10,619 research outputs found
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
Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour
Health and fitness wearable technology has recently advanced, making it
easier for an individual to monitor their behaviours. Previously self generated
data interacts with the user to motivate positive behaviour change, but issues
arise when relating this to long term mention of wearable devices. Previous
studies within this area are discussed. We also consider a new approach where
data is used to support instead of motivate, through monitoring and logging to
encourage reflection. Based on issues highlighted, we then make recommendations
on the direction in which future work could be most beneficial
Detection of hydraulic phenomena in francis turbines with different sensors
Nowadays, hydropower is demanded to provide flexibility and fast response into the electrical grid in order to compensate the non-constant electricity generation of other renewable sources. Hydraulic turbines are therefore demanded to work under o -design conditions more frequently, where di erent complex hydraulic phenomena appear, a ecting the machine stability as well as reducing the useful life of its components. Hence, it is desirable to detect in real-time these hydraulic phenomena to assess the operation of the machine. In this paper, a large medium-head Francis turbine was selected for this purpose. This prototype is instrumented with several sensors such as accelerometers, proximity probes, strain gauges, pressure sensors and a microphone. Results presented in this paper permit knowing which hydraulic phenomenon is detected with every sensor and which signal analysis technique is necessary to use. With this information, monitoring systems can be optimized with the most convenient sensors, locations and signal analysis techniquesPostprint (published version
Automatic Change-Point Detection in Time Series via Deep Learning
Detecting change-points in data is challenging because of the range of
possible types of change and types of behaviour of data when there is no
change. Statistically efficient methods for detecting a change will depend on
both of these features, and it can be difficult for a practitioner to develop
an appropriate detection method for their application of interest. We show how
to automatically generate new detection methods based on training a neural
network. Our approach is motivated by many existing tests for the presence of a
change-point being able to be represented by a simple neural network, and thus
a neural network trained with sufficient data should have performance at least
as good as these methods. We present theory that quantifies the error rate for
such an approach, and how it depends on the amount of training data. Empirical
results show that, even with limited training data, its performance is
competitive with the standard CUSUM test for detecting a change in mean when
the noise is independent and Gaussian, and can substantially outperform it in
the presence of auto-correlated or heavy-tailed noise. Our method also shows
strong results in detecting and localising changes in activity based on
accelerometer data.Comment: 16 pages, 5 figures and 1 tabl
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