Human Factors Analysis Using Wearable Sensors in the Context of Cognitive and Emotional Arousal (original) (raw)
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Quantitative investigations on stress conditions in evaluation scenarios have so far mainly been conducted in static scenarios, such as, in desktop studies. Studies involving the mobility of participants are rather rare, in particular, considering substantial comparison between different stress conditions. Recently, data and eye tracking glasses have shifted the attention on future applications classes that would require wearable devices delivering multisensory, including psychophysiological, data in various everyday contexts, requiring information about the psychological status of the user. These settings need to investigate stress conditions in different mobile settings, querying which parameters would provide discriminative features for stress indication. A study with 20 participants was conducted in a shopping and a navigation context, involving participants – being equipped with portable psychophysiological sensors and eye tracking glasses-in memory and orientation tasks, respectively, for the inducing of cognitive and emotional arousal. From the results we conclude that the specific context and the cause of arousal lead to different reactions of the psychophysiological system as well as in the eye movement behavior. Depending on the context and the stress condition under investigation, different arousal, and consequently, stress classifiers as well as attention models should be applied.
Lecture Notes in Electrical Engineering, 2019
Mental stress is a physiological state that directly correlates to the quality of life of individuals. Generally speaking, but especially true for disabled or elderly subjects, the assessment of such condition represents a very strong indicator correlated to the difficulties, and, in some case, to the frustration that derives from the execution of a task that results troublesome to be accomplished. This article describes a novel procedure for the assessment of the mental stress level through the use of low invasive wireless wearable devices. The information contained in electrocardiogram, respiratory signal, blood volume pulse, and electroencephalogram was extracted to set up an estimator for the cognitive workload level. A random forest classifier was implemented to assess the level of mental stress starting from a pool of 3481 features computed from the aforementioned physiological quantities. The proposed system was applied in a scenario in which two different mental states were elicited in the subject under investigation: first, a baseline resting condition was induced by the presentation of a relaxing video; then a stressful cognitive state was provoked by the
Wearable affect and stress recognition: A review
ArXiv, 2018
Affect recognition aims to detect a person's affective state based on observables, with the goal to e.g. provide reasoning for decision making or support mental wellbeing. Recently, besides approaches based on audio, visual or text information, solutions relying on wearable sensors as observables (recording mainly physiological and inertial parameters) have received increasing attention. Wearable systems offer an ideal platform for long-term affect recognition applications due to their rich functionality and form factor. However, existing literature lacks a comprehensive overview of state-of-the-art research in wearable-based affect recognition. Therefore, the aim of this paper is to provide a broad overview and in-depth understanding of the theoretical background, methods, and best practices of wearable affect and stress recognition. We summarise psychological models, and detail affect-related physiological changes and their measurement with wearables. We outline lab protocols ...
Sensors
The capability of monitoring user’s performance represents a crucial aspect to improve safety and efficiency of several human-related activities. Human errors are indeed among the major causes of work-related accidents. Assessing human factors (HFs) could prevent these accidents through specific neurophysiological signals’ evaluation but laboratory sensors require highly-specialized operators and imply a certain grade of invasiveness which could negatively interfere with the worker’s activity. On the contrary, consumer wearables are characterized by their ease of use and their comfortability, other than being cheaper compared to laboratory technologies. Therefore, wearable sensors could represent an ideal substitute for laboratory technologies for a real-time assessment of human performances in ecological settings. The present study aimed at assessing the reliability and capability of consumer wearable devices (i.e., Empatica E4 and Muse 2) in discriminating specific mental states c...
Assessing the added value of context during stress detection from wearable data
BMC Medical Informatics and Decision Making, 2022
Background: Insomnia, eating disorders, heart problems and even strokes are just some of the illnesses that reveal the negative impact of stress overload on health and well-being. Early detection of stress is therefore of utmost importance. Whereas the gold-standard for detecting stress is by means of questionnaires, more recent work uses wearable sensors to find continuous and qualitative physical markers of stress. As some physiological stress responses, e.g. increased heart rate or sweating and chills, might also occur when doing sports, a more profound approach is needed for stress detection than purely considering physiological data. Methods: In this paper, we analyse the added value of context information during stress detection from wearable data. We do so by comparing the performance of models trained purely on physiological data and models trained on physiological and context data. We consider the user's activity and hours of sleep as context information, where we compare the influence of user-given context versus machine learning derived context. Results: Context-aware models reach higher accuracy and lower standard deviations in comparison to the baseline (physiological) models. We also observe higher accuracy and improved weighted F1 score when incorporating machine learning predicted, instead of user-given, activities as context information. Conclusions: In this paper we show that considering context information when performing stress detection from wearables leads to better performance. We also show that it is possible to move away from human labeling and rely only on the wearables for both physiology and context.
Behavioral and contextual data for stress analysis
2015
The spread of mobile phones in the world, increasing quantity and quality of sensors embedded in modern smartphones open up new opportunities for more individual and less obtrusive stress analysis in real-life situations. One of the aims of our research is to develop a real-life stress recognition method by measuring behavioral data and context, through gathering data from smartphones, and without the use of additional wearable sensors. The parameters collected from smartphones are audio, accelerometer, gyroscope, external lighting, screen light on/off, and self-reports (current stress level assessment). In a binary classification (stress or relax) we achieved over 81% accuracy using activity level information (accelerometer and gyroscope features) and decision tree algorithms. For a 3-class stress classification (low, medium, high) we achieved a 70% accuracy with the application of all features.
Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep
BioNanoScience, 2013
Work should be a source of health, pride, and happiness, in the sense of enhancing motivation and strengthening personal development. Healthy and motivated employees perform better and remain loyal to the company for a longer time. But, when the person constantly experiences high workload over a longer period of time and is not able to recover, then work may lead to prolonged negative effects and might cause serious illnesses like chronic stress disease. In this work, we present a solution for assessing the stress experience of people, using features derived from smartphones and wearable chest belts. In particular, we use information from audio, physical activity, and communication data collected during workday and heart rate variability data collected at night during sleep to build multinomial logistic regression models. We evaluate our system in a real work environment and in daily-routine scenarios of 35 employees over a period of 4 months and apply the leave-one-day-out cross-validation method for each user individually to estimate the prediction accuracy. Using only smartphone features, we get an accuracy of 55 %, and using only heart rate variability features, we get an accuracy of 59 %. The combination of all features leads to a rate of 61 % for a three-stress level (low, moderate, and high perceived stress) classification problem.
Noninvasive stress recognition considering the current activity
Personal and Ubiquitous Computing, 2015
In this study, we aim to determine the stress level in a non-invasive way to the maximum extent possible by analyzing behavioral and contextual data received from the only source being a smartphone containing the data gathered in real-life situations. The information collected includes audio, gyroscope and accelerometer features, light condition, screen mode (on/off), current stress level self-assessment, and the current activity type. Three stress analysis models have been built: two with the consideration of current activities of a participant and one without those. Classification of low-and high-stress conditions, which was executed for a separate model for a certain kind of activity only, enabled us to achieve 3.9 % higher accuracy than that under the conditions when those activities were neglected. Also, the Android application was developed as a means for the current activity-type identification.
A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques
IEEE Access, 2021
Stress is an escalated psycho-physiological state of the human body emerging in response to a challenging event or a demanding condition. Environmental factors that trigger stress are called stressors. In case of prolonged exposure to multiple stressors impacting simultaneously, a person's mental and physical health can be adversely affected which can further lead to chronic health issues. To prevent stress-related issues, it is necessary to detect them in the nascent stages which are possible only by continuous monitoring of stress. Wearable devices promise real-time and continuous data collection, which helps in personal stress monitoring. In this paper, a comprehensive review has been presented, which focuses on stress detection using wearable sensors and applied machine learning techniques. This paper investigates the stress detection approaches adopted in accordance with the sensory devices such as wearable sensors, Electrocardiogram (ECG), Electroencephalography (EEG), and Photoplethysmography (PPG), and also depending on various environments like during driving, studying, and working. The stressors, techniques, results, advantages, limitations, and issues for each study are highlighted and expected to provide a path for future research studies. Also, a multimodal stress detection system using a wearable sensor-based deep learning technique has been proposed at the end.
Discriminating Stress From Cognitive Load Using a Wearable EDA Device
IEEE Transactions on Information Technology in Biomedicine, 2000
The inferred cost of work-related stress call for prevention strategies that aim at detecting early warning signs at the workplace. This paper goes one step towards the goal of developing a personal health system for detecting stress. We analyze the discriminative power of electrodermal activity (EDA) in distinguishing stress from cognitive load in an office environment. A collective of 33 subjects underwent a laboratory intervention that included mild cognitive load and two stress factors, which are relevant at the workplace: mental stress induced by solving arithmetic problems under time pressure and psychosocial stress induced by social-evaluative threat. During the experiments, a wearable device was used to monitor the EDA as a measure of the individual stress reaction. Analysis of the data showed that the distributions of the EDA peak height and the instantaneous peak rate carry information about the stress level of a person. Six classifiers were investigated regarding their ability to discriminate cognitive load from stress. A maximum accuracy of 82.8% was achieved for discriminating stress from cognitive load. This would allow keeping track of stressful phases during a working day by using a wearable EDA device.