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.

The inferred cost of work-related stress call for early prevention strategies. In this, we see a new opportunity for affective and pervasive computing by detecting early warning signs. This paper goes one step toward this goal. A collective of 33 subjects underwent a laboratory stress intervention, while a set of physiological signals was collected. In this paper, we investigate whether affective information related to stress can be found in the posture channel during office work. Following more recent work in this field, we directly associate features that are derived from the pressure distribution on a chair with affective states. We found that nervous subjects reveal higher variance of movements under stress. Furthermore, we show that a person-independent discrimination of stress from cognitive load is feasible when using pressure data only. A supervised variant of a self-organizing map, which is able to adapt to different patterns of stress responses, reaches an overall accuracy of 73.75% with unknown subjects.

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Previous work on emotion recognition from physiology has rarely addressed the problem of missing data. However, data loss due to artifacts is a frequent phenomenon in practical applications. Discarding the whole data instance if only a part is corrupted results in a substantial loss of data. To address this problem, two methods for handling missing data (imputation and reduced-feature models) in combination with two classifier fusion approaches (majority and confidence voting) are investigated in this work. The five emotions amusement, anger, contentment, neutral and sadness were elicited in 20 subjects by films while six physiological signals (ECG, EMG, EOG, EDA, respiration and finger temperature) were recorded. Results show that classifier fusion significantly increases the recognition accuracy in comparison to single classifiers by up to 16.3%. Regarding the methods for handling missing data, reduced-feature models are competitive or even slightly better than models which employ imputation. This is beneficial for practical applications where computational complexity is critical. Related workModalities which have been used to automatically detect emotions include facial expression [3-5], speech [6-8] and 978-1-4244-4799-2/09/$25.00 c 2009 IEEE

Air travel has become the preferred mode of long-distance transportation for most of the world's travelers. People of every age group and health status are traveling by airplane and thus the airplane has become part of our environment, in which people with health-related limitations need assistive support. Since the main interaction point between a passenger and the airplane is the seat, this work presents a smart airplane seat for measuring health-related signals of a passenger. We describe the design, implementation and testing of a multimodal sensor system integrated into the seat. The presented system is able to measure physiological signals, such as electrocardiogram, electrodermal activity, skin temperature, and respiration. We show how the design of the smart seat system is influenced by the trade-off between comfort and signal quality, i.e. incorporating unobtrusive sensors and dealing with erroneous signals. Artifact detection through sensor fusion is presented and the working principle is shown with a feasibility study, in which normal passenger activities were performed. Based on the presented method, we are able to identify signal regions in which the accuracies for detecting the heart-and respiration-rate are 88 and 82%, respectively, compared to 40 and 76% without any artifact removal.