Computational Analysis of Upper Extremity Movements for People Post-Stroke

Wearable sensors have been beneficial in assessing motor impairment after stroke. Individuals who have experienced stroke may benefit from the use of wearable sensors to quantify and assess quality of motions in unobserved environments. Seven individuals participated in a study wherein they performed various gestures from the Fugl-Meyer Assessment (FMA), a measure of post-stroke impairment. Participants performed these gestures while being monitored by wearable sensors placed on each wrist. A series of MATLAB functions were written to process recorded sensor data, extract meaningful features from the data, and prepare those features for further use with various machine learning techniques. A combination of linear and nonlinear regression was applied to frequency domain values from each gesture to determine which can more accurately predict the time spent performing the gesture, and the associated gesture FMA score. General performance suggests that linear regression techniques appear to better fit paretic gestures, while nonlinear regression techniques appear to better fit non-paretic gestures. A use of classifier techniques were used to determine if a classifier can distinguish between paretic and non-paretic gestures. The combinations include determining if a higher performance is obtained through the use of either accelerometer, rate gyroscope, or both modalities combined. Our findings indicate that, for upper-extremity motion, classifiers trained using a combination of accelerometer and rate gyroscope data performed the best (accuracy of 73.1%). Classifiers trained using accelerometer data alone and rate gyroscope data alone performed slightly worse than the combined data classifier (70.2% and 65.7%, respectively). These results suggest specific features and methods suitable for the quantification of impairment after stroke

Home monitoring of motor fluctuations in Parkinson's disease patients

In Parkinson's disease, motor fluctuations (worsening of tremor, bradykinesia, freezing of gait, postural instability) affect up to 70% of patients within 9 years of \textsc {l}-dopa therapy. Nevertheless, the assessment of motor fluctuations is difficult in a medical office, and is commonly based on poorly reliable self-reports. Hence, the use of wearable sensors is desirable. In this preliminary trial, we have investigated bradykinesia and freezing of gait—FOG—symptoms by means of inertial measurement units. To this purpose, we have employed a single smartphone on the patient's waist for FOG experiment (38 patients), and on patient thigh for LA (93 subjects). Given the sound performance achieved in this trial (AUC = 0.97 for FOG and AUC = 0.92 for LA), motor fluctuations may be estimated in domestic environments. To this end, we plan to perform measures and data processing on SensorTile, a tiny IoT module including several sensors, a microcontroller, a BlueTooth low-energy interface and microSD card, implementing an electronic diary of motor fluctuations, posture and dyskinesia during activity of daily living

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KAVELI) : Protocol for an Observational Case-Control Study

Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders.Peer reviewe

Exploration of digital biomarkers in chronic low back pain and Parkinson’s disease

Chronic pain and Parkinson’s disease are illnesses with personal disease progression, symptoms, and the experience of these. The ability to measure and monitor the symptoms by digitally and remotely is still limited. The aim was to study the usability and feasibility of real-world data from wearables, mobile devices, and patients in exploring digital biomarkers in these diseases. The key hypothesis was that this allows us to measure, analyse and detect clinically valid digital signals in movement, heart rate and skin conductance data. The laboratory grade data in chronic pain were collected in an open feasibility study by using a program and built-in sensors in virtual reality devices. The real-world data were collected with a randomized clinical study by clinical assessments, built-in sensors, and two wearables. The laboratory grade dataset in Parkinson’s disease was obtained from Michael J. Fox Foundation. It contained sensor data from three wearables with clinical assessments. The real-world data were collected with a clinical study by clinical assessments, a wearable, and a mobile application. With both diseases the laboratory grade data were first explored, before the real-world data were analyzed. The classification of chronic pain patients with the laboratory grade movement data was possible with a high accuracy. A novel real-world digital signal that correlates with clinical outcomes was found in chronic low back pain patients. A model that was able to detect different movement states was developed with laboratory grade Parkinson’s disease data. A detection of these states followed by the quantification of symptoms was found to be a potential method for the future. The usability of data collection methods in both diseases were found promising. In the future the analyses of movement data in these diseases could be further researched and validated as a movement based digital biomarkers to be used as a surrogate or additional endpoint. Combining the data science with the optimal usability enables the exploitation of digital biomarkers in clinical trials and treatment.Digitaalisten biomarkkereiden tunnistaminen kroonisessä alaselkäkivussa ja Parkinsonin taudissa Krooninen kipu ja Parkinsonin tauti ovat oireiden, oirekokemuksen sekä taudin kehittymisen osalta yksilöllisiä sairauksia. Kyky mitata ja seurata oireita etänä on vielä alkeellista. Väitöskirjassa tutkittiin kaupallisten mobiili- ja älylaitteiden hyödyntämistä digitaalisten biomarkkereiden löytämisessä näissä taudeissa. Pääolettamus oli, että kaupallisten älylaitteiden avulla kyetään tunnistamaan kliinisesti hyödyllisiä digitaalisia signaaleja. Kroonisen kivun laboratorio-tasoinen data kerättiin tätä varten kehitettyä ohjelmistoa sekä kaupallisia antureita käyttäen. Reaaliaikainen kipudata kerättiin erillisen hoito-ohjelmiston tehoa ja turvallisuutta mitanneessa kliinisessä tutkimuksessa sekä kliinisiä arviointeja että anturidataa hyödyntäen. Laboratorio-tasoinena datana Parkinsonin taudissa käytettiin Michael J. Fox Foundationin kolmella eri älylaitteella ja kliinisin arvioinnein kerättyä dataa. Reaaliaikainen data kerättiin käyttäen kliinisia arviointeja, älyranneketta ja mobiilisovellusta. Molempien indikaatioiden kohdalla laboratoriodatalle tehtyä eksploratiivista analyysia hyödynnettiin itse reaaliaikaisen datan analysoinnissa. Kipupotilaiden tunnistaminen laboratorio-tasoisesta liikedatasta oli mahdollista korkealla tarkkuudella. Reaaliaikaisesta liikedatasta löytyi uusi kliinisten arviointien kanssa korreloiva digitaalinen signaali. Parkinsonin taudin datasta kehitettiin uusi liiketyyppien tunnistamiseen tarkoitettu koneoppimis-malli. Sen hyödyntäminen liikedatan liiketyyppien tunnistamisessa ennen varsinaista oireiden mittausta on lupaava menetelmä. Käytettävyys molempien tautien reaaliaikaisissa mittausmenetelmissä havaittiin toimivaksi. Reaaliaikaiseen, kaupallisin laittein kerättävään liikedataan pohjautuvat digitaaliset biomarkkerit ovat lupaava kohde jatkotutkimukselle. Uusien analyysimenetelmien yhdistäminen optimaaliseen käytettävyyteen mahdollistaa tulevaisuudessa digitaalisten biomarkkereiden hyödyntämisen sekä kroonisten tautien kliinisessä tutkimuksessa että itse hoidossa

Objective and automatic classification of Parkinson disease with Leap Motion controller

Background: The main objective of this paper is to develop and test the ability of the Leap Motion controller (LMC) to assess the motor dysfunction in patients with Parkinson disease (PwPD) based on the MDS-UPDRSIII exercises. Four exercises (thumb forefinger tapping, hand opening/closing, pronation/supination, postural tremor) were used to evaluate the characteristics described in MDS-UPDRSIII. Clinical ratings according to the MDS/UPDRS-section III items were used as target. For that purpose, 16 participants with PD and 12 healthy people were recruited in Ospedale Cisanello, Pisa, Italy. The participants performed standardized hand movements with camera-based marker. Time and frequency domain features related to velocity, angle, amplitude, and frequency were derived from the LMC data. Results: Different machine learning techniques were used to classify the PD and healthy subjects by comparing the subjective scale given by neurologists against the predicted diagnosis from the machine learning classifiers. Feature selection methods were used to choose the most significant features. Logistic regression (LR), naive Bayes (NB), and support vector machine (SVM) were trained with tenfold cross validation with selected features. The maximum obtained classification accuracy with LR was 70.37%; the average area under the ROC curve (AUC) was 0.831. The obtained classification accuracy with NB was 81.4%, with AUC of 0.811. The obtained classification accuracy with SVM was 74.07%, with AUC of 0.675. Conclusions: Results revealed that the system did not return clinically meaningful data for measuring postural tremor in PwPD. In addition, it showed limited potential to measure the forearm pronation/supination. In contrast, for finger tapping and hand opening/closing, the derived parameters showed statistical and clinical significance. Future studies should continue to validate the LMC as updated versions of the software are developed. The obtained results support the fact that most of the set of selected features contributed significantly to classify the PwPD and healthy subjects

Designing a platform for the clinical assessment of Parkinson's Disease with inertial sensors

Tese de mestrado, Engenharia Informática (Engenharia de Software) Universidade de Lisboa, Faculdade de Ciências, 2018Parkinson é o segundo distúrbio neurodegenerativo mais comum relacionado com o avanço da idade, cerca de sete milhões a 10 milhões de pessoas em todo o mundo têm Parkinson. Estudos indicam que apenas nos países da zona ocidental da Europa existiam entre 4.1 a 4.6 Milhões de indivíduos com Parkinson com uma idade acima dos 50 anos em 2005 e estima-se que este valor vai duplicar para quantidades entre 8.7 a 9.3 Milhões até 2030[7]. Uma vez que o parkinsonismo é uma doença sem cura e cujo acompanhamento constante é essencial, são necessárias ferramentas que ajudem no acesso à condição dos pacientes e na sua avaliação. Os pacientes com doença de Parkinson (DP) apresentam problemas de estabilidade e isso afeta várias tarefas no seu cotidiano, os sintomas da DP podem ir de tremor, extrema lentidão e instabilidade postural ao comprometimento da função cognitiva, fala, deglutição ou sono, entre outros. O desafios para a avaliação clínica de pacientes com Parkinson, englobam a compreensão da progressão da doença, as respostas às intervenções farmacológicas e não farmacológicas e as flutuações sofridas pelos pacientes. No entanto, a quantidade de informação disponível para os clínicos avaliarem ainda é escassa hoje em dia. As avaliações a pacientes com Parkinson são feitas durante consultas clínicas que podem perder as flutuações existentes durante o dia devido à natureza altamente variável desta doença que difere de paciente para paciente e que muda ao longo do dia. Ainda assim, é importante estar ciente da evolução da capacidade física dos pacientes com doença de Parkinson; ´E impossível avaliar uma compreensão mais profunda da progressão da doença com as abordagens tradicionais usadas nos prestadores de cuidados de saúde comuns. Abordagens tradicionais de acompanhamento a pessoas com Parkinson em ambientes clínicos são normalmente realizadas recorrendo a um conjunto de escalas e perguntas, o que origina um conjunto de informações subjetivas referentes ao paciente e que podem ser enviesadas consoante o clínico que esteja a executar a avaliação. Existem, no entanto, consultas que recorrem a testes funcionais como: executar um certo exercício repetidamente ou movimentar-se de um local para outro; originando igualmente dados subjectivos devido aos métodos que são utilizados pelos clínicos, que vão desde a utilização de um cronómetro a avaliação visual. Atualmente, ainda há dificuldade em analisar a progressão de pacientes com Parkinson durante consultas médicas, devido aos mecanismos existentes utilizados para a análise serem subjetivos ou caros, o que significa que apenas alguns médicos são capazes de fornecer métricas objetivas aos seus pacientes. No entanto, há um interesse crescente em ter avaliações objetivas na doença de Parkinson e nos últimos anos o uso de dispositivos baseados em tecnologia no tratamento da DP tem sido apontado como uma tecnologia de ponta na medicina moderna. Existem muitos fatores que facilitam e apoiam o uso da tecnologia no monitoramento de Parkinson, bem como a existência de dispositivos tecnológicos médicos no ambiente de laboratório, o crescente acesso à Internet de alta velocidade que leva a uma transmissão de dados mais fácil e rápida entre os dispositivos, as conexões entre os dispositivos e também o aumento da alfabetização da população em geral quanto à tecnologia. Do ponto de vista dos pacientes é importante existir uma forma de traduzir informação entre clínicos e pacientes de modo a que cada indivíduo obtenha um conhecimento mais rico da sua doença e da evolução da mesma, em vez de aceitar incontestavelmente a execução de tratamentos indicados pelos clínicos. Este projeto consiste no desenvolvimento de uma plataforma baseada em demonstração de dados que visa apoiar os clínicos na avaliação física de um paciente com Parkinson durante consultas clínicas. A plataforma servirá como ferramenta de suporte para os médicos, de modo a avaliar o progresso dos pacientes e assim providenciar um diagnóstico mais dinâmico e preciso com base nos exercícios que se realizam durante as consultas médicas sendo possível, posteriormente, comparar os resultados com os anteriores. O projeto está dividido em três partes principais, um estudo inicial onde enriquecemos o conhecimento de como funcionam as avaliações executadas a pacientes com Parkinson e decidimos a melhor forma de apresentar os dados aos clínicos e pacientes seguindo uma abordagem de co-design com médicos e pacientes. Para conseguir adquirir os conhecimentos necessários foram realizados grupos de foco e sessões de observação em contexto de avaliação clínica. A segunda fase consiste na implementação da aplicação web Datapark e dos seus algoritmos de modo a obter métricas objetivas dos exercícios realizados nas consultas médicas pelos pacientes e de uma aplicação móvel utilizada para guiar e ajudar os clínicos nas avaliações realizadas. A plataforma funciona como um ponto central que integra dados recolhidos a partir da aplicação móvel e métricas objetivas obtidas a partir de um acelerómetro colocado no paciente durante a sua avaliação. Durante as avaliações clínicas o risco de queda dos pacientes é evidente, o que leva à necessidade de uma maior atenção e cuidado por parte do clínico, que ao mesmo tempo, é apoiado pela utilização de outros objetos e dispositivos para executar toda a consulta. A aplicação móvel tem como fim guiar os clínicos nas consultas médicas tal como o métodos tradicionais já utilizados. No entanto, a aplicação engloba num só dispositivo todas as funcionalidades necessárias para não existir necessidade do clínico se concentrar em nada mais senão no paciente que está sob avaliação. A terceira e última fase do projeto consiste na avaliação da plataforma com médicos e pacientes, para este fim foram realizados dois estudos em ambiente real. O primeiro estudo teve duração de uma semana com a participação de três clínicos e de sete pessoas com Parkinson, servindo para dar a entender a utilidade da plataforma e a possibilidade da inserção desta ferramenta no ambiente clínico, tendo em conta quais as melhorias aplicáveis. O último estudo realizado foi um estudo longitudinal com o objetivo de validar a plataforma e assegurar a sua utilidade a longo prazo, para isso, o sistema foi deixado em funcionamento sem interrupções durante dois meses. Durante este tempo foram avaliados onze pacientes diferentes, sendo cada um deles avaliado pelo menos duas vezes por um dos quatro fisioterapeutas que participaram nas avaliações. Este estudo terminou com um questionário feito aos fisioterapeutas com o objectivo de avaliar a usabilidade e validade da plataforma, providenciando indicadores de que o Datapark pode ser útil. A noção de que a aplicação móvel tem valor suficiente parasubstituir os métodos de recolha e anotação de informação tradicionais foi assim extraída. No entanto, para remoção total dos mecanismos tradicionais e utilização única da plataforma encontrou-se a necessidade de continuar a iterar e melhorar o sistema. Desta forma, é necessário fornecer melhores relatórios e melhorar a usabilidade, culminando por fim na plataforma visada que possui centralmente todos os mecanismos fulcrais para avaliar doentes com Parkinson. Depois de concluído este projeto, a plataforma não tem apenas uma zona para controlo de Parkinson baseado em contexto clínico, mas também uma área para verificação do dia-a-dia dos pacientes. Existe também uma terceira área cuja função será obter dados subjetivos dos pacientes durante o dia-a-dia com o intuito de oferecer aos médicos um melhor controlo da evolução desta patologia em cada um dos pacientes. Finalmente, o sistema desenhado como solução para este problema para além de ter sido utilizado para os estudos descritos, encontra-se neste momento num estado estável e está em funcionamento, sendo possível a quem estiver registado no nosso sistema executar avaliações utilizando qualquer uma das ferramentas apresentadas.Parkinson’s is the second most common age-related neurodegenerative disorder, an estimated seven million to 10 million people worldwide have Parkinson’s disease. Since Parkinsonism is a disease without cure and whose constant monitoring is essential, tools that help in the access to the condition of the patients and their evaluation are necessary. This project consists on the development of a data-driven platform that aims to support clinicians in physical assessment of a patient with Parkinson’s during clinical appointments. The platform will serve as a support tool for clinicians to evaluate the progress of their patients and thus give more dynamic and accurate diagnosis based on the exercises that they perform during their appointments and thus compare the results with the previous ones. The project is divided into three main parts, an initial study where we will decide how best present show the data to clinicians and patients, following a co-design approach. A second phase will be the implementation of the platform and its algorithms to obtain metrics of the exercises performed in appointments, by the patients. The third and final phase of the project consists on the evaluation of the platform with clinicians and patients. After completing this project, the platform not only has a Parkinson’s control environment based on clinical context analysis but also an area for free-living Parkinson’s verification. It is also included a third area whose function is to obtain patients’ subjective data during their daily life in order to give clinicians a better control the evolution of this pathology in each patient

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes