2,678 research outputs found
Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition
Body-worn sensors in general and accelerometers in particular have been widely used in
order to detect human movements and activities. The execution of each type of movement by each
particular individual generates sequences of time series of sensed data from which specific movement
related patterns can be assessed. Several machine learning algorithms have been used over windowed
segments of sensed data in order to detect such patterns in activity recognition based on intermediate
features (either hand-crafted or automatically learned from data). The underlying assumption is
that the computed features will capture statistical differences that can properly classify different
movements and activities after a training phase based on sensed data. In order to achieve high
accuracy and recall rates (and guarantee the generalization of the system to new users), the training
data have to contain enough information to characterize all possible ways of executing the activity or
movement to be detected. This could imply large amounts of data and a complex and time-consuming
training phase, which has been shown to be even more relevant when automatically learning the
optimal features to be used. In this paper, we present a novel generative model that is able to generate
sequences of time series for characterizing a particular movement based on the time elasticity
properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn
the particular features able to detect human movements. The results of movement detection using a
newly generated database with information on five users performing six different movements are
presented. The generalization of results using an existing database is also presented in the paper.
The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77)
even in the case of using different people executing a different sequence of movements and using
different hardware
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Dragon-kings: mechanisms, statistical methods and empirical evidence
This introductory article presents the special Discussion and Debate volume
"From black swans to dragon-kings, is there life beyond power laws?" published
in Eur. Phys. J. Special Topics in May 2012. We summarize and put in
perspective the contributions into three main themes: (i) mechanisms for
dragon-kings, (ii) detection of dragon-kings and statistical tests and (iii)
empirical evidence in a large variety of natural and social systems. Overall,
we are pleased to witness significant advances both in the introduction and
clarification of underlying mechanisms and in the development of novel
efficient tests that demonstrate clear evidence for the presence of
dragon-kings in many systems. However, this positive view should be balanced by
the fact that this remains a very delicate and difficult field, if only due to
the scarcity of data as well as the extraordinary important implications with
respect to hazard assessment, risk control and predictability.Comment: 20 page
Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures
The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy
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