A Wearable Device For Physical and Emotional Health Monitoring

Personal health monitoring systems are emerging as promising solutions to develop ultra-small, portable devices that can continuously monitor and process several vital body parameters. In this work, we present a wearable device for physical and emotional health monitoring. The device obtains user’s key physiological signals: ECG, respiration, Impedance Cardiogram (ICG), blood pressure and skin conductance and derives the user’s emotion states as well. We have developed embedded algorithms that process the bio-signals in real-time to detect any abnormalities (cardiac arrhythmias and morphology changes) in the ECG and to detect key parameters (such as the Pre- Ejection Period and fluid status level) from the ICG. We present a novel method to detect continuous beat-by-beat blood pressure from the ECG and ICG signals, as well as a realtime embedded emotion classifier that computes the emotion levels of the user. Emotions are classified according to their attractiveness (positive valence) or their averseness (negative valence) in the horizontal valence dimension. The excitement level induced by the emotions is represented by high to low positions in the vertical arousal dimension of the valence-arousal space. The signals are measured either intermittently by touching the metal electrodes on the device (for point-of-care testing) or continuously, using a chest strap for long term monitoring. The processed data from device is sent to a mobile phone using a Bluetooth Low Energy protocol. Our results show that the device can monitor the signals continuously, providing accurate detection of the motion state, for over 72 hours on a single battery charge

Real-time classification technique for early detection and prevention of myocardial infarction on wearable devices

Continuous monitoring of patients suffering from cardiovascular diseases and, in particular, myocardial infarction (MI) places a considerable burden on health-care systems and government budgets. The rise of wearable devices alleviates this burden, allowing for long-term patient monitoring in ambulatory settings. One of the major challenges in this area is to design ultra-low energy wearable devices for long-term monitoring of patients’ vital signs. In this work, we present a real-time event-driven classification technique, based on support vector machines (SVM) and statistical outlier detection. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. This technique leads to a reduction in energy consumption and thus battery lifetime extension. We validate our approach on a well-established and complete myocardial infarction (MI) database (Physiobank, PTB Diagnostic ECG database [1]). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 3, while maintaining the classification accuracy at a medically-acceptable level of 90%

Self-Aware Wearable Systems in Epileptic Seizure Detection

Today, wearable systems are facing fundamental barriers in terms of battery lifetime and quality of their results. The main challenge in wearable systems is to increase the battery lifetime, while maintaining the machine-learning performance of the system. A recently proposed concept for overcoming this challenge is self-awareness, which increases system's knowledge of itself and the surrounding environment. This is precisely what health monitoring wearable systems require to adapt to different situations. To demonstrate the impact of introducing self-awareness in wearable technologies, we consider the epileptic seizure detection problem, as a case study. Epilepsy affects around 1% of the world's population, which can dramatically degrade the quality of life and represents a major public health issue. As a result, detection of epileptic seizures has become more important over the past decades. In this paper, we aim to introduce a new generation of self-aware wearable systems to decrease energy consumption and improve their seizures detection capabilities by introducing the notion of self-awareness in such systems. These techniques include switching to low-power mode to reduce the energy consumption and machine-learning model enhancement to improve detection quality. We incorporated our proposed techniques in the machine learning module, which detects epileptic seizures by monitoring the cardiac and respiratory systems. We evaluated the performance of our approach based on an epilepsy database of more than 141 hours, provided by the Lausanne University Hospital (CHUV). Our self-aware wearable system achieves 36% reduction in computational complexity and 10.51% improvement in detection performance

Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems

A considerable portion of government health-care spending is allocated to the continuous monitoring of patients suffering from cardiovascular diseases, particularly myocardial infarction (MI). Wearable devices present a cost-effective means of monitoring patients’ vital signs in ambulatory settings. A major challenge is to design such ultra-low energy devices for long-term patient monitoring. In this paper, we present a real-time event-driven classification technique based on the random forest classification scheme, which uses a confidence-related decision-making process. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. We validate our approach on a well-established and complete MI database (Physiobank, PTB Diagnostic ECG database [1]). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 2.60, with no classification quality loss

Survey of contributions for a pipeline of emotion recognition and awareness - context variables, instruments & sensors, pre-processing techniques and extracted properties for automatic recognition of emotions

A avaliação emocional tem sido uma área de investigação, desde há muitos anos, na área da saúde e na área psicossocial. Foi a partir da década de 90 que o reconhecimento de emoções ganhou mais atenção por parte dos investigadores, tornando-se num importante tópico de investigação até aos dias de hoje (Basu, Bag, Mahadevappa, Mukherjee, & Guha, 2016). Segundo Picard, o estudo das emoções moveu-se da psicologia para a área da computação, criando um novo campo de investigação chamado de Affective Computing (AC). Aliás, no seu livro “Affective Computing”, indica as bases para a criação de um sistema inteligente para deteção emocional de forma automática (R. W. Picard, 1995). Nos últimos anos, tem-se presenciado a um aumento deste tipo de investigações, talvez pela necessidade de transformar a relação entre as coisas (e.g. hardware, software e produtos em geral) e as pessoas, numa interação mais inteligente e natural (R. Picard & Klein, 2002), transformando assim o AC num tópico importante de investigação (Bos, 2010). Vários autores consideram que a deteção automática de emoções poderá ter um impacto positivo na vida das pessoas. Por exemplo, a área da psicologia poderá beneficiar, com menos subjetividade, de dados contínuos e menos diferidos no tempo; a saúde poderá ser avaliada com informação complementar à fisiológica; poderá ser mais fácil detetar delitos como atos de delinquência e atentados terroristas; e será mais fácil desenhar produtos especializados em provocar ou transmitir emoções no mundo virtual (Murad & Malkawi, 2012). Poderá também ser possível criar sistemas inteligentes do ponto de vista afetivo, conscientes ao nível emocional, capazes de percecionar e reagir às emoções dos utilizadores. Apesar de existirem já vários estudos com o objetivo de detetar automaticamente emoções, os autores acreditam que a correlação de variáveis sociais, culturais e religiosas, com as fisiológicas, poderá contribuir de forma positiva para a qualidade dos resultados obtidos. Neste contexto, está-se a preparar uma experiência para detetar automaticamente o bem-estar nos trabalhadores de escritório. Pretende-se recolher variáveis de contexto de várias modalidades e, depois do respetivo pré-processamento, usar esses dados como input de algoritmos de Machine Learning (ML) para a respetiva classificação. O objetivo é verificar a possibilidade de criar sistemas inteligentes do ponto de vista afetivo, conscientes ao nível emocional, capazes de percecionar e reagir às emoções dos funcionários de escritório. Este relatório resume as obras estudadas pelos autores na área do AC na revisão bibliográfica sobre o tema. Sugere-se um sistema de tokens para melhor categorização da informação, e propõe-se também uma sistematização da informação através da organização desses tokens em quadros resumo, para permitir uma análise agregada das investigações. Na secção seguinte são resumidas as variáveis de contexto e propriedades de domínio utilizadas pelos autores. Depois são apresentados os instrumentos & sensores utilizados na recolha das variáveis de contexto. Posteriormente são resumidas as técnicas de pré-processamento utilizadas. Conclui-se com uma enumeração das propriedades extraídas mais utilizadas nas obras estudadas.Emotional assessment has been a research area of health and psychosocial field, since many years. It was from 90’s that the recognition of emotions gained more attention from the researchers, becoming an importante topic of research up to today (Basu, Bag, Mahadevappa, Mukherjee, & Guha, 2016). According to Picard, the study of emotions moved from psychology to the area of computing, creating a new research field called Affective Computing (AC). In fact, in her book “Affective Computing”, she indicates the basis for creating na intelligent system for automatic emotional detection (R. W. Picard, 1995). In recent years, there has been an increase in this kind of research, perhaps due the need to transform the interaction between things (e.g. hardware, software and products in general) and people more natural and intelligent (R. Picard & Klein, 2002). This transformed the AC in an important research topic (Bos, 2010). Several authors believe that the automatic emotional detection can have positive impacto on people’s lives. As an exemple, the area of psychology may benefit with less subjectivity, continuous and less deferred data in time; health can be assessed with additional info besides physiological data; it may be easier to detect crimes such as acts of delinquency and terrorist attacks; and it will be easier to design products specialized in provoking or transmitting emotions in the virtual world (Murad & Malkawi, 2012). It may also be possible to create intelligent affective systems. Emotion-aware systems that can understanding and react to people emotions. Although there are already several studies with the objective of automatically detecting emotions, the authors believe that the correlation of social, cultural and religious variables with physiological ones, may contribute positively to the quality of the results obtained. In this context, an experiment is being prepared to automatically detect the well-being of office workers. It is intended to collect context variables of several modalities and, after the pre- processing phase, use that data as input to Machine Learning (ML) classification algorithms. The goal is to verify the possibility of creating intelligent systems from an affective point of view, conscious at the emotional level, capable of perceiving and reacting to the emotions of office workers. This technical report summarizes the studied researchs by the authors during the bibliographic review on the AC topic. A token system is suggested for better categorization of information, and a systematization of information is also proposed through the organization of these tokens in summary tables, to allow an aggregated analysis of the investigations. The following section summarizes the context variables and domain properties used by the authors. Then, the instruments & sensors used to collect the context variables are presented. Subsequently, the pre-processing techniques used are summarized. It concludes with an enumeration of the extracted properties most used in the studied works.info:eu-repo/semantics/draf