Empowering patients in self-management of parkinson's disease through cooperative ICT systems

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies. © 2016, IGI Global. All rights reserved

Wearable feedback systems for rehabilitation

In this paper we describe LiveNet, a flexible wearable platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification. Based on the MIT Wearable Computing Group's distributed mobile system architecture, LiveNet is a stable, accessible system that combines inexpensive, commodity hardware; a flexible sensor/peripheral interconnection bus; and a powerful, light-weight distributed sensing, classification, and inter-process communications software architecture to facilitate the development of distributed real-time multi-modal and context-aware applications. LiveNet is able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. We demonstrate the power and functionality of this platform by describing a number of health monitoring applications using the LiveNet system in a variety of clinical studies that are underway. Initial evaluations of these pilot experiments demonstrate the potential of using the LiveNet system for real-world applications in rehabilitation medicine

A new approach to study gait impairments in Parkinson’s disease based on mixed reality

Dissertação de mestrado integrado em Engenharia Biomédica (especialização em Eletrónica Médica)Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease. PD onset is at 55 years-old on average, and its incidence increases with age. This disease results from dopamine-producing neurons degeneration in the basal ganglia and is characterized by various motor symptoms such as freezing of gait, bradykinesia, hypokinesia, akinesia, and rigidity, which negatively impact patients’ quality of life. To monitor and improve these PD-related gait disabilities, several technology-based methods have emerged in the last decades. However, these solutions still require more customization to patients’ daily living tasks in order to provide more objective, reliable, and long-term data about patients’ motor conditions in home-related contexts. Providing this quantitative data to physicians will ensure more personalized and better treatments. Also, motor rehabilitation sessions fostered by assistance devices require the inclusion of quotidian tasks to train patients for their daily motor challenges. One of the most promising technology-based methods is virtual, augmented, and mixed reality (VR/AR/MR), which immerse patients in virtual environments and provide sensory stimuli (cues) to assist with these disabilities. However, further research is needed to improve and conceptualize efficient and patient-centred VR/AR/MR approaches and increase their clinical evidence. Bearing this in mind, the main goal of this dissertation was to design, develop, test, and validate virtual environments to assess and train PD-related gait impairments using mixed reality smart glasses, integrated with another high-technological motion tracking device. Using specific virtual environments that trigger PD-related gait impairments (turning, doorways, and narrow spaces), it is hypothesized that patients can be assessed and trained in their daily challenges related to walking. Also, this tool integrates on-demand visual cues to provide visual biofeedback and foster motor training. This solution was validated with end-users to test the identified hypothesis. The results showed that, in fact, mixed reality has the potential to recreate real-life environments that often provoke PD-related gait disabilities, by placing virtual objects on top of the real world. On the contrary, biofeedback strategies did not significantly improve the patients’ motor performance. The user experience evaluation showed that participants enjoyed participating in the activity and felt that this tool can help their motor performance.A doença de Parkinson (DP) é a segunda doença neurodegenerativa mais comum depois da doença de Alzheimer. O início da DP ocorre, em média, aos 55 anos de idade, e a sua incidência aumenta com a idade. Esta doença resulta da degeneração dos neurónios produtores de dopamina nos gânglios basais e é caracterizada por vários sintomas motores como o congelamento da marcha, bradicinesia, hipocinesia, acinesia, e rigidez, que afetam negativamente a qualidade de vida dos pacientes. Nas últimas décadas surgiram métodos tecnológicos para monitorizar e treinar estas desabilidades da marcha. No entanto, estas soluções ainda requerem uma maior personalização relativamente às tarefas diárias dos pacientes, a fim de fornecer dados mais objetivos, fiáveis e de longo prazo sobre o seu desempenho motor em contextos do dia-a-dia. Através do fornecimento destes dados quantitativos aos médicos, serão assegurados tratamentos mais personalizados. Além disso, as sessões de reabilitação motora, promovidas por dispositivos de assistência, requerem a inclusão de tarefas quotidianas para treinar os pacientes para os seus desafios diários. Um dos métodos tecnológicos mais promissores é a realidade virtual, aumentada e mista (RV/RA/RM), que imergem os pacientes em ambientes virtuais e fornecem estímulos sensoriais para ajudar nestas desabilidades. Contudo, é necessária mais investigação para melhorar e conceptualizar abordagens RV/RA/RM eficientes e centradas no paciente e ainda aumentar as suas evidências clínicas. Tendo isto em mente, o principal objetivo desta dissertação foi conceber, desenvolver, testar e validar ambientes virtuais para avaliar e treinar as incapacidades de marcha relacionadas com a DP usando óculos inteligentes de realidade mista, integrados com outro dispositivo de rastreio de movimento. Utilizando ambientes virtuais específicos que desencadeiam desabilidades da marcha (rodar, portas e espaços estreitos), é possível testar hipóteses de que os pacientes possam ser avaliados e treinados nos seus desafios diários. Além disso, esta ferramenta integra pistas visuais para fornecer biofeedback visual e fomentar a reabilitação motora. Esta solução foi validada com utilizadores finais de forma a testar as hipóteses identificadas. Os resultados mostraram que, de facto, a realidade mista tem o potencial de recriar ambientes da vida real que muitas vezes provocam deficiências de marcha relacionadas à DP. Pelo contrário, as estratégias de biofeedback não provocaram melhorias significativas no desempenho motor dos pacientes. A avaliação feita pelos pacientes mostrou que estes gostaram de participar nos testes e sentiram que esta ferramenta pode auxiliar no seu desempenho motor

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice

Preliminary evaluation of SensHand V1 in assessing motor skills performance in Parkinson Disease

Nowadays, the increasing old population 65+ as well as the pace imposed by work activities lead to a high number of people that have particular injuries for limbs. In addition to persistent or temporary disabilities related to accidental injuries we must take into account that part of the population suffers from motor deficits of the hands due to stroke or diseases of various clinical nature. The most recurrent technological solutions to measure the rehabilitation or skill motor performance of the hand are glove-based devices, able to faithfully capture the movements of the hand and fingers. This paper presents a system for hand motion analysis based on 9-axis complete inertial modules and dedicated microcontroller which are fixed on fingers and forearm. The technological solution presented is able to track the patients' hand motions in real-time and then to send data through wireless communication reducing the clutter and the disadvantages of a glove equipped with sensors through a different technological structure. The device proposed has been tested in the study of Parkinson's disease

A minimalistic co-culture platform for alpha-synuclein spreading in human dopaminergic neurons

Parkinson’s disease (PD) is the second major neurodegenerative disease and the most common movement disorder. Due to age being a critical risk factor, the rapid ageing of the world population further increases the prevalence of PD. So far no treatment is available and therapies mainly focus on motor symptoms by pharmacologically substituting striatal dopamine, caused by the loss of dopaminergic neurons in the substantia nigra. This neuronal loss and intracellular protein aggregates, termed Lewy bodies (LBs), are pathological characteristics of PD. With disease progression, a spread of LBs through the brain can be observed which mainly follows axonal projections. Understanding the mechanisms of this progressive spread could be central to discovering the underlying molecular pathogenesis of the disease. As LBs mainly consist of alpha-synuclein (-syn), a prion-like spreading of -syn was suggested and is now widely accepted as a component in the PD pathogenesis. New dopaminergic model systems to study the exact mechanisms underlying -syn spread are urgently needed. As PD is a human disease, in vitro models should be derived from humans. Lund human mesencephalic (LUHMES) cells are a suitable alternative to other, mostly non-human, dopaminergic cell lines. However, difficulties cultivating them in microfluidics devices has made them thus far inaccessible for co-cultivation studies in the field of PD spreading. In the first part of this thesis, a human dopaminergic cell model system for studying the spreading of -syn fibrils is presented. First, the well-characterized LUHMES cell line was tested for suitability of PD research on prion-like spreading, as no data is currently available on this matter. For the analysis, immunofluorescence light microscopy was employed. An extended period of differentiation aimed for a high degree of neuronal maturity and long neurites to facilitate the connectivity of spatially-separated cell populations. Seeding experiments with -syn fibrils revealed a weak toxicity against these assemblies, even at prolonged differentiation. Second, to study the transmission of -syn fibrils via neuronal projections, we developed a light microscopy-compatible microfluidic co-culturing device, to maintain two LUHMES cell populations in separate cell compartments for up to two weeks of differentiation. During this time, a neurite network is formed which connects the fluidically isolated cell growth compartments. The ability to cultivate cells with neurites and soma in an isolated environment enabled seeding and transmission experiments in anterograde and retrograde directions. In the second part of this thesis, implementation strategies of the microfluidic co-culturing chip for alternative analysis methods are discussed. Firstly, the accessibility of the cells in the co-culturing device using a single-cell lysis instrument is evaluated. The tool allows for targeted lysis of individual adherent cells. Preliminary tests point in a promising direction, while LUHMES single cell lysate was successfully transferred to different analysis techniques. However, direct access to the channels of the microfluidic co-culturing chip was problematic and needs further modifications. Secondly, an implementation of the microfluidic device aiming for co-cultivation of LUHMES cells on electron microscopy grids to study neurite architecture was pursued. Thereby, microfluidic devices harbor only cell soma, but neurites can grow onto an electron microscopy grid, as only they are thin enough to be visualized by cryo-electron microscopy. Proof-of-concept experiments demonstrate the direct visualization of LUHMES cell neurites in a near-native, frozen-hydrated state