Vathsangam Harshvardhan, Emken B, Schroeder E, Spruijt-Metz Donna, Sukhatme Gaurav S
Dept. of Computer Science, Univ. of Southern California, Los Angeles, CA 90089, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:6497-501. doi: 10.1109/IEMBS.2010.5627365.
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.
步行是身体活跃人群中最常见的活动。利用佩戴在身体上的惯性传感器进行标准的身体活动特征描述时，采用的是加速度计生成的计数。这存在两个问题——不精确性（由于使用专利计数）和不完整性（由于对运动的描述不完整）。我们通过直接从一个由三轴加速度计和三轴陀螺仪组成的髋部佩戴惯性传感器预测稳态跑步机步行过程中的能量消耗，来解决这两个问题。我们使用贝叶斯线性回归，基于对流数据联合概率的建模来预测能量消耗。与当前实践中常见的仅使用两个线性加速度的流数据相比，使用来自六轴传感器的数据时预测效果显著更好。我们还展示了如何从原始流加速度数据（直至线性变换）中再现市售加速度计的计数，且具有高度相关性（X轴加速度流为0.9787±0.0089，Y轴加速度流为0.9141±0.0460）。本文强调了概率技术与三轴加速度和旋转速率联合建模相结合在改善稳态跑步机步行能量消耗预测方面的作用。
