Monitoring stress with a wrist device using context.
TL;DR: This work explores the problem of stress detection using machine learning and signal processing techniques in laboratory conditions, and then applies the extracted laboratory knowledge to real-life data to propose a novel context-based stress-detection method.
About: This article is published in Journal of Biomedical Informatics.The article was published on 2017-09-01 and is currently open access. It has received 221 citations till now. The article focuses on the topics: Context (language use).
Citations
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TL;DR: This survey will examine the recent works on stress detection in daily life which are using smartphones and wearable devices and investigate the works according to used physiological modality and their targeted environment such as office, campus, car and unrestricted daily life conditions.
255 citations
TL;DR: An automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals which has modality-specific artifact removal and feature extraction methods for real-life conditions.
Abstract: The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.
186 citations
Cites background or methods or result from "Monitoring stress with a wrist devi..."
...5 min have better accuracy in general [34], which is similar to our results....
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...[34] (2017) HR-IBI-HRV-EDATemperature Real Life Weka Toolkit 2 class(S, R) 70 Yes...
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...[34] employed activity recognition to increase the knowledge regarding context and improve their recognition performance....
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TL;DR: A taxonomy of sensors, functionalities, and methods used in non-invasive wrist-wearable devices was assembled and the main features of commercial wrist- wearable devices are presented.
Abstract: Wearable devices have recently received considerable interest due to their great promise for a plethora of applications. Increased research efforts are oriented towards a non-invasive monitoring of human health as well as activity parameters. A wide range of wearable sensors are being developed for real-time non-invasive monitoring. This paper provides a comprehensive review of sensors used in wrist-wearable devices, methods used for the visualization of parameters measured as well as methods used for intelligent analysis of data obtained from wrist-wearable devices. In line with this, the main features of commercial wrist-wearable devices are presented. As a result of this review, a taxonomy of sensors, functionalities, and methods used in non-invasive wrist-wearable devices was assembled.
180 citations
Cites background or methods from "Monitoring stress with a wrist devi..."
...• office environment and industry—to monitor employee’s health parameters, especially monitoring stress levels and to prevent the potential deterioration of health conditions caused by occupational stress [17,29,36,43,44]; and...
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...The changes arise when the skin receives specific signals from the brain [44]....
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...[44] presented a novel approach on stress detection, where stress is recognized in 2 and 20-min intervals from the signals (accelerometer, heart rate, EDA, the time between individual heartbeats, skin temperature) from wrist-wearable devices....
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...HR and HR Variability can be derived from the Blood Volume Pulse [44]....
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...It can be estimated from the skin temperature, which can be measured with infrared thermopile [44], thermistors, thermoelectic effects or via optical means [13]....
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TL;DR: The end-to-end learning approach takes the time-frequency spectra of synchronised PPG- and accelerometer-signals as input, and provides the estimated heart rate as output, and shows that on large datasets the deep learning model significantly outperforms other methods.
Abstract: Photoplethysmography (PPG)-based continuous heart rate monitoring is essential in a number of domains, eg, for healthcare or fitness applications Recently, methods based on time-frequency spectra emerged to address the challenges of motion artefact compensation However, existing approaches are highly parametrised and optimised for specific scenarios of small, public datasets We address this fragmentation by contributing research into the robustness and generalisation capabilities of PPG-based heart rate estimation approaches First, we introduce a novel large-scale dataset (called PPG-DaLiA), including a wide range of activities performed under close to real-life conditions Second, we extend a state-of-the-art algorithm, significantly improving its performance on several datasets Third, we introduce deep learning to this domain, and investigate various convolutional neural network architectures Our end-to-end learning approach takes the time-frequency spectra of synchronised PPG- and accelerometer-signals as input, and provides the estimated heart rate as output Finally, we compare the novel deep learning approach to classical methods, performing evaluation on four public datasets We show that on large datasets the deep learning model significantly outperforms other methods: The mean absolute error could be reduced by 31 % on the new dataset PPG-DaLiA, and by 21 % on the dataset WESAD
176 citations
Cites background from "Monitoring stress with a wrist devi..."
...[37] collected real-life data with the E4 to detect stress in unconstrained environments, or Di Lascio et al....
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TL;DR: An introduction to the field of affective computing is presented though the description of key theoretical concepts, and the current state-of-the-art of emotion recognition is described, tracing the developments that helped foster the growth of the field.
Abstract: The seminal work on Affective Computing in 1995 by Picard set the base for computing that relates to, arises from, or influences emotions. Affective computing is a multidisciplinary field of research spanning the areas of computer science, psychology, and cognitive science. Potential applications include automated driver assistance, healthcare, human-computer interaction, entertainment, marketing, teaching and many others. Thus, quickly, the field acquired high interest, with an enormous growth of the number of papers published on the topic since its inception. This paper aims to (1) Present an introduction to the field of affective computing though the description of key theoretical concepts; (2) Describe the current state-of-the-art of emotion recognition, tracing the developments that helped foster the growth of the field; and lastly, (3) point the literature take-home messages and conclusions, evidencing the main challenges and future opportunities that lie ahead, in particular for the development of novel machine learning (ML) algorithms in the context of emotion recognition using physiological signals.
114 citations
References
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Abstract: From the publisher: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.
13,736 citations
"Monitoring stress with a wrist devi..." refers methods in this paper
...SVM – an algorithm for building a classifier where the classification function is a hyperplane in the feature space [41]....
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TL;DR: This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.
Abstract: Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several real-world databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.
4,499 citations
14 Aug 1995
TL;DR: In this article, the authors proposed a method to construct tree-based classifiers whose capacity can be arbitrarily expanded for increases in accuracy for both training and unseen data, which can be monotonically improved by building multiple trees in different subspaces of the feature space.
Abstract: Decision trees are attractive classifiers due to their high execution speed. But trees derived with traditional methods often cannot be grown to arbitrary complexity for possible loss of generalization accuracy on unseen data. The limitation on complexity usually means suboptimal accuracy on training data. Following the principles of stochastic modeling, we propose a method to construct tree-based classifiers whose capacity can be arbitrarily expanded for increases in accuracy for both training and unseen data. The essence of the method is to build multiple trees in randomly selected subspaces of the feature space. Trees in, different subspaces generalize their classification in complementary ways, and their combined classification can be monotonically improved. The validity of the method is demonstrated through experiments on the recognition of handwritten digits.
2,957 citations
TL;DR: The present report meta-analyzes more than 300 empirical articles describing a relationship between psychological stress and parameters of the immune system in human participants to find that physical vulnerability as a function of age or disease also increased vulnerability to immune change during stressors.
Abstract: The present report meta-analyzes more than 300 empirical articles describing a relationship between psychological stress and parameters of the immune system in human participants. Acute stressors (lasting minutes) were associated with potentially adaptive upregulation of some parameters of natural immunity and downregulation of some functions of specific immunity. Brief naturalistic stressors (such as exams) tended to suppress cellular immunity while preserving humoral immunity. Chronic stressors were associated with suppression of both cellular and humoral measures. Effects of event sequences varied according to the kind of event (trauma vs. loss). Subjective reports of stress generally did not associate with immune change. In some cases, physical vulnerability as a function of age or disease also increased vulnerability to immune change during stressors.
2,756 citations
"Monitoring stress with a wrist devi..." refers background in this paper
...Chronical stress has negative health consequences, such as raised blood pressure, bad sleep, increased vulnerability to infections, decreased performance, and slower body recovery [1]....
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