12 research outputs found
Learning Biosignals with Deep Learning
The healthcare system, which is ubiquitously recognized as one of the most influential
system in society, is facing new challenges since the start of the decade.The myriad of
physiological data generated by individuals, namely in the healthcare system, is generating
a burden on physicians, losing effectiveness on the collection of patient data. Information
systems and, in particular, novel deep learning (DL) algorithms have been prompting a
way to take this problem.
This thesis has the aim to have an impact in biosignal research and industry by
presenting DL solutions that could empower this field. For this purpose an extensive study
of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive
Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed.
Different architecture configurations were explored for signal processing and decision
making and were implemented in three different scenarios: (1) Biosignal learning and
synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram
(ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate
autonomously three types of biosignals with a high degree of confidence. As for (2) three
CNN-based architectures, and a RNN-based architecture (same used in (1)) were used
for both biometric identification, reaching values above 90% for electrode-base datasets
(Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric
authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH
and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal
and detection of its deviation was made and tested in two different scenarios: presence of
noise using autoencoder and fully-connected network (reaching 99% accuracy for binary
classification and 71% for multi-class), and; arrhythmia events by including a RNN to the
previous architecture (57% accuracy and 61% sensitivity).
In sum, these systems are shown to be capable of producing novel results. The incorporation
of several AI systems into one could provide to be the next generation of
preventive medicine, as the machines have access to different physiological and anatomical
states, it could produce more informed solutions for the issues that one may face in the
future increasing the performance of autonomous preventing systems that could be used
in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context
as explainable AI could trigger this perception and link the inner states of a network with
the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes
da sociedade, enfrenta novos desafios desde o ínicio da década. A miríade de dados fisiológicos
gerados por indíviduos, nomeadamente no sistema de saúde, está a gerar um fardo
para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de
informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda
(DL) têm sido usados na procura de uma solução para este problema.
Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais,
apresentando soluções de DL que poderiam melhorar esta área de investigação. Para
esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais
convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para
o estudo de biosinais.
Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de
sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e síntese de
biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema
de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz
de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança.
Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN
(a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores
acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT
-BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo
taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para
CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção
do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruído
usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão
na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN
na arquitetura anterior (57 % de precisão e 61 % de sensibilidade).
Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir
resultados inovadores. A incorporação de vários sistemas de inteligência artificial em
um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os
algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir
soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida
quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes
sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no
contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os
estados internos de uma rede às características biológicas
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
Establishing a Framework for the development of Multimodal Virtual Reality Interfaces with Applicability in Education and Clinical Practice
The development of Virtual Reality (VR) and Augmented Reality (AR) content with
multiple sources of both input and output has led to countless contributions in a great
many number of fields, among which medicine and education.
Nevertheless, the actual process of integrating the existing VR/AR media and subsequently
setting it to purpose is yet a highly scattered and esoteric undertaking. Moreover,
seldom do the architectures that derive from such ventures comprise haptic feedback in
their implementation, which in turn deprives users from relying on one of the paramount
aspects of human interaction, their sense of touch.
Determined to circumvent these issues, the present dissertation proposes a centralized
albeit modularized framework that thus enables the conception of multimodal VR/AR
applications in a novel and straightforward manner.
In order to accomplish this, the aforesaid framework makes use of a stereoscopic VR
Head Mounted Display (HMD) from Oculus Rift©, a hand tracking controller from Leap
Motion©, a custom-made VR mount that allows for the assemblage of the two preceding
peripherals and a wearable device of our own design. The latter is a glove that encompasses
two core modules in its innings, one that is able to convey haptic feedback to its
wearer and another that deals with the non-intrusive acquisition, processing and registering
of his/her Electrocardiogram (ECG), Electromyogram (EMG) and Electrodermal
Activity (EDA). The software elements of the aforementioned features were all interfaced
through Unity3D©, a powerful game engine whose popularity in academic and scientific
endeavors is evermore increasing.
Upon completion of our system, it was time to substantiate our initial claim with
thoroughly developed experiences that would attest to its worth. With this premise
in mind, we devised a comprehensive repository of interfaces, amid which three merit
special consideration: Brain Connectivity Leap (BCL), Ode to Passive Haptic Learning
(PHL) and a Surgical Simulator
Hardware-software design of embedded systems for intelligent sensing applications
This Thesis wants to highlight the importance of ad-hoc designed and developed embedded systems in the implementation of intelligent sensor networks. As evidence four areas of application are presented: Precision Agriculture, Bioengineering, Automotive and Structural Health Monitoring. For each field is reported one, or more, smart device design and developing, in addition to on-board elaborations, experimental validation and in field tests. In particular, it is presented the design and development of a fruit meter. In the bioengineering field, three different projects are reported, detailing the architectures implemented and the validation tests conducted. Two prototype realizations of an inner
temperature measurement system in electric motors for an automotive application are then discussed. Lastly, the HW/SW design of a Smart Sensor Network is analyzed: the network features on-board data management and processing, integration in an IoT toolchain,
Wireless Sensor Network developments and an AI framework for vibration-based structural assessment
Imagining & Sensing: Understanding and Extending the Vocalist-Voice Relationship Through Biosignal Feedback
The voice is body and instrument. Third-person interpretation of the voice by listeners, vocal teachers, and digital agents is centred largely around audio feedback. For a vocalist, physical feedback from within the body provides an additional interaction. The vocalist’s understanding of their multi-sensory experiences is through tacit knowledge of the body. This knowledge is difficult to articulate, yet awareness and control of the body are innate. In the ever-increasing emergence of technology which quantifies or interprets physiological processes, we must remain conscious also of embodiment and human perception of these processes. Focusing on the vocalist-voice relationship, this thesis expands knowledge of human interaction and how technology influences our perception of our bodies. To unite these different perspectives in the vocal context, I draw on mixed methods from cog- nitive science, psychology, music information retrieval, and interactive system design. Objective methods such as vocal audio analysis provide a third-person observation. Subjective practices such as micro-phenomenology capture the experiential, first-person perspectives of the vocalists them- selves. Quantitative-qualitative blend provides details not only on novel interaction, but also an understanding of how technology influences existing understanding of the body. I worked with vocalists to understand how they use their voice through abstract representations, use mental imagery to adapt to altered auditory feedback, and teach fundamental practice to others. Vocalists use multi-modal imagery, for instance understanding physical sensations through auditory sensations. The understanding of the voice exists in a pre-linguistic representation which draws on embodied knowledge and lived experience from outside contexts. I developed a novel vocal interaction method which uses measurement of laryngeal muscular activations through surface electromyography. Biofeedback was presented to vocalists through soni- fication. Acting as an indicator of vocal activity for both conscious and unconscious gestures, this feedback allowed vocalists to explore their movement through sound. This formed new perceptions but also questioned existing understanding of the body. The thesis also uncovers ways in which vocalists are in control and controlled by, work with and against their bodies, and feel as a single entity at times and totally separate entities at others. I conclude this thesis by demonstrating a nuanced account of human interaction and perception of the body through vocal practice, as an example of how technological intervention enables exploration and influence over embodied understanding. This further highlights the need for understanding of the human experience in embodied interaction, rather than solely on digital interpretation, when introducing technology into these relationships
Aerospace Medicine and Biology: A cumulative index to a continuing bibliography
This publication is a cumulative index to the abstracts contained in Supplements 138 through 149 of AEROSPACE MEDICINE AND BIOLOGY: A CONTINUING BIBLIOGRAPHY. It includes three indexes -- subject, personal author, and corporate source
Personalized data analytics for internet-of-things-based health monitoring
The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months
Runtime Hardware Reconfiguration in Wireless Sensor Networks for Condition Monitoring
The integration of miniaturized heterogeneous electronic components has enabled the deployment of tiny sensing platforms empowered by wireless connectivity known as wireless sensor networks. Thanks to
an optimized duty-cycled activity, the energy consumption of these battery-powered devices can be reduced to a level where several years of operation is possible. However, the processing capability of currently available wireless sensor nodes does not scale well with the observation of phenomena requiring a high sampling resolution. The large amount of data generated by the sensors cannot be handled efficiently by low-power wireless communication protocols without a preliminary filtering of the information relevant for the application. For this purpose, energy-efficient, flexible, fast and accurate processing units are required to extract important features from the sensor data and relieve the operating system from computationally demanding tasks. Reconfigurable hardware is identified as a suitable technology to fulfill these requirements, balancing implementation
flexibility with performance and energy-efficiency.
While both static and dynamic power consumption of field programmable gate arrays has often been pointed out as prohibitive for very-low-power applications, recent programmable logic chips based on non-volatile memory appear as a potential solution overcoming this constraint. This thesis first verifies this assumption with the help of a modular sensor node built around a field programmable gate array based on Flash technology. Short and autonomous duty-cycled operation combined with hardware acceleration efficiently drop the energy consumption of the device in the considered context.
However, Flash-based devices suffer from restrictions such as long configuration times and limited resources, which reduce their suitability for complex processing tasks. A template of a dynamically
reconfigurable architecture built around coarse-grained reconfigurable function units is proposed in a second part of this work to overcome these issues. The module is conceived as an overlay of the sensor node FPGA increasing the implementation flexibility and introducing a standardized programming model. Mechanisms for virtual reconfiguration tailored for resource-constrained systems are introduced to minimize the overhead induced by this genericity.
The definition of this template architecture leaves room for design space exploration and application- specific customization. Nevertheless, this aspect must be supported by appropriate design tools which facilitate and automate the generation of low-level design files. For this purpose, a software tool is introduced to graphically configure the architecture and operation of the hardware accelerator. A middleware service is further integrated into the wireless sensor
network operating system to bridge the gap between the hardware and the design tools, enabling remote reprogramming and scheduling of the hardware functionality at runtime.
At last, this hardware and software toolchain is applied to real-world wireless sensor network deployments in the domain of condition monitoring. This category of applications often require the complex analysis of signals in the considered range of sampling frequencies such as vibrations or electrical currents, making the proposed system ideally suited for the implementation. The flexibility of the approach is demonstrated by taking examples with heterogeneous algorithmic
specifications. Different data processing tasks executed by the sensor node hardware accelerator are modified at runtime according to application requests