You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28 for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.

Cited Abstract(s):
[1]: This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from 5-min intervals of data during the rest, highway, and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97 across multiple drivers and driving days. Analysis II compared continuous features, calculated at 1-s intervals throughout the entire drive, with a metric of observable stressors created by independent coders from videotapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage noncritical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers.

[2]: The stress and poor sleep quality of a person may be used as two of several components for predicting the onset of mental health problems, in particular depression. Ergonomie smart sensors that can determine the heart rate variations related to stress and the variability of sleep may provide unique insights to the coping behavior of stressed people. Rather than relying on wearable computers, a single smart miniature sensor that is worn 24 7 should perform the complex embedded recognition tasks while meeting difficult battery life, wireless communications and ergonomie constraints. The development and testing of such a smart sensor is described focusing on implementation within a distributed intelligence based architecture. The manner in which the user's heart rate and the user's physical motion is used to measure stress and sleep quality is explained.

[3]: Repeated exposures to psychological stress can lead to or worsen diseases of slow accumulation such as heart diseases and cancer. The main challenge in addressing the growing epidemic of stress is a lack of robust methods to measure a person's exposure to stress in the natural environment. Periodic self-reports collect only subjective aspects, often miss stress episodes, and impose significant burden on subjects. Physiological sensors provide objective and continuous measures of stress response, but exhibit wide between-person differences and are easily confounded by daily activities (e.g., speaking, physical movements, coffee intake, etc.). In this paper, we propose, train, and test two models for continuous prediction of stress from physiological measurements captured by unobtrusive, wearable sensors. The first model is a physiological classifier that predicts whether changes in physiology represent stress. Since the effect of stress may persist in the mind longer than its acute effect on physiology, we propose a perceived stress model to predict perception of stress. It uses the output of the physiological classifier to model the accumulation and gradual decay of stress in the mind. To account for wide between-person differences, both models self-calibrate to each subject. Both models were trained using data collected from 21 subjects in a lab study, where they were exposed to cognitive, physical, and social stressors representative of that experienced in the natural environment. Our physiological classifier achieves 90 accuracy and our perceived stress model achieves a median correlation of 0.72 with self-reported rating. We also evaluate the perceived stress model on data collected from 17 participants in a two-day field study, and find that the average rating of stress obtained from our model has a correlation of 0.71 with that obtained from periodic self-reports.
A large body of research on stress detection focused on physiological measurements to infer stress levels (see [1] , [2] , [3] ). Heart-rate variability, galvanic skin response, respiration, muscle activity and temperature are among the most relevant features. However, despite providing reliable insights on stress levels, this approach has major limitations because it comprises wearable sensors that need to be carried at all times to allow for continuous monitoring.