An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use

Multimodal region-based behavioral modeling for suicide risk screening

IntroductionSuicide is a leading cause of death around the world, interpolating a huge suffering to the families and communities of the individuals. Such pain and suffering are preventable with early screening and monitoring. However, current suicide risk identification relies on self-disclosure and/or the clinician's judgment.Research question/statmentTherefore, we investigate acoustic and nonverbal behavioral markers that are associated with different levels of suicide risks through a multimodal approach for suicide risk detection.Given the differences in the behavioral dynamics between subregions of facial expressions and body gestures in terms of timespans, we propose a novel region-based multimodal fusion.MethodsWe used a newly collected video interview dataset of young Japanese who are at risk of suicide to extract engineered features and deep representations from the speech, regions of the face (i.e., eyes, nose, mouth), regions of the body (i.e., shoulders, arms, legs), as well as the overall combined regions of face and body.ResultsThe results confirmed that behavioral dynamics differs between regions, where some regions benefit from a shorter timespans, while other regions benefit from longer ones. Therefore, a region-based multimodal approach is more informative in terms of behavioral markers and accounts for both subtle and strong behaviors. Our region-based multimodal results outperformed the single modality, reaching a sample-level accuracy of 96% compared with the highest single modality that reached sample-level accuracy of 80%. Interpretation of the behavioral markers, showed the higher the suicide risk levels, the lower the expressivity, movement and energy observed from the subject. Moreover, the high-risk suicide group express more disgust and contact avoidance, while the low-risk suicide group express self-soothing and anxiety behaviors.DiscussionEven though multimodal analysis is a powerful tool to enhance the model performance and its reliability, it is important to ensure through a careful selection that a strong behavioral modality (e.g., body movement) does not dominate another subtle modality (e.g., eye blink). Despite the small sample size, our unique dataset and the current results adds a new cultural dimension to the research on nonverbal markers of suicidal risks. Given a larger dataset, future work on this method can be useful in helping psychiatrists with the assessment of suicide risk and could have several applications to identify those at risk