520 research outputs found
The prediction of fatigue using speech as a biosignal
Automatic systems for estimating operator fatigue have application in safety-critical environments. We develop and evaluate a system to detect fatigue from speech recordings collected from speakers kept awake over a 60-hour period. A binary classification system (fatigued/not-fatigued) based on time spent awake showed good discrimination, with 80 % unweighted accuracy using raw features, and 90 % with speaker-normalized features. We describe the data collection, feature analysis, machine learning and cross-validation used in the study. Results are promising for real-world applications in domains such as aerospace, transportation and mining where operators are in regular verbal communication as part of their normal working activities
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Uncertainty Quantification (UQ) has gained traction in an attempt to fix the black-box nature of Deep Learning. Specifically (medical) biosignals such as electroencephalography (EEG), electrocardiography (ECG), electroocculography (EOG) and electromyography (EMG) could benefit from good UQ, since these suffer from a poor signal to noise ratio, and good human interpretability is pivotal for medical applications and Brain Computer Interfaces. In this paper, we review the state of the art at the intersection of Uncertainty Quantification and Biosignal with Machine Learning. We present various methods, shortcomings, uncertainty measures and theoretical frameworks that currently exist in this application domain. Overall it can be concluded that promising UQ methods are available, but that research is needed on how people and systems may interact with an uncertainty model in a (clinical) environment
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Uncertainty Quantification (UQ) has gained traction in an attempt to fix the
black-box nature of Deep Learning. Specifically (medical) biosignals such as
electroencephalography (EEG), electrocardiography (ECG), electroocculography
(EOG) and electromyography (EMG) could benefit from good UQ, since these suffer
from a poor signal to noise ratio, and good human interpretability is pivotal
for medical applications and Brain Computer Interfaces. In this paper, we
review the state of the art at the intersection of Uncertainty Quantification
and Biosignal with Machine Learning. We present various methods, shortcomings,
uncertainty measures and theoretical frameworks that currently exist in this
application domain. Overall it can be concluded that promising UQ methods are
available, but that research is needed on how people and systems may interact
with an uncertainty model in a (clinical) environment.Comment: 26 pages, 13 figures, 3 table
Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle
Unobtrusive in-vehicle health monitoring has the potential to use the driving time to perform regular medical check-ups. This work intends to provide a guide to currently proposed sensor systems for in-vehicle monitoring and to answer, in particular, the questions: (1) Which sensors are suitable for in-vehicle data collection? (2) Where should the sensors be placed? (3) Which biosignals or vital signs can be monitored in the vehicle? (4) Which purposes can be supported with the health data? We reviewed retrospective literature systematically and summarized the up-to-date research on leveraging sensor technology for unobtrusive in-vehicle health monitoring. PubMed, IEEE Xplore, and Scopus delivered 959 articles. We firstly screened titles and abstracts for relevance. Thereafter, we assessed the entire articles. Finally, 46 papers were included and analyzed. A guide is provided to the currently proposed sensor systems. Through this guide, potential sensor information can be derived from the biomedical data needed for respective purposes. The suggested locations for the corresponding sensors are also linked. Fifteen types of sensors were found. Driver-centered locations, such as steering wheel, car seat, and windscreen, are frequently used for mounting unobtrusive sensors, through which some typical biosignals like heart rate and respiration rate are measured. To date, most research focuses on sensor technology development, and most application-driven research aims at driving safety. Health-oriented research on the medical use of sensor-derived physiological parameters is still of interest
Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Uncertainty Quantification (UQ) has gained traction in an attempt to fix the black-box nature of Deep Learning. Specifically (medical) biosignals such as electroencephalography (EEG), electrocardiography (ECG), electroocculography (EOG) and electromyography (EMG) could benefit from good UQ, since these suffer from a poor signal to noise ratio, and good human interpretability is pivotal for medical applications and Brain Computer Interfaces. In this paper, we review the state of the art at the intersection of Uncertainty Quantification and Biosignal with Machine Learning. We present various methods, shortcomings, uncertainty measures and theoretical frameworks that currently exist in this application domain. Overall it can be concluded that promising UQ methods are available, but that research is needed on how people and systems may interact with an uncertainty model in a (clinical) environment
A State of the Art Overview on Biosignal-based User-Adaptive Video Conferencing Systems
Video conferencing systems are widely used in times of distributed teams since they support flexible work arrangements. However, they have negative impacts on users, such as lacking eye gaze or zoom fatigue. Adaptive interventions in video conferences based on user behavior provide interesting solutions to overcome these challenges, for example, by alerting users when looking tired. Specifically, biosignals measured by sensors like microphones or eye-trackers are a promising basis for adaptive interventions. To provide an overview of current biosignal-based user-adaptive video conferencing systems, we conducted a systematic literature review and identified 24 publications. We summarize existing knowledge in a morphological box and outline further research directions. Thereby, a focus on biooptical signals is visible. Current adaptations target audience feedback, expression understanding and eye gaze mostly by image and representation modifications. In future, we recommend including further biosignals and addressing more diverse problems by investigating adaptation capabilities of further software elements
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
- …