Title: Bag-of-Words Method Applied to Accelerometer Measurements for the Purpose of Classification and Energy Estimation,
Abstract: Accelerometer measurements are the prime type of sensor information most
think of when seeking to measure physical activity. On the market, there are
many fitness measuring devices which aim to track calories burned and steps
counted through the use of accelerometers. These measurements, though good
enough for the average consumer, are noisy and unreliable in terms of the
precision of measurement needed in a scientific setting. The contribution of
this paper is an innovative and highly accurate regression method which uses an
intermediary two-stage classification step to better direct the regression of
energy expenditure values from accelerometer counts.
We show that through an additional unsupervised layer of intermediate feature
construction, we can leverage latent patterns within accelerometer counts to
provide better grounds for activity classification than expert-constructed
timeseries features. For this, our approach utilizes a mathematical model
originating in natural language processing, the bag-of-words model, that has in
the past years been appearing in diverse disciplines outside of the natural
language processing field such as image processing. Further emphasizing the
natural language connection to stochastics, we use a gaussian mixture model to
learn the dictionary upon which the bag-of-words model is built. Moreover, we
show that with the addition of these features, we're able to improve regression
root mean-squared error of energy expenditure by approximately 1.4 units over
existing state-of-the-art methods.