33 research outputs found
A wearable biofeedback device to improve motor symptoms in Parkinson’s disease
Dissertação de mestrado em Engenharia BiomédicaThis dissertation presents the work done during the fifth year of the course Integrated Master’s in
Biomedical Engineering, in Medical Electronics. This work was carried out in the Biomedical & Bioinspired
Robotic Devices Lab (BiRD Lab) at the MicroElectroMechanics Center (CMEMS) established at the
University of Minho. For validation purposes and data acquisition, it was developed a collaboration with
the Clinical Academic Center (2CA), located at Braga Hospital.
The knowledge acquired in the development of this master thesis is linked to the motor
rehabilitation and assistance of abnormal gait caused by a neurological disease. Indeed, this dissertation
has two main goals: (1) validate a wearable biofeedback system (WBS) used for Parkinson's disease
patients (PD); and (2) develop a digital biomarker of PD based on kinematic-driven data acquired with the
WBS. The first goal aims to study the effects of vibrotactile biofeedback to play an augmentative role to
help PD patients mitigate gait-associated impairments, while the second goal seeks to bring a step
advance in the use of front-end algorithms to develop a biomarker of PD based on inertial data acquired
with wearable devices. Indeed, a WBS is intended to provide motor rehabilitation & assistance, but also
to be used as a clinical decision support tool for the classification of the motor disability level. This system
provides vibrotactile feedback to PD patients, so that they can integrate it into their normal physiological
gait system, allowing them to overcome their gait difficulties related to the level/degree of the disease.
The system is based on a user- centered design, considering the end-user driven, multitasking and less
cognitive effort concepts.
This manuscript presents all steps taken along this dissertation regarding: the literature review and
respective critical analysis; implemented tech-based procedures; validation outcomes complemented with
results discussion; and main conclusions and future challenges.Esta dissertação apresenta o trabalho realizado durante o quinto ano do curso Mestrado
Integrado em Engenharia Biomédica, em Eletrónica Médica. Este trabalho foi realizado no Biomedical &
Bioinspired Robotic Devices Lab (BiRD Lab) no MicroElectroMechanics Center (CMEMS) estabelecido na
Universidade do Minho. Para efeitos de validação e aquisição de dados, foi desenvolvida uma colaboração
com Clinical Academic Center (2CA), localizado no Hospital de Braga.
Os conhecimentos adquiridos no desenvolvimento desta tese de mestrado estão ligados à
reabilitação motora e assistência de marcha anormal causada por uma doença neurológica. De facto,
esta dissertação tem dois objetivos principais: (1) validar um sistema de biofeedback vestível (WBS)
utilizado por doentes com doença de Parkinson (DP); e (2) desenvolver um biomarcador digital de PD
baseado em dados cinemáticos adquiridos com o WBS. O primeiro objetivo visa o estudo dos efeitos do
biofeedback vibrotáctil para desempenhar um papel de reforço para ajudar os pacientes com PD a mitigar
as deficiências associadas à marcha, enquanto o segundo objetivo procura trazer um avanço na utilização
de algoritmos front-end para biomarcar PD baseado em dados inerciais adquiridos com o dispositivos
vestível. De facto, a partir de um WBS pretende-se fornecer reabilitação motora e assistência, mas
também utilizá-lo como ferramenta de apoio à decisão clínica para a classificação do nível de deficiência
motora. Este sistema fornece feedback vibrotáctil aos pacientes com PD, para que possam integrá-lo no
seu sistema de marcha fisiológica normal, permitindo-lhes ultrapassar as suas dificuldades de marcha
relacionadas com o nível/grau da doença. O sistema baseia-se numa conceção centrada no utilizador,
considerando o utilizador final, multitarefas e conceitos de esforço menos cognitivo.
Portanto, este manuscrito apresenta todos os passos dados ao longo desta dissertação
relativamente a: revisão da literatura e respetiva análise crítica; procedimentos de base tecnológica
implementados; resultados de validação complementados com discussão de resultados; e principais
conclusões e desafios futuros
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
Instrumented Footwear and Machine Learning for Gait Analysis and Training
Gait analysis allows clinicians and researchers to quantitatively characterize the kinematics and kinetics of human movement. Devices that quantify gait can be either portable, such as instrumented shoes, or non-portable, such as motion capture systems and instrumented walkways. There is a tradeoff between these two classes of systems in terms of portability and accuracy. However, recent computer advances allow for the collection of meaningful data outside of the clinical setting. In this work, we present the DeepSole system combined with the different neural network models. This system is a fully capable to characterize the gait of the individuals and provide vibratory feedback to the wearer.
Thanks to the flexible construction and its wireless capabilities, it can be comfortably worn by wide arrange of people, both able-bodied and people with pathologies that affect their gait. It can be used for characterization, training, and as an abstract sensor to measure human gait in real-time. Three neural network models were designed and implemented to map the sensors embedded in the DeepSole system to gait characteristics and events. The first one is a recurrent neural network that classifies the gait into the correct gait phase of the wearer. This model was validated with data from healthy young adults and children with Cerebral Palsy. Furthermore, this model was implemented in real-time to provide vibratory feedback to healthy young adults to create temporal asymmetry on the dominant side during regular walking. During the experiment, the subjects who walked had an increased stance time on both sides, but the dominant side was affected more.
The second model is encoder-decoder recurrent neural network that maps the sensors into current gait cycle percentage. This model is useful to provide continuous feedback that is synchronized to the gait. This model was implemented in real-time to provide vibratory feedback to six muscle groups used during regular walking. The effects of the vibration were analyzed. It was found that depending on the feedback, the subjects changed their spatial and temporal gait parameters.
The third model uses all the sensors in the instrumented footwear to identify a motor phenomenon called freezing of gait in patients with Parkinson’s Disease. This phenomenon is characterized by transient periods, usually lasting for several seconds, in which attempted ambulation is halted. The model has better performance than the state-of-the-art and does not require any pre-processing.
The DeepSole system when used in conjunction with the presented models is able to characterize and provide feedback in a wide range of scenarios. The system is portable, comfortable, and can accommodate a wide range of populations who can benefit from this wearable technology
Cybersecurity and the Digital Health: An Investigation on the State of the Art and the Position of the Actors
Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain
NON-VERBAL COMMUNICATION WITH PHYSIOLOGICAL SENSORS. THE AESTHETIC DOMAIN OF WEARABLES AND NEURAL NETWORKS
Historically, communication implies the transfer of information between bodies, yet this
phenomenon is constantly adapting to new technological and cultural standards. In a
digital context, it’s commonplace to envision systems that revolve around verbal modalities.
However, behavioural analysis grounded in psychology research calls attention to
the emotional information disclosed by non-verbal social cues, in particular, actions that
are involuntary. This notion has circulated heavily into various interdisciplinary computing
research fields, from which multiple studies have arisen, correlating non-verbal
activity to socio-affective inferences. These are often derived from some form of motion
capture and other wearable sensors, measuring the ‘invisible’ bioelectrical changes that
occur from inside the body.
This thesis proposes a motivation and methodology for using physiological sensory
data as an expressive resource for technology-mediated interactions. Initialised from a
thorough discussion on state-of-the-art technologies and established design principles
regarding this topic, then applied to a novel approach alongside a selection of practice
works to compliment this. We advocate for aesthetic experience, experimenting with
abstract representations. Atypically from prevailing Affective Computing systems, the
intention is not to infer or classify emotion but rather to create new opportunities for rich
gestural exchange, unconfined to the verbal domain.
Given the preliminary proposition of non-representation, we justify a correspondence
with modern Machine Learning and multimedia interaction strategies, applying an iterative,
human-centred approach to improve personalisation without the compromising
emotional potential of bodily gesture. Where related studies in the past have successfully
provoked strong design concepts through innovative fabrications, these are typically limited
to simple linear, one-to-one mappings and often neglect multi-user environments;
we foresee a vast potential. In our use cases, we adopt neural network architectures to
generate highly granular biofeedback from low-dimensional input data.
We present the following proof-of-concepts: Breathing Correspondence, a wearable
biofeedback system inspired by Somaesthetic design principles; Latent Steps, a real-time auto-encoder to represent bodily experiences from sensor data, designed for dance performance;
and Anti-Social Distancing Ensemble, an installation for public space interventions,
analysing physical distance to generate a collective soundscape. Key findings are
extracted from the individual reports to formulate an extensive technical and theoretical
framework around this topic. The projects first aim to embrace some alternative perspectives
already established within Affective Computing research. From here, these concepts
evolve deeper, bridging theories from contemporary creative and technical practices with
the advancement of biomedical technologies.Historicamente, os processos de comunicação implicam a transferência de informação
entre organismos, mas este fenómeno está constantemente a adaptar-se a novos padrões
tecnológicos e culturais. Num contexto digital, é comum encontrar sistemas que giram
em torno de modalidades verbais. Contudo, a análise comportamental fundamentada
na investigação psicológica chama a atenção para a informação emocional revelada por
sinais sociais não verbais, em particular, acções que são involuntárias. Esta noção circulou
fortemente em vários campos interdisciplinares de investigação na área das ciências da
computação, dos quais surgiram múltiplos estudos, correlacionando a actividade nãoverbal
com inferências sócio-afectivas. Estes são frequentemente derivados de alguma
forma de captura de movimento e sensores “wearable”, medindo as alterações bioeléctricas
“invisíveis” que ocorrem no interior do corpo.
Nesta tese, propomos uma motivação e metodologia para a utilização de dados sensoriais
fisiológicos como um recurso expressivo para interacções mediadas pela tecnologia.
Iniciada a partir de uma discussão aprofundada sobre tecnologias de ponta e princípios
de concepção estabelecidos relativamente a este tópico, depois aplicada a uma nova abordagem,
juntamente com uma selecção de trabalhos práticos, para complementar esta.
Defendemos a experiência estética, experimentando com representações abstractas. Contrariamente
aos sistemas de Computação Afectiva predominantes, a intenção não é inferir
ou classificar a emoção, mas sim criar novas oportunidades para uma rica troca gestual,
não confinada ao domínio verbal.
Dada a proposta preliminar de não representação, justificamos uma correspondência
com estratégias modernas de Machine Learning e interacção multimédia, aplicando uma
abordagem iterativa e centrada no ser humano para melhorar a personalização sem o
potencial emocional comprometedor do gesto corporal. Nos casos em que estudos anteriores
demonstraram com sucesso conceitos de design fortes através de fabricações
inovadoras, estes limitam-se tipicamente a simples mapeamentos lineares, um-para-um,
e muitas vezes negligenciam ambientes multi-utilizadores; com este trabalho, prevemos
um potencial alargado. Nos nossos casos de utilização, adoptamos arquitecturas de redes
neurais para gerar biofeedback altamente granular a partir de dados de entrada de baixa dimensão.
Apresentamos as seguintes provas de conceitos: Breathing Correspondence, um sistema
de biofeedback wearable inspirado nos princípios de design somaestético; Latent
Steps, um modelo autoencoder em tempo real para representar experiências corporais
a partir de dados de sensores, concebido para desempenho de dança; e Anti-Social Distancing
Ensemble, uma instalação para intervenções no espaço público, analisando a
distância física para gerar uma paisagem sonora colectiva. Os principais resultados são
extraídos dos relatórios individuais, para formular um quadro técnico e teórico alargado
para expandir sobre este tópico. Os projectos têm como primeiro objectivo abraçar algumas
perspectivas alternativas às que já estão estabelecidas no âmbito da investigação
da Computação Afectiva. A partir daqui, estes conceitos evoluem mais profundamente,
fazendo a ponte entre as teorias das práticas criativas e técnicas contemporâneas com o
avanço das tecnologias biomédicas
Body sensor networks: smart monitoring solutions after reconstructive surgery
Advances in reconstructive surgery are providing treatment options in the face of major trauma and cancer. Body Sensor Networks (BSN) have the potential to offer smart solutions to a range of clinical challenges. The aim of this thesis was to review the current state of the art devices, then develop and apply bespoke technologies developed by the Hamlyn Centre BSN engineering team supported by the EPSRC ESPRIT programme to deliver post-operative monitoring options for patients undergoing reconstructive surgery.
A wireless optical sensor was developed to provide a continuous monitoring solution for free tissue transplants (free flaps). By recording backscattered light from 2 different source wavelengths, we were able to estimate the oxygenation of the superficial microvasculature. In a custom-made upper limb pressure cuff model, forearm deoxygenation measured by our sensor and gold standard equipment showed strong correlations, with incremental reductions in response to increased cuff inflation durations. Such a device might allow early detection of flap failure, optimising the likelihood of flap salvage.
An ear-worn activity recognition sensor was utilised to provide a platform capable of facilitating objective assessment of functional mobility. This work evolved from an initial feasibility study in a knee replacement cohort, to a larger clinical trial designed to establish a novel mobility score in patients recovering from open tibial fractures (OTF). The Hamlyn Mobility Score (HMS) assesses mobility over 3 activities of daily living: walking, stair climbing, and standing from a chair. Sensor-derived parameters including variation in both temporal and force aspects of gait were validated to measure differences in performance in line with fracture severity, which also matched questionnaire-based assessments. Monitoring the OTF cohort over 12 months with the HMS allowed functional recovery to be profiled in great detail. Further, a novel finding of continued improvements in walking quality after a plateau in walking quantity was demonstrated objectively.
The methods described in this thesis provide an opportunity to revamp the recovery paradigm through continuous, objective patient monitoring along with self-directed, personalised rehabilitation strategies, which has the potential to improve both the quality and cost-effectiveness of reconstructive surgery services.Open Acces
Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design
Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data
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