46 research outputs found
Personalization of convolutional neural networks within the stress detection task using heart rate variability data
Stress detection is an active area of research with important implications for personal, occupational, and social health.
Most modern approaches use features computed from multiple sensor modalities, i.e., grouping different types of
data from multiple sources for processing. These include electrocardiogram, electrodermal activity, electromyogram,
skin temperature, respiration, accelerometer data, etc. Also, traditional machine learning algorithms (decision tree,
discriminant analysis, support vector machine, etc.) or fully-connected neural networks are mostly used. Using these
methods requires large amounts of data. Researchers are considering different approaches to personalization or
generalization of models relative to subjects, namely subject-independent and subject-dependent (initially personal or
adapted) models. The aim of the presented work is to develop a method for detecting stress based on heart rate variability
data, taking into account the process of personalization of neural networks. The use of a convolutional neural network
is proposed. The dependence of accuracy on the length of the input signal is studied. The dependence of accuracy on
the data dimensionality reduction layer (one-dimensional convolutional layer, maximizing and averaging pooling) used
in the network is also considered. The importance of personalizing models is demonstrated to significantly increase the
accuracy of models of specific subjects. It is shown that the proposed method, based on 60 intervals between heartbeats,
makes it possible to binary determine whether a person is under stress. Personalization allowed increasing the accuracy
from 91.8 % to 98.9 ± 2.6 %. The F1-score value increased from 0.907 to 0.983 ± 0.038. The proposed personalized
networks can be used in systems for monitoring the functional state of a person. They can also be used as part of a system
that grants or restricts access to private resources based on whether a person is currently at rest
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Tangible fidgeting interfaces for mental wellbeing recognition using deep learning applied to physiological sensor data
The momentary assessment of an individual's affective state is critical to the monitoring of mental wellbeing and the ability to instantly apply interventions. This thesis introduces the concept of tangible fidgeting interfaces for affective recognition from design and development through to evaluation. Tangible interfaces expand upon the affordance of familiar physical objects as the ability to touch and fidget may help to tap into individuals' psychological need to feel occupied and engaged. Embedding digital technologies within interfaces capitalises on motor and perceptual capabilities and allows for the direct manipulation of data, offering people the potential for new modes of interaction when experiencing mental wellbeing challenges.
Tangible interfaces present an ideal opportunity to digitally enable physical fidgeting interactions along with physiological sensor monitoring to unobtrusively and comfortable measure non-visable changes in affective state. This opportunity initiated the investigation of factors that would bring about the designing of more effective intelligent solutions using participatory design techniques to engage people in designing solutions relevant to themselves.
Adopting an artificial intelligence approach using physiological signals creates the possibility to quantify affect with high levels of accuracy. However, labelling is an indispensable stage of data pre-processing that is required before classification and can be extremely challenging with multi-model sensor data. New techniques are introduced for labelling at the point of collection coupled with a pilot study and a systematic performance comparison of five custom built labelling interfaces.
When classifying labelled physiological sensor data, individual differences between people limit the generalisability of models. To address this challenge, a transfer learning approach has been developed that personalises affective models using few labelled samples. This approach to personalise models and improve cross-domain performance is completed on-device, automating the traditionally manual process, saving time and labour. Furthermore, monitoring trajectories over long periods of time inherits some critical limitations in relation to the size of the training dataset. This shortcoming may hinder the development of reliable and accurate machine learning models. A second framework has been developed to overcome the limitation of small training datasets using an image-encoding transfer learning approach.
This research offers the first attempt at the development of tangible interfaces using artificial intelligence towards building a real-world continuous affect recognition system in addition to offering real-time feedback to perform as interventions. This exploration of affective interfaces has many potential applications to help improve quality of life for the wider population
Beyond mobile apps: a survey of technologies for mental well-being
Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties, which facilitate the delivery of state-of-the-art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy, and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health toolkits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the data these ubiquitous devices can record, state of the art machine learning algorithms can lead to the development of robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time
A state-of-the-art of physics-informed neural networks in engineering
TĂ©cnicas de machine learning vĂŞm ganhando cada vez mais espaço no cenário industrial no intuito de converter o crescente fluxo de informação (data) em melhorias de processos. Entre tais tĂ©cnicas, as redes neuronais se destacam devido Ă sua capacidade de aproximador universal de funções, cuja performance pode ser enriquecida ao se fornecer conhecimentos fĂsicos prĂ©vios: tem-se, entĂŁo, o desenvolvimento das
Physics-informed neural networks (PINN). Nesse contexto e observando-se um “gap” na produção de trabalhos relacionados ao tema e da difusĂŁo dessa temática na grade de formação dos cursos da Escola de QuĂmica, esse trabalho se propõe a realizar um estado da arte da tĂ©cnica mencionada. Observou-se interesse particular das PINN para aplicações em mecânica dos fluidos e transferĂŞncia de calor. Ademais, as PINN
se mostram ferramentas importantes tanto para a resolução de problemas ditos “diretos” quanto “indiretos”. Por fim, atravĂ©s de exemplos práticos, constatou-se a capacidade de se aproximar funções de interesse particular na indĂşstria quĂmica usando-se redes neurais sem nenhuma informação fĂsica do problema (obtenção do fator de atrito) e utilizando-se a equação diferencial que descreve o problema (resolução da equação de difusĂŁo em 1D)
ERS International Congress 2020 Virtual: highlights from the Allied Respiratory Professionals Assembly
This article provides an overview of outstanding sessions that were (co)organised by the Allied
Respiratory Professionals Assembly during the European Respiratory Society International Congress 2020,
which this year assumed a virtual format. The content of the sessions was mainly targeted at allied
respiratory professionals, including respiratory function technologists and scientists, physiotherapists, and
nurses. Short take-home messages related to spirometry and exercise testing are provided, highlighting the
importance of quality control. The need for quality improvement in sleep interventions is underlined as it
may enhance patient outcomes and the working capacity of healthcare services. The promising role of digital
health in chronic disease management is discussed, with emphasis on the value of end-user participation in
the development of these technologies. Evidence on the effectiveness of airway clearance techniques in
chronic respiratory conditions is provided along with the rationale for its use and challenges to be addressed
in future research. The importance of assessing, preventing and reversing frailty in respiratory patients is
discussed, with a clear focus on exercise-based interventions. Research on the impact of disease-specific fear
and anxiety on patient outcomes draws attention to the need for early assessment and intervention. Finally,
advances in nursing care related to treatment adherence, self-management and patients’ perspectives in
asthma and chronic obstructive pulmonary disease are provided, highlighting the need for patient
engagement and shared decision making. This highlights article provides readers with valuable insight into
the latest scientific data and emerging areas affecting clinical practice of allied respiratory professionals.European CommissionFWOCenter for Innovative Care and Health Technology (ciTechCare) of the Polytechnic of Leiria - Fundacao para a Ciencia e Tecnologia (FCT)
UIDB/05704/2020
UIDP/05704/202
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Digital phenotyping through multimodal, unobtrusive sensing
The growing adoption of multimodal wearable and mobile devices, such as smartphones and wrist-worn watches has generated an increase in the collection of physiological and behavioural data at scale. This digital phenotyping data enables researchers to make inferences regarding users’ physical and mental health at scale, for the first time. However, translating this data into actionable insights requires computational approaches that turn unlabelled, multimodal time-series sensor data into validated measures that can be interpreted at scale.
This thesis describes the derivation of novel computational methods that leverage digital phenotyping data from wearable devices in large-scale populations to infer physical behaviours. These methods combine insights from signal processing, data mining and machine learning alongside domain knowledge in physical activity and sleep epidemiology. First, the inference of sleeping windows in free-living conditions through a heart rate sensing approach is explored. This algorithm is particularly valuable in the absence of ground truth or sleep diaries given its simplicity, adaptability and capacity for personalization. I then explore multistage sleep classification through combined movement and cardiac wearable sensing and machine learning. Further, I demonstrate that postural changes detected through wrist accelerometers can inform habitual behaviours and are valuable complements to traditional, intensity-based physical activity metrics. I then leverage the concomitant responses of heart rate to physical activity that can be captured through multimodal wearable sensors through a self-supervised training task. The resulting embeddings from this task are shown to be useful for the downstream classification of demographic factors, BMI, energy expenditure and cardiorespiratory fitness. Finally, I describe a deep learning model for the adaptive inference of cardiorespiratory fitness (VO2max) using wearable data in free living conditions. I demonstrate the robustness of the model in a large UK population and show the models’ adaptability by evaluating its performance in a subset of the population with repeated measures ~6 years after the original recordings.
Together, this work increases the potential of multimodal wearable and mobile sensors for physical activity and behavioural inferences in population studies. In particular, this thesis showcases the potential of using wearable devices to make valuable physical activity, sleep and fitness inferences in large cohort studies. Given the nature of the data collected and the fact that most of this data is currently generated by commercial providers and not research institutes, laying the foundations for responsible data governance and ethical use of these technologies will be critical to building trust and enabling the development of the field of digital phenotyping.I was funded by GlaxoSmithKline and the Engineering and Physical Sciences Research Council. I was also supported by the Alan Turing Institute through their Enrichment Scheme
Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results
Humour is a substantial element of human affect and cognition. Its automatic
understanding can facilitate a more naturalistic human-device interaction and
the humanisation of artificial intelligence. Current methods of humour
detection are solely based on staged data making them inadequate for
'real-world' applications. We address this deficiency by introducing the novel
Passau-Spontaneous Football Coach Humour (Passau-SFCH) dataset, comprising of
about 11 hours of recordings. The Passau-SFCH dataset is annotated for the
presence of humour and its dimensions (sentiment and direction) as proposed in
Martin's Humor Style Questionnaire. We conduct a series of experiments,
employing pretrained Transformers, convolutional neural networks, and
expert-designed features. The performance of each modality (text, audio, video)
for spontaneous humour recognition is analysed and their complementarity is
investigated. Our findings suggest that for the automatic analysis of humour
and its sentiment, facial expressions are most promising, while humour
direction can be best modelled via text-based features. The results reveal
considerable differences among various subjects, highlighting the individuality
of humour usage and style. Further, we observe that a decision-level fusion
yields the best recognition result. Finally, we make our code publicly
available at https://www.github.com/EIHW/passau-sfch. The Passau-SFCH dataset
is available upon request.Comment: This work has been submitted to the IEEE for possible publication.
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