g.imputeTimegaps

Data.frame with raw accelerometer data, and a timestamp column with millisecond resolution.

Columnnames or numbers for the x, y and z column

xyzCol

Column name or number for the timestamp column

timeCol

Minimum time gap length to be imputed

Removes all sample with a zero in each of the three axes, and then imputes time 
 gaps by the last recorded value per axis normalised to 1 _g_

A tool to process and analyse data collected with wearable raw acceleration sensors as described in Migueles and colleagues (JMPB 2019), and van Hees and colleagues (JApplPhysiol 2014; PLoSONE 2015). The package has been developed and tested for binary data from 'GENEActiv' <https://www.activinsights.com/> and GENEA devices (not for sale), .csv-export data from 'Actigraph' <https://actigraphcorp.com> devices, and .cwa and .wav-format data from 'Axivity' <https://axivity.com>. These devices are currently widely used in research on human daily physical activity. Further, the package can handle accelerometer data file from any other sensor brand providing that the data is stored in csv format and has either no header or a two column header. Also the package allows for external function embedding.

Vincent T van Hees

GGIR

Raw Accelerometer Data Analysis

Zhou Fang

Jing Hua Zhao

Joe Heywood

Evgeny Mirkes

Severine Sabia

Joan Capdevila Pujol

Jairo H Migueles

Matthew R Patterson

Dan Jackson

Lena Kushleyeva

Mathilde Chen

Manasa Yerramalla

Tuomo Nieminen

John Muschelli

Patrick Bos

g.imputeTimegaps function

Impute gaps in three axis raw accelerometer data — g.imputeTimegaps

Impute gaps in three axis raw accelerometer data

g.imputeTimegaps: Impute gaps in three axis raw accelerometer data

Description

Usage

Arguments

Value