not found for rehabilitation studies. Future powered, prospective and robust clinical trials are needed to investigate the effects of rehabilitation interventions on objective gait and balance outcomes in people with PSP

Development and Evaluation of AI-based Parkinson's Disease Related Motor Symptom Detection Algorithms

Parkinson's Disease (PD) is a chronic, progressive, neurodegenerative disorder that is typically characterized by a loss of (motor) function, increased slowness and rigidity. Due to a lack of feasible biomarkers, progression cannot easily be quantified with objective measures. For the same reason, neurologists have to revert to monitoring of (motor) symptoms (i.e. by means of subjective and often inaccurate patient diaries) in order to evaluate a medication's effectiveness. Replacing or supplementing these diaries with an automatic and objective assessment of symptoms and side effects could drastically reduce manual efforts and potentially help in personalizing and improving medication regime. In turn, appearance of symptoms could be reduced and the patient's quality of life increased. The objective of this thesis is two-fold: (1) development and improvement of algorithms for detecting PD related motor symptoms and (2) to develop a software framework for time series analysis

INERTIA SENSOR-BASED MOBILITY ANALYSIS FOR PARKINSON'S DISEASE

Ph.DDOCTOR OF PHILOSOPH

Locomotion Traces Data Mining for Supporting Frail People with Cognitive Impairment

The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of older people. Thus, this thesis at first focused on providing a systematic literature review of locomotion data mining systems for supporting Neuro-Degenerative Diseases (NDD) diagnosis, identifying locomotion anomaly indicators and movement patterns for discovering low-level locomotion indicators, sensor data acquisition, and processing methods, as well as NDD detection algorithms considering their pros and cons. Then, we investigated the use of sensor data and Deep Learning (DL) to recognize abnormal movement patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduced novel visual feature extraction methods for locomotion data. Our solutions rely on locomotion traces segmentation, image-based extraction of salient features from locomotion segments, and vision-based DL. Furthermore, we proposed a data augmentation strategy to increase the volume of collected data and generalize the solution to different smart-homes with different layouts. We carried out extensive experiments with a large real-world dataset acquired in a smart-home test-bed from older people, including people with cognitive diseases. Experimental comparisons show that our system outperforms state-of-the-art methods