A Distributed Computing Framework for Real-Time Detection of Stress and of Its Propagation in a Team

Abstract

Stress is one of the key factor that impacts the quality of our daily life: From the productivity and efficiency in the production processes to the ability of (civilian and military) individuals in making rational decisions. Also, stress can propagate from one individual to other working in a close proximity or toward a common goal, e.g., in a military operation or workforce. Real-time assessment of the stress of individuals alone is, however, not sufficient, as understanding its source and direction in which it propagates in a group of people is equally-if not more-important. A continuous near real-time in situ personal stress monitoring system to quantify level of stress of individuals and its direction of propagation in a team is envisioned. However, stress monitoring of an individual via his/her mobile device may not always be possible for extended periods of time due to limited battery capacity of these devices. To overcome this challenge a novel distributed mobile computing framework is proposed to organize the resources in the vicinity and form a mobile device cloud that enables offloading of computation tasks in stress detection algorithm from resource constrained devices (low residual battery, limited CPU cycles) to resource rich devices. Our framework also supports computing parallelization and workflows, defining how the data and tasks divided/assigned among the entities of the framework are designed. The direction of propagation and magnitude of influence of stress in a group of individuals are studied by applying real-time, in situ analysis of Granger Causality. Tangible benefits (in terms of energy expenditure and execution time) of the proposed framework in comparison to a centralized framework are presented via thorough simulations and real experiments.

Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life

“…Although our study employed only 42 healthy subjects to develop a model to automatically detect stress, it is able to detect stress with higher accuracy than the models presented in the existing literature [7, 15, 20, 23, 24, 27–29, 32, 72].…”

“…In fact, a recent literature review highlights the lack of rigorous methods utilized to explore the extent of which ultra-short HRV features can be used to estimate short term ones [22]. In medicine, particularly in clinical trial designs, in order to cope with this kind of problems, the concept of surrogate endpoint (or marker) was introduced [23, 24]. However, proving whether a marker is a valid surrogate of a real clinical outcome can be quite difficult, and combination of appropriate statistical and correlation tests is required, as detailed elsewhere [22].…”

“…To the best of the authors’ knowledge, none of the studies investigating ultra-short HRV features has proposed a robust methodology to assess if ultra-short HRV features are valid surrogates of short ones to detect stress [22]. There have been some attempts to investigate the reliability and accuracy of ultra-short term HRV analysis [15, 23, 24, 26–42], but only one study investigated the validity of ultra-short HRV features in a more rigorous way [39]. However, the authors in [39] only considered 2 time domain HRV features under one standard condition (i.e., rest phase).…”

An intelligent AAA++ approach to predict high blood pressure using PARP classifier

“…11 As a response nerves send more blood to muscles and, brain, which elevates the blood pressure. 13 In this paper we developed a model to predict the HBP based on biological and psychological factors of human body. Blood pressure is affected by many factors, such as excess sodium intake, lack of physical exercise, age, obesity, smoking, alcohol consumption, gender and etc.…”

“…The anxiety level of a person is also measured through personal interview; every person is posed to a set of 20 predefined questions 6,11,13 such as do you have breathing difficulty often, do you compare always with others, do you feel that you are not doing worth noted, do you feel sense of dryness in the mouth, do you scare often without clear reason, the answer obtained for each question is marked on the scale of 0-3, for example if the question is, do you have constant fear about anything in life, the answer could be 0 (no never), or it could be 1 (yes, rarely), or it could be 2 (yes, often) or it could be 3 (yes, most of the time), and the answer is marked as 0 or 1 or 2 or 3. We considered the average value of all answers for experimental analysis.…”