87 research outputs found
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
Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step
Increase in workload across many organisations and consequent increase in
occupational stress is negatively affecting the health of the workforce.
Measuring stress and other human psychological dynamics is difficult due to
subjective nature of self- reporting and variability between and within
individuals. With the advent of smartphones it is now possible to monitor
diverse aspects of human behaviour, including objectively measured behaviour
related to psychological state and consequently stress. We have used data from
the smartphone's built-in accelerometer to detect behaviour that correlates
with subjects stress levels. Accelerometer sensor was chosen because it raises
fewer privacy concerns (in comparison to location, video or audio recording,
for example) and because its low power consumption makes it suitable to be
embedded in smaller wearable devices, such as fitness trackers. 30 subjects
from two different organizations were provided with smartphones. The study
lasted for 8 weeks and was conducted in real working environments, with no
constraints whatsoever placed upon smartphone usage. The subjects reported
their perceived stress levels three times during their working hours. Using
combination of statistical models to classify self reported stress levels, we
achieved a maximum overall accuracy of 71% for user-specific models and an
accuracy of 60% for the use of similar-users models, relying solely on data
from a single accelerometer.Comment: in IEEE Journal of Biomedical and Health Informatics, 201
Investigation of indoor localization with ambient FM radio stations
Localization plays an essential role in many ubiquitous computing
applications. While the outdoor location-aware services based on GPS are
becoming increasingly popular, their proliferation to indoor environments is
limited due to the lack of widely available indoor localization systems. The
de-facto standard for indoor positioning is based on Wi-Fi and while other
localization alternatives exist, they either require expensive hardware or
provide a low accuracy. This paper presents an investigation into localization
system that leverages signals of broadcasting FM radio stations. The FM
stations provide a worldwide coverage, while FM tuners are readily available in
many mobile devices. The experimental results show that FM radio can be used
for indoor localization, while providing longer battery life than Wi-Fi, making
FM an alternative to consider for positioning.Comment: 10th IEEE Pervasive Computing and Communication conference, PerCom
2012, pp. 171 - 17
Automatic Sensing of Speech Activity and Correlation with Mood Changes
he association between social relationships and psychological health has been established fairly recently, in the last 30-40 years, relying on survey-based methods to record past activities and the psychological responses in individuals. However, using the self-reporting methods for capturing social behavior exhibits a number of shortcomings including recall bias, memory dependence, and a high end user effort for a continuous long-term monitoring. In contrast, automated sensing techniques for monitoring social activity, and in general, human behavior, has a potential to provide more objective measurements thus to overcome the shortcomings of self-reporting methods. In this paper, we present a privacy preserving approach to detect one component of social interactions - the speech activity, through the use of off-the-shelf accelerometers. Furthermore, we used the accelerometer based speech detection method to investigate the correlation between the amount of speech (which is an aspect that reflects the participation in verbal social interactions) and mood changes. Our pilot study suggested that verbal interactions are an important factor that has an impact on individuals’ mood, while the study also demonstrated the potential of automated capturing social activity comparable to the use of gold standard surveys
Processing of Electronic Health Records using Deep Learning: A review
Availability of large amount of clinical data is opening up new research
avenues in a number of fields. An exciting field in this respect is healthcare,
where secondary use of healthcare data is beginning to revolutionize
healthcare. Except for availability of Big Data, both medical data from
healthcare institutions (such as EMR data) and data generated from health and
wellbeing devices (such as personal trackers), a significant contribution to
this trend is also being made by recent advances on machine learning,
specifically deep learning algorithms
Enabling Prescription-based Health Apps
We describe an innovative framework for prescription of personalised health
apps by integrating Personal Health Records (PHR) with disease-specific mobile
applications for managing medical conditions and the communication with
clinical professionals. The prescribed apps record multiple variables including
medical history enriched with innovative features such as integration with
medical monitoring devices and wellbeing trackers to provide patients and
clinicians with a personalised support on disease management. Our framework is
based on an existing PHR ecosystem called TreC, uniquely positioned between
healthcare provider and the patients, which is being used by over 70.000
patients in Trentino region in Northern Italy. We also describe three important
aspects of health app prescription and how medical information is automatically
encoded through the TreC framework and is prescribed as a personalised app,
ready to be installed in the patients' smartphone
Virtual uniforms: using sound frequencies for grouping individuals
In this paper, we present the concept of grouping individuals and detecting their proximity by emitting/receiving inaudible tones using their mobile phones. The inspiration stems from uniforms metaphor (of different colors) that groups subjects based on the roles, occupations or teams. The goal is to get an insight into the social context and social interaction patterns
Detecting dressing failures using temporal–relational visual grammars
Evaluation of dressing activities is essential in the assessment of the performance of patients with psycho-motor impairments. However, the current practice of monitoring dressing activity (performed by the patients in front of the therapist) has a number of disadvantages when considering the personal nature of dressing activity as well as inconsistencies between the recorded performance of the activity and performance of the same activity carried out in the patients’ natural environment, such as their home. As such, a system that can evaluate dressing activities automatically and objectively would alleviate some of these issues. However, a number of challenges arise, including difficulties in correctly identifying garments, their position in the body (partially of fully worn) and their position in relation to other garments. To address these challenges, we have developed a novel method based on visual grammars to automatically detect dressing failures and explain the type of failure. Our method is based on the analysis of image sequences of dressing activities and only requires availability of a video recording device. The analysis relies on a novel technique which we call temporal–relational visual grammar; it can reliably recognize temporal dressing failures, while also detecting spatial and relational failures. Our method achieves 91% precision in detecting dressing failures performed by 11 subjects. We explain these results and discuss the challenges encountered during this work
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