This vignette provides an example of segmentation of walking strides (two consecutive steps) in sub-second accelerometry data with
adept package. The exemplary dataset is a part of the
adeptdata package. We demonstrate that ADEPT1 can be used to perform automatic and precise walking stride segmentation from data collected during a combination of running, walking and resting exercises. We introduce how to segment data:
See Introduction to adept package2 vignette for an introduction to the ADEPT and usage examples of the
segmentPattern function which implements the ADEPT method.
adeptdata package contains
acc_running - a sample of raw accelerometry data collected during 25 minutes of an outdoor run. Data were collected at the sampling frequency of 100 Hz with two ActiGraph GT9X Link sensors located at the left hip and left ankle.
mapmyrun mobile tracking application (link) was used during 25 minutes of running (Patterson Park area, Baltimore, MD) to collect
acc_running accelerometry data set. Based on the mobile app, the distance covered is approximately 3.35 km. A ground elevation plot generated by the mobile app presents signature trial characteristics (see figure below). The timestamp in
acc_running dataset matches the mobile app up to ~1-minute.
Screenshot taken from a personal profile of mapmyrun tracking application, accessed via https://www.mapmyrun.com.
Data were collected with two ActiGraph GT9X Link physical activity monitors at the sampling frequency of 100 Hz. ActiGraph GT9X Link has 3-axis accelerometer collecting accelerometry data along three orthogonal axes. At a sampling frequency of 100 Hz, we collected 100 observations per second per axis (total of 300 observations per second).
First sensor (denoted as “left_ankle”) was attached to the outer side of the left shoe with a slide-on clip, just below the ankle. A second sensor (denoted as “left_hip”) was attached on the left side of the elastic belt located hip (see image below). Both devices remained stable during the run-trial.