Energy estimation of treadmill walking using on-body accelerometers and gyroscopes.

Walking is the most common activity among people who are physically active. Standard practice physical activity characterization from body-mounted inertial sensors uses accelerometer-generated counts. There are two problems with this - imprecison (due to usage of proprietary counts) and incompleteness (due to incomplete description of motion). We address both these problems by directly predicting energy expenditure during steady-state treadmill walking from a hip-mounted inertial sensor comprised of a tri-axial accelerometer and a tri-axial gyroscope. We use Bayesian Linear Regression to predict energy expenditure based on modelling joint probabilities of streaming data. The prediction is significantly better with data from a 6 axis sensor as compared with streaming data from only 2 linear accelerations as is common in current practice. We also show how counts from a commercially available accelerometer can be reproduced from raw streaming acceleration data (up to a linear transformation) with high correlation (.9787 ± .0089 for the X-axis and .9141 ± .0460 for the Y-axis acceleration streams). The paper emphasizes the role of probabilistic techniques in conjunction with joint modeling of tri-axial accelerations and rotational rates to improve energy expenditure prediction for steady-state treadmill walking.