Database Reference
In-Depth Information
number of times a pedal revolution is performed per minute) and heart rate,
among other factors. By measuring these in a laboratory based environment, it
is possible to generate a dataset that can be specific to the question being asked
by researchers and free of external artefacts. Many different systems exist to test
cyclists under laboratory conditions while attempting to recreate the specific de-
mands of cycling, with each
cycling ergometer
(a machine designed to replicate
cycling in a measurable and repeatable manner) generating and measuring its
resistive force in a different manner. However, this can lead to significant differ-
ences between ergometers. For the purpose of reliability in testing, an athlete
must repeat tests on the same ergometer, under the same environmental con-
ditions, and in the same training state. This will not eliminate all the changes
from test to test, but will reduce the error from testing on dissimilar systems.
Scientists tend to prefer field-testing rather than laboratory testing, as it pro-
vides additional environmental factors which can effect performance. However,
field tests for absolute physiological values tend to be less exact than labora-
tory based tests and are logistically more dicult to perform. Depending on
the activity, there are many different factors that can predict eventual perfor-
mance during the event. These predictors can be physiological, environmental,
or equipment specific. Measurement of physiological factors is generally done
via heart rate monitoring, power output measurement, respiration, and psycho-
logical scales. The information gained on physiological performance factors can
give insight into how an athlete is performing during the training session, race,
or event in which they are partaking. Over repeat measures it can be possible
to track changes in performance and fitness of the athlete. By sensing physio-
logical, environmental, and equipment changes and how they affect each other
we are able to get a greater understanding of the changes that are occurring in
both racing and training. This can potentially allow the development of targeted
training sessions to investigate aspects of race performance.
1.1 Motivation
Over the past decade cycling has undergone a surge in technology aimed at
the measurement and analysis of training and racing. Due to its repetitive and
prolonged nature, it is possible to measure many factors during cycling once a
sensor is available to monitor the variable required. Technological advances have
allowed sensors and computers to reduce in size and weight dramatically bringing
previously laboratory based tools to the general market. Technologies such as
power measuring
and GPS systems are now light enough for competitive cyclists
to apply them on their bicycles. Although some of these systems integrate several
sensors with one unit, many do not. This generates a problem when several
different sensor sets are needed to determine the information needs described
above. As cyclists are ever concerned with gaining a competitive edge, a system
that will allow them to combine and investigate the data gathered from several
sources is crucial to cyclists, their coach, and the scientists who can interpret
the data. Thus, the goal is to provide a means of facilitating high level queries
across all of these low level devices.
