Stress detection using wearable physiological sensors

As the population increases in the world, the ratio of health carers is rapidly decreasing. Therefore, there is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies, usually due to stressful situations during everyday activities including work. This paper presents a machine learning approach for stress detection on people using wearable physiological sensors with the �final aim of improving their quality of life. The presented technique can monitor the state of the subject continuously and classify it into "stressful" or "non-stressful" situations. Our classification results show that this method is a good starting point towards real-time stress detection

The relation between treasury yields and corporate bond yield spreads

Because the option to call a corporate bond should rise in value when bond yields fall, the relation between noncallable Treasury yields and spreads of corporate bond yields over Treasury yields should depend on the callability of the corporate bond. I confirm this hypothesis for investment-grade corporate bonds. Although yield spreads on both callable and noncallable corporate bonds fall when Treasury yields rise, this relation is much stronger for callable bonds. This result has important implications for interpreting the behavior of yields on commonly used corporate bond indexes, which are composed primarily of callable bonds. COMMONLY USED INDEXES OF CORPORATE bond yields, such as those produced by Moody’s or Lehman Brothers, are constructed using both callable and noncallable bonds. Because the objective of those producing the indexes is to track the universe of corporate bonds, this methodology is sensible. Until the mid-1980s, few corporations issued noncallable bonds, hence an index designed to measure the yield on a typical corporate bond would have to b

The Effect of Stress on Cognitive Load Measurement

Abstract. Human physiological signals have been widely used to non‐invasively measure cognitive load (CL) during task execution. A major challenge for CL detection is the presence of stress, which may affect physiological measurements in ways that confound reliable detection of CL. In this experiment we investigated the effect of stress on cognitive load measurement using galvanic skin response (GSR) as a physiological index of CL. The experiment utilized feelings of lack of control, task failure and social‐evaluation to induce stress. Mean GSR values were shown to be significantly different between CL levels in the „no‐stress ‟ condition, but not when including the „stress‟ condition. On the other hand, features extracted from GSR signals based on peak detection exhibited consistent behaviour under both conditions, demonstrating the usefulness of the features as cognitive load index even when a person‟s stress level is fluctuating