Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm

Abstract

We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration (gravity-removal physical activity classification algorithm, GRPACA) measured by a triaxial accelerometer. The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer. A total of sixty-six subjects were randomly assigned into validation (n 44) and cross-validation (n 22) groups. All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting. During each activity, energy expenditure was measured by indirect calorimetry, and physical activity intensities were expressed as metabolic equivalents (MET). The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household (r 0·907, P,0·001) and locomotive (r 0·961, P, 0·001) activities. In the cross-validation group, two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities. Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions. Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts. Therefore, the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities, compared with previously reported cutoff classification models. This method may be useful for field investigations as well as for self-monitoring by general users.

Status of physical activity in the Japanese population

“…Recently, an activity monitor that can predict nonlocomotive PA (with the exception of cycling and stair climbing), as well as locomotive PA, was developed [43][44][45] . Several researchers, including Oshima et al, 30) have used this monitor for activity evaluation [46][47][48] .…”

Optimal Conditions for Obtaining Valid Step Count Measurements in Hospitalized Patients with Abnormal Gait

“…Regarding device performance, there are differences in the number of accelerometer axes (AS, triaxial accelerometer; LC, uniaxial accelerometer), the acceleration resolution (AS, 3 mG; LC, 60 mG), and the acceleration measurement range (AS, ±6 G; LC, 0.06-1.94 G). 10,13,25) Furthermore, differences in the algorithms used by the devices probably affected the results. However, it was not possible to examine the algorithms because their details are not publicly available.…”

Assessing sedentary behavior using wearable devices: An overview and future directions

“…This device estimates METs every 10 or 60 sec based on synthetic accelerations measured by a built-in tri-axial accelerometer 20,21) . The validity of METs estimation was confirmed with the Douglas bag method 21) .…”

“…This device estimates METs every 10 or 60 sec based on synthetic accelerations measured by a built-in tri-axial accelerometer 20,21) . The validity of METs estimation was confirmed with the Douglas bag method 21) . One of Active style Pro's unique features is a function to accurately distinguish between locomotive and non-locomotive (mainly lifestyle) activities 20) .…”