Computer Science 분야 (13): Physiology; Stresses; Galvanic skin

The Empatica E3 is a wearable wireless multisensor device for real-time computerized biofeedback and data acquisition. The E3 has four embedded sensors: photoplethysmograph (PPG), electrodermal activity (EDA), 3-axis accelerometer, and temperature. It is small, light and comfortable and it is suitable for almost all real-life applications. The E3 operates both in streaming mode for real-time data processing using a Bluetooth low energy interface and in recording mode using its internal flash memory. With E3, it is possible to conduct research outside of the lab by acquiring continuous data for ambulatory situations in a comfortable and non-distracting way.

Stress is a major problem of our society, as it is the cause of many health problems and huge economic losses in companies. Continuous high mental workloads and non-stop technological development, which leads to constant change and need for adaptation, makes the problem increasingly serious for office workers. To prevent stress from becoming chronic and provoking irreversible damages, it is necessary to detect it in its early stages. Unfortunately, an automatic, continuous and unobtrusive early stress detection method does not exist yet. The multimodal nature of stress and the research conducted in this area suggest that the developed method will depend on several modalities. Thus, this work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behavioural modalities, along with contextual measurements, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.

Recent advances in mobile health have produced several new models for inferring stress from wearable sensors. But, the lack of a gold standard is a major hurdle in making clinical use of continuous stress measurements derived from wearable sensors. In this paper, we present a stress model (called cStress) that has been carefully developed with attention to every step of computational modeling including data collection, screening, cleaning, filtering, feature computation, normalization, and model training. More importantly, cStress was trained using data collected from a rigorous lab study with 21 participants and validated on two independently collected data sets --- in a lab study on 26 participants and in a week-long field study with 20 participants. In testing, the model obtains a recall of 89% and a false positive rate of 5% on lab data. On field data, the model is able to predict each instantaneous self-report with an accuracy of 72%.

Recognizing when computer users are stressed can help reduce their frustration and prevent a large variety of negative health conditions associated with chronic stress. However, measuring stress non-invasively and continuously at work remains an open challenge. This work explores the possibility of using a pressure-sensitive keyboard and a capacitive mouse to discriminate between stressful and relaxed conditions in a laboratory study. During a 30 minute session, 24 participants performed several computerized tasks consisting of expressive writing, text transcription, and mouse clicking. During the stressful conditions, the large majority of the participants showed significantly increased typing pressure (>79% of the participants) and more contact with the surface of the mouse (75% of the participants). We discuss the potential implications of this work and provide recommendations for future work.

The Spanish Ministry of Defense, through its Future Combatant program, has sought to develop technology aids with the aim of extending combatants’ operational capabilities. Within this framework the ATREC project funded by the “Coincidente” program aims at analyzing diverse biometrics to assess by real time monitoring the stress levels of combatants. This project combines multidisciplinary disciplines and fields, including wearable instrumentation, textile technology, signal processing, pattern recognition and psychological analysis of the obtained information. In this work the ATREC project is described, including the different execution phases, the wearable biomedical measurement systems, the experimental setup, the biomedical signal analysis and speech processing performed. The preliminary results obtained from the data analysis collected during the first phase of the project are presented, indicating the good classification performance exhibited when using features obtained from electrocardiographic recordings and electrical bioimpedance measurements from the thorax. These results suggest that cardiac and respiration activity offer better biomarkers for assessment of stress than speech, galvanic skin response or skin temperature when recorded with wearable biomedical measurement systems.