Machine Learning Models for Mental Stress Classification based on Multimodal Biosignal Input

Mental stress is a largely prevalent condition directly or indirectly responsible for almost half of all work-related diseases. Work-Related Stress is the second most impactful occupational health problem in Europe, behind musculoskeletal diseases. When mental health is adequately handled, a worker’s well-being, performance, and productivity can be considerably improved. This thesis presents machine learning models to classify mental stress experienced by computer users using physiological signals including heart rate, acquired using a smart- watch; respiration, derived from a smartphone’s acc placed on the chest; and trapezius electromyography, using proprietary electromyography sensors. Two interactive proto- cols were implemented to collect data from 12 individuals. Time and frequency domain features were extracted from the heart rate and electromyography signals, and statistical and temporal features were extracted from the derived respiration signal. Three algorithms: Support Vector Machine, Random Forest, and K-Nearest-Neighbor were employed for mental stress classification. Different input modalities were tested for the machine learning models: one for each physiological signal and a multimodal one, combining all of them. Random Forest obtained the best mean accuracy (98.5%) for the respiration model whereas K-Nearest-Neighbor attained higher mean accuracies for the heart rate (89.0%) left, right and total electromyography (98.9%, 99.2%, and 99.3%) models. KNN algorithm was also able to achieve 100% mean accuracy for the multimodal model. A possible future approach would be to validate these models in real-time.O stress mental é uma condição amplamente prevalente direta ou indiretamente responsável por quase metade de todas doenças relacionadas com trabalho. O stress expe- rienciado no trabalho é o segundo problema de saúde ocupacional com maior impacto na Europa, depois das doenças músculo-esqueléticas. Quando a saúde mental é adequada- mente cuidada, o bem-estar, o desempenho e a produtividade de um trabalhador podem ser consideravelmente melhorados. Esta tese apresenta modelos de aprendizagem automática que classificam o stress mental experienciado por utilizadores de computadores recorrendo a sinais fisiológi- cos, incluindo a frequência cardíaca, adquirida pelo sensor de fotopletismografia de um smartwatch; a respiração, derivada de um acelerómetro incorporado no smartphone po- sicionado no peito; e electromiografia de cada um dos músculos trapézios, utilizando sensores electromiográficos proprietários. Foram implementados dois protocolos inte- ractivos para recolha de dados de 12 indivíduos. Características do domínio temporal e de frequência foram extraídas dos sinais de frequência cardíaca e electromiografia, e características estatísticas e temporais foram extraídas do sinal respiratório. Três algoritmos entitulados K-Nearest-Neighbor, Random Forest, e Support Vector Machine foram utilizados para a classificação do stress mental. Foram testadas diferentes modalidades de dados para os modelos de aprendizagem automática: uma para cada sinal fisiológico e uma multimodal, combinando os três. O Random Forest obteve a melhor precisão média (98,5%) para o modelo de respiração enquanto que o K-Nearest-Neighbor atingiu uma maior precisão média nos modelos de frequência cardíaca (89,0%) e electro- miografia esquerda, direita e total (98,9%, 99,2%, e 99,3%). O algoritmo KNN conseguiu ainda atingir uma precisão média de 100% para o modelo multimodal. Uma possível abordagem futura seria efetuar uma validação destes modelos em tempo real

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research

An energy-efficient hardware system for robust and reliable heart rate monitoring

Cardiac arrhythmia, one of the most common causes of death in the world today, is not always effectively detected by regular examinations, as it usually occurs infrequently and suddenly. Therefore, real-time, continuous monitoring of the heart rate is needed to detect arrhythmia problems sooner and prevent their severe consequences. To make continuous monitoring possible and give it widespread acceptance, a portable heart rate monitoring system must have three key characteristics: (1) accuracy, (2) portability, and (3) long battery life. Previous studies have focused on addressing these problems separately, either improving the accuracy of the monitoring algorithm or the efficiency of the underlying hardware. This thesis proposes a robust and reliable heart rate monitoring system (RRHMS), in which both algorithm accuracy and hardware efficiency are considered. As a result, algorithmic optimizations are exploited to enable further hardware efficiency. In the RRHMS, robust heart rate monitoring is achieved by extracting heart rates from both electrocardiogram (ECG) and arterial blood pressure (ABP) signals and fusing them based on the signal qualities. Therefore, accurate heart rate data can be provided continuously, even when one signal is severely corrupted. Algorithmic optimizations are applied to merge the separate ECG and ABP processing steps into shared ones, which allows shared hardware modules and hence low-area (portable) hardware design. Also, an embedded hardware architecture framework is proposed for the design of the RRHMS hardware system. Coarse-grained functional units (FUs) can be easily added or removed in this framework, allowing for application-specific hardware optimization. Further, the application invariant properties are used to achieve low-overhead fault tolerance in the FUs to enhance reliability. Both ASIC and FPGA implementations of the RRHMS are able to accurately detect heart rates in real time while consuming only 1/2870 and 1/923 of the energy required by the Android implementation

Continuous Estimation of Smoking Lapse Risk from Noisy Wrist Sensor Data Using Sparse and Positive-Only Labels

Estimating the imminent risk of adverse health behaviors provides opportunities for developing effective behavioral intervention mechanisms to prevent the occurrence of the target behavior. One of the key goals is to find opportune moments for intervention by passively detecting the rising risk of an imminent adverse behavior. Significant progress in mobile health research and the ability to continuously sense internal and external states of individual health and behavior has paved the way for detecting diverse risk factors from mobile sensor data. The next frontier in this research is to account for the combined effects of these risk factors to produce a composite risk score of adverse behaviors using wearable sensors convenient for daily use. Developing a machine learning-based model for assessing the risk of smoking lapse in the natural environment faces significant outstanding challenges requiring the development of novel and unique methodologies for each of them. The first challenge is coming up with an accurate representation of noisy and incomplete sensor data to encode the present and historical influence of behavioral cues, mental states, and the interactions of individuals with their ever-changing environment. The next noteworthy challenge is the absence of confirmed negative labels of low-risk states and adequate precise annotations of high-risk states. Finally, the model should work on convenient wearable devices to facilitate widespread adoption in research and practice. In this dissertation, we develop methods that account for the multi-faceted nature of smoking lapse behavior to train and evaluate a machine learning model capable of estimating composite risk scores in the natural environment. We first develop mRisk, which combines the effects of various mHealth biomarkers such as stress, physical activity, and location history in producing the risk of smoking lapse using sequential deep neural networks. We propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of smoking lapse. To circumvent the lack of confirmed negative labels (i.e., annotated low-risk moments) and only a few positive labels (i.e., sensor-based detection of smoking lapse corroborated by self-reports), we propose a new loss function to accurately optimize the models. We build the mRisk models using biomarker (stress, physical activity) streams derived from chest-worn sensors. Adapting the models to work with less invasive and more convenient wrist-based sensors requires adapting the biomarker detection models to work with wrist-worn sensor data. To that end, we develop robust stress and activity inference methodologies from noisy wrist-sensor data. We first propose CQP, which quantifies wrist-sensor collected PPG data quality. Next, we show that integrating CQP within the inference pipeline improves accuracy-yield trade-offs associated with stress detection from wrist-worn PPG sensors in the natural environment. mRisk also requires sensor-based precise detection of smoking events and confirmation through self-reports to extract positive labels. Hence, we develop rSmoke, an orientation-invariant smoking detection model that is robust to the variations in sensor data resulting from orientation switches in the field. We train the proposed mRisk risk estimation models using the wrist-based inferences of lapse risk factors. To evaluate the utility of the risk models, we simulate the delivery of intelligent smoking interventions to at-risk participants as informed by the composite risk scores. Our results demonstrate the envisaged impact of machine learning-based models operating on wrist-worn wearable sensor data to output continuous smoking lapse risk scores. The novel methodologies we propose throughout this dissertation help instigate a new frontier in smoking research that can potentially improve the smoking abstinence rate in participants willing to quit