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

Motor symptoms in Parkinson's disease: A unified framework

Parkinson’s disease (PD) is characterized by a range of motor symptoms. Besides the cardinal symptoms (akinesia and bradykinesia, tremor and rigidity), PD patients show additional motor deficits, including: gait disturbance, impaired handwriting, grip force and speech deficits, among others. Some of these motor symptoms (e.g., deficits of gait, speech, and handwriting) have similar clinical profiles, neural substrates, and respond similarly to dopaminergic medication and deep brain stimulation (DBS). Here, we provide an extensive review of the clinical characteristics and neural substrates of each of these motor symptoms, to highlight precisely how PD and its medical and surgical treatments impact motor symptoms. In conclusion, we offer a unified framework for understanding the range of motor symptoms in PD. We argue that various motor symptoms in PD reflect dysfunction of neural structures responsible for action selection, motor sequencing, and coordination and execution of movement

Dispositivo electrónico para la medición de la rigidez de la mano en pacientes con la enfermedad de parkinson

Ingeniería ElectrónicaLa enfermedad de Parkinson, EP, es reconocida como una de las patologías neurológicas más comunes; es un trastorno crónico y progresivo donde el agente encargado para la comunicación entre las neuronas del cerebro, Dopamina, disminuye debido a un problema degenerativo en las neuronas generadoras de este elemento, ubicadas en los ganglios basales, el cual es la zona del cerebro encargada de las funciones motoras del cuerpo. La mayoría de exámenes clínicos para la valoración del Parkinson, consisten en ejercicios subjetivos como la UPDRS (Unified Parkinson Disease Rating Scale) donde la opinión del paciente, examinador, o incluso de algún familiar, se tienen en cuenta a la hora de realizar el diagnóstico al paciente, esto se debe a la falta de un equipo médico que arroje una valoración objetiva. Por tal motivo, en este proyecto se aborda el diseño de un guante para la medición de la flexión en las falanges de la mano, ofreciendo resultados de distintas mediciones que se someterán a una valoración médica, con lo que los doctores pueden estimar si el sujeto sometido al experimento sufre de rigidez, uno de los síntomas causados por la EP. El guante se realizó usando un sensor de flexión, el cual se ubicó en la primera falange del dedo índice para conseguir los datos del movimiento de este cuando la persona realiza ejercicios de abrir y cerrar la mano. Estos datos fueron almacenados en una micro SD para que luego fueran ilustrados gráficamente, y de igual forma, se presenta la opción de exportar la señal como una imagen o a una tabla en Excel para que posteriormente, un especialista en señales se encargue de analizar el comportamiento de estas.Parkinson Disease or PD is well known as one of the most common neurological pathologies, it is a chronic and progressive disorder where the agent in charge for the communication between brain neurons or Dopamine, decrease because of a degenerative issue in the neurons that generate this agent, which is located in the Basal Ganglia, the brain zone in charge of the body motor function. Most of the clinical tests for the Parkinson assessment, consist of subjective exercises as the UPDRS (Unified Parkinson Disease Rating Scale), where the judgement from the patient, examiner or even a patient's familiar, is what they take into account at the moment when they are going to diagnose the patient, this is because of the lack of a medical equipment that can be able to give us an objective assessment. For this reason, this project approaches the design of a glove that measures the flexion of the hand's phalanges, measuring different test subjects to gather different samples which will be brought to a medical assessment, where the doctor can evaluate if the subject for the experiment underwent rigidity, one of the symptoms caused by Parkinson. The glove was made using a flex sensor, which were placed on the index finger first phalanx to pick up the motion data when the person performs open and close exercises with the hand. This information was saved on a micro SD to graphically illustrate the signal and similarly, we show the option to export the graph as an image or an Excel table with the purpose that a signal specialist can analyze the signal behavior

Visualização e classificação de características para a discriminação entre indivíduos com a doença de Parkinson submetidos a tratamento com Levodopa e estimulação profunda do cérebro

Over the years, a number of distinct treatments have been adopted for the management of the motor symptoms of Parkinson’s disease (PD), including pharmacologic therapies and deep brain stimulation (DBS). Efficacy is most often evaluated by subjective assessments which are prone to error and dependent on the experience of the examiner. Our goal was to identify an objective means of assessing response to therapy. In this study, we employed objective analyses in order to visualize and identify differences between three groups: healthy control (N=10), subjects with PD treated with DBS (N=12), and subjects with PD treated with levodopa (N=16). Subjects were assessed during execution of three dynamic tasks (finger taps, finger to nose, supination and pronation) and a static task (rest, i.e., extended arm with no active movement). Measurements were acquired with two pairs of inertial and electromyographic sensors. Feature extraction was applied to estimate the relevant information from the data after which high-dimensional feature space was reduced to a two-dimensional space by using the nonlinear Sammon’s map. The statistical method Non-Parametric Analysis of Variance was employed for the verification of relevant statistical differences among the groups (p < 0.05). In addition, K-fold cross-validation for discriminant analysis based on Gaussian Finite Mixture Modeling was employed for data classification. The results showed visual and statistical differences for all groups and conditions (i.e., static and dynamic tasks). The employed methods were successful for the discrimination of the groups. Classification accuracy was 81%±6% (mean ± standard deviation) and 71%±8%, for classification and test groups respectively. This research showed the discrimination between healthy and diseased groups conditions. The methods were also able to discriminate individuals with PD treated with DBS and levodopa. These methods enable objective characterization and visualization of features extracted from inertial and electromyographic sensors for different groups.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo a Pesquisa do Estado de Minas GeraisFundação LemannTese (Doutorado)Ao longo dos anos vários tratamentos têm sido adotados para o gerenciamento do comportamento motor em pessoas que sofrem da Doença de Parkinson, incluindo tratamentos baseados em medicamentos e Estimulação Profunda do Cérebro. Geralmente a eficácia desses tratamentos é avaliada através de mensurações subjetivas, onde os resultados estão sujeitos a erros e dependem da experiência do examinador. Neste estudo foram empregadas análises objetivas com a finalidade de se visualizar e capturar diferenças motoras entre grupos de pacientes com Doença de Parkinson submetidos ao tratamento medicamentoso e à Estimulação Profunda do Cérebro e um grupo controle com sujeitos saudáveis. Um total de 38 sujeitos participaram desta pesquisa, sendo 10 indivíduos saudáveis pertencentes ao grupo controle, 12 indivíduos com a doença de Parkinson tratados com a Estimulação Profunda do Cérebro e 16 indivíduos com a Doença de Parkinson tratados com medicamento. Os sujeitos foram avaliados através de monitoramento durante a execução de três tarefas dinâmicas (movimento de pinça dos dedos, movimento de levar o dedo indicador ao nariz, supinação e pronação do antebraço) e uma tarefa estática (cotovelo em extensão sem a realização de movimentos voluntários). Com o objetivo de se mensurar o movimento e a atividade muscular resultantes dessas tarefas, dois pares de sensores inerciais e dois pares de sensores eletromiográficos foram utilizados. A extração de características foi utilizada para se estimar informações relevantes sobre os dados e, então, as características em espaço de alta dimensionalidade foram reduzidas em um espaço de baixa dimensionalidade através do mapeamento não linear de Sammon. O método estatístico de análise de variância não paramétrico foi empregado para a verificação de diferenças relevantes entre os grupos (p<0,05). Adicionalmente, o método de validação cruzada K-fold para análises discriminantes foi empregado para a classificação dos dados. Os resultados mostraram diferenças visuais e estatísticas para todos os grupos e condições. Os métodos empregados foram bem-sucedidos para a discriminação entre os grupos, sendo, portanto, eficientes para a caracterização das diferenças com uma taxa média de sucesso de 81%±6% (média ± o desvio padrão) e 71%±8%, para os grupos de classificação e teste, respectivamente. Esse estudo mostrou a discriminação entre grupos de pessoas em condições saudáveis e não-saudáveis. Os métodos empregados foram capazes de discriminar indivíduos com a Doença de Parkinson tratados com levodopa e Estimulação Profunda do Cérebro, contribuindo assim para a objetiva caracterização e visualização das características extraídas dos sensores inerciais e eletromiográficos para cara grupo investigado