20,156 research outputs found
Protocol of the STRess at Work (STRAW) project : how to disentangle day-to-day occupational stress among academics based on EMA, physiological data, and smartphone sensor and usage data
Several studies have reported on increasing psychosocial stress in academia due to work environment risk factors like job insecurity, work-family conflict, research grant applications, and high workload. The STRAW project adds novel aspects to occupational stress research among academic staff by measuring day-to-day stress in their real-world work environments over 15 working days. Work environment risk factors, stress outcomes, health-related behaviors, and work activities were measured repeatedly via an ecological momentary assessment (EMA), specially developed for this project. These results were combined with continuously tracked physiological stress responses using wearable devices and smartphone sensor and usage data. These data provide information on workplace context using our self-developed Android smartphone app. The data were analyzed using two approaches: 1) multilevel statistical modelling for repeated data to analyze relations between work environment risk factors and stress outcomes on a within- and between-person level, based on EMA results and a baseline screening, and 2) machine-learning focusing on building prediction models to develop and evaluate acute stress detection models, based on physiological data and smartphone sensor and usage data. Linking these data collection and analysis approaches enabled us to disentangle and model sources, outcomes, and contexts of occupational stress in academia
Predict Daily Life Stress based on Heart Rate Variability
Department of Human Factors EngineeringThe purpose of this study is to investigate the feasibility of predicting a daily mental stress level from analyzing Heart Rate Variability (HRV) by using a Photoplethysmography (PPG) sensor which is integrated in the wristband-type wearable device. In this experiment, each participant was asked to measure their own PPG signals for 30 seconds, three times a day (at noon, 6 P.M, and 10 minutes before going to sleep) for a week.
And 10 minutes before going to sleep, all participants were asked to self-evaluate their own daily mental stress level using Perceived Stress Scale (PSS). The recorded signals were transmitted and stored at each participant???s smartphone via Bluetooth Low Energy (BLE) communication by own-made mobile application.
The preprocessing procedure was used to remove PPG signal artifacts in order to make better performance for detecting each pulse peak point at PPG signal. In this preprocessing, three- level-bandpass filtering which consisted three different pass band range bandpass filters was used.
In this study, frequency domain HRV analysis feature that the ratio of low-frequency (0.04Hz ~ 0.15Hz) to high-frequency (0.15Hz ~ 0.4Hz) power value was used. In frequency domain analysis, autoregressive (AR) model was used, because this model has higher resolution than that of Fast Fourier Transform (FFT). The accuracy of this prediction was 86.35% on average of all participants. Prediction result was calculated from the leave-one-out validation. The IoT home appliances are arranged according to the result of this prediction algorithm. This arrangement is offering optimized user???s relaxation. Also, this algorithm can help acute stress disorder patients to concentrate on getting treatment.clos
Smartphone apps usage patterns as a predictor of perceived stress levels at workplace
Explosion of number of smartphone apps and their diversity has created a
fertile ground to study behaviour of smartphone users. Patterns of app usage,
specifically types of apps and their duration are influenced by the state of
the user and this information can be correlated with the self-reported state of
the users. The work in this paper is along the line of understanding patterns
of app usage and investigating relationship of these patterns with the
perceived stress level within the workplace context. Our results show that
using a subject-centric behaviour model we can predict stress levels based on
smartphone app usage. The results we have achieved, of average accuracy of 75%
and precision of 85.7%, can be used as an indicator of overall stress levels in
work environments and in turn inform stress reduction organisational policies,
especially when considering interrelation between stress and productivity of
workers
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
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.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US
Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities
There is an increasing interest in exploiting mobile sensing technologies and
machine learning techniques for mental health monitoring and intervention.
Researchers have effectively used contextual information, such as mobility,
communication and mobile phone usage patterns for quantifying individuals' mood
and wellbeing. In this paper, we investigate the effectiveness of neural
network models for predicting users' level of stress by using the location
information collected by smartphones. We characterize the mobility patterns of
individuals using the GPS metrics presented in the literature and employ these
metrics as input to the network. We evaluate our approach on the open-source
StudentLife dataset. Moreover, we discuss the challenges and trade-offs
involved in building machine learning models for digital mental health and
highlight potential future work in this direction.Comment: 6 pages, 2 figures, In Proceedings of the NIPS Workshop on Machine
Learning for Healthcare 2017 (ML4H 2017). Colocated with NIPS 201
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing
System
- …