| Simultaneous Activity Recognition and Gait Segmentation Using Graphical Models Objective health data about subjects outside of the
laboratory is important in order
to analyse symptoms that cannot be reproduced in the
laboratory. A simple daily life
example would be how stride length changes with tiredness
or stress. In order to
investigate this we must be able to accurately segment a
stride from daily living data
in order to have an accurate measure of duration and
distance. State-of-the-art
methods use separate segmentation and classification
approaches. This is inaccurate
for segmentation of an isolated activity, especially one
that is not repeated. This
could be solved using a model that is based on the
sequence of phases within
activities. Such a model is a graphical model. Currently
we are working with
Conditional Random Fields and Hierarchical Hidden Markov
Models on daily living
data. The applications will include sports as well as
daily living activities. | Project manager:
Prof. Dr. Björn Eskofier
Project participants:
Christine Martindale, M. Sc.
Keywords:
Inertial sensors; graphical models; activity recognition; segmentation
Start: 1.2.2015
Sponsored by:
Bosch Sensortec
Contact:
Martindale, Christine
Phone +49 9131 85 20159, Fax +49 9131 85 27270, E-Mail: christine.f.martindale@fau.de
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