Validity of using tri-axial accelerometers to measure human movement - Part I: Posture and movement detection

Abstract

A robust method for identifying movement in the free-living environment is needed to objectively measure physical activity. The purpose of this study was to validate the identification of postural orientation and movement from acceleration data against visual inspection from video recordings. Using tri-axial accelerometers placed on the waist and thigh, static orientations of standing, sitting, and lying down, as well as dynamic movements of walking, jogging and transitions between postures were identified. Additionally, subjects walked and jogged at self-selected slow, comfortable, and fast speeds. Identification of tasks was performed using a combination of the signal magnitude area, continuous wavelet transforms and accelerometer orientations. Twelve healthy adults were studied in the laboratory, with two investigators identifying tasks during each second of video observation. The intraclass correlation coefficients for inter-rater reliability were greater than 0.95 for all activities except for transitions. Results demonstrated high validity, with sensitivity and positive predictive values of greater than 85% for sitting and lying, with walking and jogging identified at greater than 90%. The greatest disagreement in identification accuracy between the algorithm and video occurred when subjects were asked to fidget while standing or sitting. During variable speed tasks, gait was correctly identified for speeds between 0.1m/s and 4.8m/s. This study included a range of walking speeds and natural movements such as fidgeting during static postures, demonstrating that accelerometer data can be used to identify orientation and movement among the general population.

Keywords: Acceleration; Accuracy; Gait velocity; Movement analysis.

Conflict of interest statement

Competing Interests: The authors report no conflict of interest.

Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Figures

Figure 1

Decision algorithm for the possible posture and activity classifications determined from the accelerometer data. SMA refers to the signal magnitude area and CWT to the continuous wavelet transform.

Figure 2

Sample data of the waist accelerations (g = 9.81 m/s2) collected for a subject while performing a series of tasks (A) and walking at a range of gait speeds (B).

Figure 3

Sensitivity (A) and positive predictive value (B) when identifying static orientations and dynamic movements with accelerometer data compared to video identification among all subjects. The central line represents the median, the edges of the box are the 25th and 75th percentiles, and the whiskers extend to ± 1.5 of the interquartile range. Outliers beyond this range are labeled as +. For the PPV of jogging, the median value is equal to 100%.

Figure 4

Bland-Altman plots demonstrating error in identifying each of the static and dynamic activities when using accelerometer compared to video identification. The data for each of the 12 subjects studied includes fidgeting while sitting or standing. The dashed line is the average, while the solid lines represent the repeatability coefficient (± 1.96 SD).

Figure 5

Boxplot of activity detection when walking at a range of gait velocities. The median sensitivity (A) was greater than 84% and the median PPV (B) 100% at all velocities. The central line represents the median, the edges of the box are the 25th and 75th percentiles, and the whiskers extend to ± 1.5 of the interquartile range. Outliers beyond this range are labeled as +.