Using $B$-Spline Model on Depth Camera Data to Predict Physical Activity Energy Expenditure of Different Levels of Human Exercise

Abstract

Energy expenditure (EE) is often used to quantify physical activity. Currently, EE is estimated with data collected by inertial measurement units or depth cameras and verified by oxygen consumption data. Due to the different data collection time spans in this system, raw data were split into minute-by-minute windows, and summary statistics for each window were computed. However, using summary statistics to aggregate data might be influenced by redundant noise or result in the loss of valuable information. This article presents a modeling method using functional analysis to characterize the trajectory of the collected skeletal data, thus enabling the effective use of the complete data. Next, the fitted values of the skeletal data can be aligned to the overall EE data and used to predict the overall EE as well as the task-based EE. The study results revealed for metabolic equivalent of task prediction that the root-mean-square error (RMSE) derived for the proposed method was $< $ 0.5 and that the mean absolute error (MAE) was approximately 0.3. Models for estimating task-based EE, including EE related to standing and walking task, also exhibited low RMSE and MAE values. Accordingly, the proposed modeling approach is superior to summary statistics for estimating EE in depth camera systems.