Abstract
The provided data are the raw sensor data gathered from the Google Pixel smartphones used during the field sampling for the development of the activity recognition models. Additionally, corresponding GNSS locations and observed cycle elements from time and motion study are included and are all matched by time stamps. Data is provided based on the sampling rates used for the study (10, 20, 50Hz) These data are labeled (AS_TM_GNSS_"date collected"_"sampling rate in Hz"). Individual tree records derived from the lidar acquisition are also included for the study area. These data are labeled (Unit_#_Trees). The R code provided gives foundational R-code for random forest model training and final models for all windows (1, 5, 7.5, and 10 seconds) for each of the sampling rates (10, 20, and 50Hz) for natively sampled and downsampled (ds) data.
zip directory that contains all the data and code for associated research manuscript submitted to PLOS ONE for review 01/03/2022
Manuscript Title: A novel smartphone-based activity recognition modeling method for tracked equipment in forest operations
Data Files:
Becker_Keefe_DOI_2022.txt:
Discovery level metadata utilized to establish DOI for dataset
AS_TM_GNSS_Combined folder:
The provided data are the raw sensor data gathered from the Google Pixel smartphones used during the field sampling for the development of the activity recognition models. Additionally, corresponding GNSS locations and observed cycle elements from time and motion study are included and are all matched by time stamps. Data is provided based on the sampling rates used for the study (10, 20, 50Hz) These data are labeled (AS_TM_GNSS_"date collected"_"sampling rate in Hz").
Data Files:
AS_TM_GNSS_8_10_10.csv
AS_TM_GNSS_8_10_20.csv
AS_TM_GNSS_8_10_50.csv
AS_TM_GNSS_8_11_10.csv
AS_TM_GNSS_8_11_20.csv
AS_TM_GNSS_8_11_50.csv
AS_TM_GNSS_9_20_10.csv
AS_TM_GNSS_9_20_20.csv
AS_TM_GNSS_9_20_50.csv
AS_TM_GNSS_9_21_10.csv
AS_TM_GNSS_9_21_20.csv
AS_TM_GNSS_9_21_50.csv
AS_TM_GNSS_9_26_10.csv
AS_TM_GNSS_9_26_20.csv
AS_TM_GNSS_9_26_50.csv
AS_TM_GNSS_9_27_10.csv
AS_TM_GNSS_9_27_20.csv
AS_TM_GNSS_9_27_50.csv
Single_Tree_Data folder:
Individual tree records derived from the lidar acquisition for the study area. These data are labeled (Unit_#_Trees).
Data Files:
Unit_1_Trees.csv
Unit_2_Trees.csv
Unit_3_Trees.csv
Code:
Random_forests folder:
The R code provided gives foundational R-code for random forest model training and final models for all windows (1, 5, 7.5, and 10 seconds) for each of the sampling rates (10, 20, and 50Hz) for natively sampled and downsampled (ds) data.
Code Files:
Random_Forest_10hz_ALL_WINDOWS_ds.R
Random_Forest_10hz_ALL_WINDOWS.R
Random_Forest_20hz_ALL_WINDOWS_ds.R
Random_Forest_20hz_ALL_WINDOWS.R
Random_Forest_50hz_ALL_WINDOWS.R
TRAIN_Random_Forest_10hz_10_ds.R
TRAIN_Random_Forest_10hz_10.R
TRAIN_Random_Forest_20hz_10_ds.R
TRAIN_Random_Forest_20hz_10.R
TRAIN_Random_Forest_50hz_10.R