Unobtrusive Monitoring of Knowledge Workers for Stress Self-regulation (original) (raw)
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Employees often report the experience of stress at work. In the SWELL project we investigate how new context aware pervasive systems can support knowledge workers to diminish stress. The focus of this paper is on developing automatic classifiers to infer working conditions and stress related mental states from a multimodal set of sensor data (computer logging, facial expressions, posture and physiology). We address two methodological and applied machine learning challenges: 1) Detecting work stress using several (physically) unobtrusive sensors, and 2) Taking into account individual differences. A comparison of several classification approaches showed that, for our SWELL-KW dataset, neutral and stressful working conditions can be distinguished with 90% accuracy by means of SVM. Posture yields most valuable information, followed by facial expressions. Furthermore, we found that the subjective variable 'mental effort' can be better predicted from sensor data than e.g. 'perceived stress'. A comparison of several regression approaches showed that mental effort can be predicted best by a decision tree (correlation of 0.82). Facial expressions yield most valuable information, followed by posture. We find that especially for estimating mental states it makes sense to address individual differences. When we train models on particular subgroups of similar users, (in almost all cases) a specialized model performs equally well or better than a generic model.
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Automatic detection of work-related stress has attracted an increasing amount of attention from researchers from various disciplines and industries. An experiment is discussed in this paper that was designed to evaluate the efficacy of multimodal sensor measures that have often been used but not yet been systematically tested and compared with each other in previous work, such as pressure distribution sensor, physiological sensors, and an eye tracker. We used the Stroop test and information pick up task as the stressors. In the subject independent case in particular, signals from the combined (chair and floor) pressure distribution sensors, which we consider the most feasible sensors in the office environment, resulted in higher recognition accuracy rates than the physiological or eye tracker signals for the two stressors.
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The problem of job stress is generally recognized as one of the major factors leading to a spectrum of health problems. People with certain professions, like intensive care special-ists or call-center operators, and people in certain phases of their lives, like working parents with young children, are at increased risk of getting overstressed. For instance, one third of the intensive care specialists in the Netherlands are re-ported to have (had) a burn-out. Stress management should start far before the stress starts causing illnesses. The cur-rent state of sensor technology allows to develop systems measuring physical symptoms reflecting the stress level. We propose to use data mining and predictive modeling for gain-ing insight in the stress effects of the events at work and for enabling better stress management by providing timely and personalized coaching. In this paper we present a gen-eral framework allowing to achieve this goal and discuss the lessons learnt from the conducte...
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Imagine a typical working day of a knowledge worker, ie someone who is predominantly concerned with interpreting and generating information. Bob gets into the office at 9, starts up his computer, takes a look at his mails and calendar and plans what things he has to do this day. Then he starts working on one of the important tasks that have to be completed this week. When an e-mail comes in, he quickly reads it. As it is not relevant to him, he continues his task.
Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices
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This research aims to identify a feasible model to predict a learner's stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner's emotions. The few signals produced by mouse and keyboard could enable such solutionto measure real world individual's affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification.
The SWELL Knowledge Work Dataset for Stress and User Modeling Research
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This paper describes the new multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed typical knowledge work (writing reports, making presentations, reading e-mail, searching for information). We manipulated their working conditions with the stressors: email interruptions and time pressure. A varied set of data was recorded: computer logging, facial expression from camera recordings, body postures from a Kinect 3D sensor and heart rate (variability) and skin conductance from body sensors. The dataset made available not only contains raw data, but also preprocessed data and extracted features. The participants' subjective experience on task load, mental effort, emotion and perceived stress was assessed with validated questionnaires as a ground truth. The resulting dataset on working behavior and affect is a valuable contribution to several research fields, such as work psychology, user modeling and context aware systems.
The problem of job stress is generally recognized as one of the major factors leading to a spectrum of health problems. People with certain professions, like intensive care specialists or call-center operators, and people in certain phases of their lives, like working parents with young children, are at increased risk of getting overstressed. For instance, one third of the intensive care specialists in the Netherlands are reported to have (had) a burn-out. Stress management should start far before the stress starts causing illnesses. The current state of sensor technology allows to develop systems measuring physical symptoms reflecting the stress level. We propose to use data mining and predictive modeling for gaining insight in the stress effects of the events at work and for enabling better stress management by providing timely and personalized coaching. In this paper we present a general framework allowing to achieve this goal and discuss the lessons learnt from the conducted case study.
International Journal of Emerging Trends in Engineering Research, 2022
This paper presents the use of a wearable device as a low-cost technological tool for estimating the stress level of a university professor. The wearable device has infrared sensors that, when in contact with the skin, allow it to know the variations in the user's heart rate and indirectly estimate stress levels through an endorsed model. An experimental methodology with a non-probabilistic convenience sample was proposed. The period of experimental evaluation was one year, 24 hours a day. A classification of the levels of stress that the university professor manages was made: relaxed, normal, medium and high. This made it possible to show the percentage of time that he held in each of these categories. Within the quantitative analysis, a differentiation was made between working days, weekends and holidays. As significant results, it was found that the working day with the highest stress values was Wednesday with a value of %59.78 and, in a non-presumptive way, Saturday was detected, despite not being a working day, the university professor reached significant levels of stress. It was possible to show that the use of low-cost wearable devices allows an estimation of the repercussions at the stress level of workloads, for which it can serve as a tool for planning and scheduling tasks.
TECHNICAL CONTRIBUTION Personalized Stress Management: Enabling Stress Monitoring
2016
Stress is one of the major triggers for many diseases. Improving stress balance is therefore an important prevention step. With advances in wearable sensors, it becomes possible to continuously monitor and analyse user’s behavior and arousal in an unobtrusive way. In this paper, we report on a case study in which users (21 teachers of a vocational school) were provided with wearable sen-sors and could view their arousal information put in the context of their life events during the period of four weeks using our software tool in an unsupervised setting. The goal was to evaluate user engagement and enabling of self-coaching abilities. Our results show that users actively explored their arousal data during the study. Further qualitative evaluation conducted with 15 of 21 users indi-cated that 12 of 15 users were able to learn about their stress patterns based on the information they obtained, but only 5 of them were able to come up with practical inter-ventions for improving their str...