Bag-of-Words Method Applied to Accelerometer Measurements for the Purpose of Classification and Energy Estimation

TL;DR

This paper introduces a novel bag-of-words approach using a two-stage classification and regression framework to improve energy expenditure estimation from accelerometer data, outperforming existing methods.

Contribution

It presents an innovative application of the bag-of-words model with an unsupervised feature construction layer for activity classification and energy estimation from accelerometer data.

Findings

Improved regression root mean-squared error by approximately 1.4 units.

Leveraged latent patterns in accelerometer counts for better activity classification.

Demonstrated the effectiveness of a natural language processing-inspired model in sensor data analysis.

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.