학술논문
Floating Epoch Length Improves the Accuracy of Accelerometry-Based Estimation of Coincident Oxygen Consumption
Document Type
Academic Journal
Source
Sensors. December 2023, Vol. 24 Issue 1
Subject
Language
English
ISSN
1424-8220
Abstract
Author(s): Henri Vähä-Ypyä [1]; Pauliina Husu [1]; Tommi Vasankari [1,2]; Harri Sievänen (corresponding author) [1,*] 1. Introduction Physical activity (PA) offers significant health benefits and mitigates health risks [1]. It [...]
Estimation of oxygen consumption (VO[sub.2]) from accelerometer data is typically based on prediction equations developed in laboratory settings using steadily paced and controlled test activities. These equations may not capture the temporary changes in VO[sub.2] occurring in sporadic real-life physical activity. In this study, we introduced a novel floating epoch for accelerometer data analysis and hypothesized that an adaptive epoch length provides a more consistent estimation of VO[sub.2] in irregular activity conditions than a 6 s constant epoch. Two different activity tests were conducted: a progressive constant-speed test (CS) performed on a track and a 6 min back-and-forth walk test including accelerations and decelerations (AC/DC) performed as fast as possible. Twenty-nine adults performed the CS test, and sixty-one performed the AC/DC test. The data were collected using hip-worn accelerometers and a portable metabolic gas analyzer. General linear models were employed to create the prediction models for VO[sub.2] that were cross-validated using both data sets and epoch types as training and validation sets. The prediction equations based on the CS test or AC/DC test and 6 s epoch had excellent performance (R[sup.2] = 89%) for the CS test but poor performance for the AC/DC test (31%). Only the VO[sub.2] prediction equation based on the AC/DC test and the floating epoch had good performance (78%) for both tests. The overall accuracy of VO[sub.2] prediction is compromised with the constant length epoch, whereas the prediction model based on irregular acceleration data analyzed with a floating epoch provided consistent performance for both activities.
Estimation of oxygen consumption (VO[sub.2]) from accelerometer data is typically based on prediction equations developed in laboratory settings using steadily paced and controlled test activities. These equations may not capture the temporary changes in VO[sub.2] occurring in sporadic real-life physical activity. In this study, we introduced a novel floating epoch for accelerometer data analysis and hypothesized that an adaptive epoch length provides a more consistent estimation of VO[sub.2] in irregular activity conditions than a 6 s constant epoch. Two different activity tests were conducted: a progressive constant-speed test (CS) performed on a track and a 6 min back-and-forth walk test including accelerations and decelerations (AC/DC) performed as fast as possible. Twenty-nine adults performed the CS test, and sixty-one performed the AC/DC test. The data were collected using hip-worn accelerometers and a portable metabolic gas analyzer. General linear models were employed to create the prediction models for VO[sub.2] that were cross-validated using both data sets and epoch types as training and validation sets. The prediction equations based on the CS test or AC/DC test and 6 s epoch had excellent performance (R[sup.2] = 89%) for the CS test but poor performance for the AC/DC test (31%). Only the VO[sub.2] prediction equation based on the AC/DC test and the floating epoch had good performance (78%) for both tests. The overall accuracy of VO[sub.2] prediction is compromised with the constant length epoch, whereas the prediction model based on irregular acceleration data analyzed with a floating epoch provided consistent performance for both activities.