readAxivity close to OMGUI

[muschellij2]

ap = reticulate::py_require(c("actipy", "pyarrow"))
ap = reticulate::import("actipy")
library(tidyverse)
#> Warning: package 'ggplot2' was built under R version 4.4.3
#> Warning: package 'tibble' was built under R version 4.4.1
#> Warning: package 'tidyr' was built under R version 4.4.1
#> Warning: package 'purrr' was built under R version 4.4.3
#> Warning: package 'dplyr' was built under R version 4.4.3
#> Warning: package 'stringr' was built under R version 4.4.1
library(GGIRread)
#> Warning: package 'GGIRread' was built under R version 4.4.3
library(feather)
#> Warning: package 'feather' was built under R version 4.4.1
library(googledrive)
library(clock)
#> 
#> Attaching package: 'clock'
#> The following object is masked from 'package:lubridate':
#> 
#>     as_date

options(digits.secs = 3L)

Read in CWA File

Example CWA file

This is the example data from the Axivity web page

zip_file = tempfile(fileext = ".zip")
url = "https://github.com/openmovementproject/openmovement/raw/refs/heads/master/Downloads/AX3/longitudinal_data.zip"
download.file(url, zip_file, mode = "wb")
file = unzip(zip_file, exdir = tempdir())
file
#> [1] "/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpTvLVc1/longitudinal_data.cwa"

CSV file from OMGUI

This is the data output from passing in the CWA to get a CSVfrom using OMGUI. This is the gold standard in the sense that Axivity lists it on the website as the software to use.

csv_file = tempfile(fileext = ".csv.gz")
csv_url = "https://www.dropbox.com/scl/fi/c03dkupyxlchept11gtwh/longitudinal_data.csv.gz?rlkey=njsm0bz36fzib7170t3uly4kp&dl=1"
curl::curl_download(csv_url, csv_file)

# This is the output to compare the output
om_gui_output = readr::read_csv(csv_file,
                                col_names = c("time", "x", "y", "z"))
#> Rows: 60337200 Columns: 4
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> dbl  (3): x, y, z
#> dttm (1): time
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
om_gui_output = om_gui_output |>
  mutate(time = with_tz(time, Sys.timezone()))

Reading in CWA Data using GGIRread

Here is reading the data using GGIRread

r_list = readAxivity(file, end = Inf, progressBar = TRUE, desiredtz = "UTC", configtz = "UTC",
                     loadbattery = FALSE)
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r_data = r_list$data |>
  select(time, x, y, z)
r_data = r_data |>
  mutate(time = as.POSIXct(time, tz = Sys.timezone()))
r_data = r_data |>
  as_tibble()

Using actipy

Here we will use a call out to actipy from PyPi to compare to GGIRread

ap = reticulate::import("actipy", convert = FALSE)

Read in the device without processing

data = ap$read_device(
  file,
  lowpass_hz = FALSE,
  calibrate_gravity = FALSE,
  detect_nonwear = FALSE,
  resample_hz = FALSE)
#> Reading file...Reading file... Done! (13.10s)
#> Converting to dataframe...Converting to dataframe... Done! (9.62s)
#> Quality control...Quality control... Done! (6.70s)

Grab the data

data = reticulate::py_get_item(data, 0L)

Here, instead of converting from Python to R, we can use feather as an intermediary because DateTimeIndex from pandas has some conversion issues in reticulate.

tfile = tempfile(fileext = ".feather")
data$to_feather(tfile)
#> None
rm(data); gc()
#>             used   (Mb) gc trigger    (Mb) limit (Mb)   max used   (Mb)
#> Ncells   2711388  144.9    4819517   257.4         NA    4819517  257.4
#> Vcells 666834440 5087.6 1386603308 10579.0      65536 1147693973 8756.3
data = feather::read_feather(tfile)
file.remove(tfile)
#> [1] TRUE

tz(data$time) = Sys.timezone()
# time goes first
data = data |>
  dplyr::select(time, x, y, z)

Checking results

Comparing actipy and OMGUI

all.equal(om_gui_output, data)
#> [1] TRUE

Comparing GGIRread and OMGUI

all.equal(om_gui_output, r_data)
#> [1] "Attributes: < Component \"row.names\": Numeric: lengths (60337200, 60110386) differ >"
#> [2] "Component \"time\": Numeric: lengths (60337200, 60110386) differ"                     
#> [3] "Component \"x\": Numeric: lengths (60337200, 60110386) differ"                        
#> [4] "Component \"y\": Numeric: lengths (60337200, 60110386) differ"                        
#> [5] "Component \"z\": Numeric: lengths (60337200, 60110386) differ"

Reading in actipy with uniform resampling

udata = ap$read_device(
  file,
  lowpass_hz = FALSE,
  calibrate_gravity = FALSE,
  detect_nonwear = FALSE,
  resample_hz = "uniform")
#> Reading file...Reading file... Done! (11.96s)
#> Converting to dataframe...Converting to dataframe... Done! (9.42s)
#> Quality control...Quality control... Done! (6.67s)
#> Resampling...Resampling... Done! (22.06s)

Grab the data

udata = reticulate::py_get_item(udata, 0L)

tfile = tempfile(fileext = ".feather")
udata$to_feather(tfile)
#> None
rm(udata); gc()
#>             used   (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
#> Ncells   2784592  148.8    4819517   257.4         NA    4819517   257.4
#> Vcells 908316718 6930.0 1386603308 10579.0      65536 1374681881 10488.0
udata = feather::read_feather(tfile)
file.remove(tfile)
#> [1] TRUE

tz(udata$time) = Sys.timezone()
# time goes first
udata = udata |>
  dplyr::select(time, x, y, z)

Comparing Data Features

We can see the comparison, but future comparisons are only relevant for data and r_data since data == om_gui_output.

nrow(r_data)
#> [1] 60110386
nrow(data)
#> [1] 60337200
nrow(om_gui_output)
#> [1] 60337200

The sizes of the data are very different. This is because GGIRread does resampling for each block as per

GGIRread/R/readAxivity.R

Line 655 in 302919a

tmp = resample(rawAccel, rawTime, timeRes[pos:last], rawLast, type = interpolationType)

We can see the GGIRread output is similar in size to the uniform resampled data.

nrow(r_data)
#> [1] 60110386
nrow(udata)
#> [1] 60110388

The time covered is the same (data is in EST local for me, to keep things the same):

range(data$time)
#> [1] "2013-02-19 08:00:04.28 EST" "2013-02-26 06:58:28.16 EST"
range(r_data$time)
#> [1] "2013-02-19 08:00:04.28 EST" "2013-02-26 06:58:28.13 EST"
range(udata$time)
#> [1] "2013-02-19 08:00:04.28 EST" "2013-02-26 06:58:28.16 EST"

We see some small differences at the head of the data

head(data)
#> # A tibble: 6 × 4
#>   time                          x       y       z
#>   <dttm>                    <dbl>   <dbl>   <dbl>
#> 1 2013-02-19 08:00:04.289  0.0625 -0.0469 -0.922 
#> 2 2013-02-19 08:00:04.299 -0.969  -0.0469  0.0469
#> 3 2013-02-19 08:00:04.310 -1.02   -0.0469  0.0469
#> 4 2013-02-19 08:00:04.319 -1.03   -0.0469  0.0469
#> 5 2013-02-19 08:00:04.329 -1.03   -0.0469  0.0469
#> 6 2013-02-19 08:00:04.339 -1.03   -0.0469  0.0469
head(r_data)
#> # A tibble: 6 × 4
#>   time                          x       y       z
#>   <dttm>                    <dbl>   <dbl>   <dbl>
#> 1 2013-02-19 08:00:04.289  0.0625 -0.0469 -0.922 
#> 2 2013-02-19 08:00:04.299 -0.969  -0.0469  0.0469
#> 3 2013-02-19 08:00:04.309 -1.02   -0.0469  0.0469
#> 4 2013-02-19 08:00:04.319 -1.03   -0.0469  0.0469
#> 5 2013-02-19 08:00:04.329 -1.03   -0.0469  0.0469
#> 6 2013-02-19 08:00:04.339 -1.03   -0.0469  0.0462
head(udata)
#> # A tibble: 6 × 4
#>   time                          x       y       z
#>   <dttm>                    <dbl>   <dbl>   <dbl>
#> 1 2013-02-19 08:00:04.289  0.0625 -0.0469 -0.922 
#> 2 2013-02-19 08:00:04.299 -0.969  -0.0469  0.0469
#> 3 2013-02-19 08:00:04.310 -1.02   -0.0469  0.0469
#> 4 2013-02-19 08:00:04.319 -1.03   -0.0469  0.0469
#> 5 2013-02-19 08:00:04.329 -1.03   -0.0469  0.0469
#> 6 2013-02-19 08:00:04.339 -1.03   -0.0469  0.0469

We see more differences towards the tail of the data, but time is somewhat mis-aligned

tail(data)
#> # A tibble: 6 × 4
#>   time                          x       y      z
#>   <dttm>                    <dbl>   <dbl>  <dbl>
#> 1 2013-02-26 06:58:28.110 -0.0156 -0.0156 -0.984
#> 2 2013-02-26 06:58:28.119 -0.0156 -0.0156 -0.984
#> 3 2013-02-26 06:58:28.129 -0.0156 -0.0156 -0.984
#> 4 2013-02-26 06:58:28.140 -0.0156 -0.0156 -0.969
#> 5 2013-02-26 06:58:28.150 -0.0156 -0.0312 -0.969
#> 6 2013-02-26 06:58:28.160 -0.0156 -0.0312 -0.984
tail(r_data)
#> # A tibble: 6 × 4
#>   time                          x       y      z
#>   <dttm>                    <dbl>   <dbl>  <dbl>
#> 1 2013-02-26 06:58:28.089 -0.0156 -0.0156 -0.977
#> 2 2013-02-26 06:58:28.099 -0.0156 -0.0156 -0.969
#> 3 2013-02-26 06:58:28.109 -0.0156 -0.0231 -0.969
#> 4 2013-02-26 06:58:28.119 -0.0156 -0.0310 -0.969
#> 5 2013-02-26 06:58:28.129 -0.0156 -0.0312 -0.976
#> 6 2013-02-26 06:58:28.139 -0.0156 -0.0312 -0.984
tail(udata)
#> # A tibble: 6 × 4
#>   time                          x       y      z
#>   <dttm>                    <dbl>   <dbl>  <dbl>
#> 1 2013-02-26 06:58:28.110 -0.0156 -0.0156 -0.984
#> 2 2013-02-26 06:58:28.119 -0.0156 -0.0156 -0.984
#> 3 2013-02-26 06:58:28.129 -0.0156 -0.0156 -0.984
#> 4 2013-02-26 06:58:28.140 -0.0156 -0.0156 -0.969
#> 5 2013-02-26 06:58:28.150 -0.0156 -0.0312 -0.969
#> 6 2013-02-26 06:58:28.160 -0.0156 -0.0312 -0.984

We see differences in the data, but it's unclear how this affects downstream analysis

keep_cols = c("time", "x", "y", "z")
data[3714:3720, keep_cols]
#> # A tibble: 7 × 4
#>   time                         x       y      z
#>   <dttm>                   <dbl>   <dbl>  <dbl>
#> 1 2013-02-19 08:00:41.230 -0.703 -0.109  -0.359
#> 2 2013-02-19 08:00:41.240 -0.703 -0.0938 -0.406
#> 3 2013-02-19 08:00:41.249 -0.734 -0.0625 -0.5  
#> 4 2013-02-19 08:00:41.259 -0.781 -0.0156 -0.562
#> 5 2013-02-19 08:00:41.270 -0.828  0.0469 -0.484
#> 6 2013-02-19 08:00:41.279 -0.875  0.0781 -0.422
#> 7 2013-02-19 08:00:41.289 -0.953  0.109  -0.344
r_data[3695:3701, keep_cols]
#> # A tibble: 7 × 4
#>   time                         x       y      z
#>   <dttm>                   <dbl>   <dbl>  <dbl>
#> 1 2013-02-19 08:00:41.230 -0.703 -0.0945 -0.404
#> 2 2013-02-19 08:00:41.240 -0.733 -0.0638 -0.496
#> 3 2013-02-19 08:00:41.250 -0.780 -0.0172 -0.560
#> 4 2013-02-19 08:00:41.259 -0.827  0.0453 -0.486
#> 5 2013-02-19 08:00:41.269 -0.874  0.0776 -0.423
#> 6 2013-02-19 08:00:41.279 -0.952  0.109  -0.344
#> 7 2013-02-19 08:00:41.289 -0.969  0.109  -0.281
udata[3695:3701, keep_cols]
#> # A tibble: 7 × 4
#>   time                         x       y      z
#>   <dttm>                   <dbl>   <dbl>  <dbl>
#> 1 2013-02-19 08:00:41.230 -0.703 -0.109  -0.359
#> 2 2013-02-19 08:00:41.240 -0.703 -0.0938 -0.406
#> 3 2013-02-19 08:00:41.249 -0.734 -0.0625 -0.5  
#> 4 2013-02-19 08:00:41.259 -0.781 -0.0156 -0.562
#> 5 2013-02-19 08:00:41.270 -0.828  0.0469 -0.484
#> 6 2013-02-19 08:00:41.279 -0.875  0.0781 -0.422
#> 7 2013-02-19 08:00:41.289 -0.953  0.109  -0.344
r_data[3695:3701,keep_cols]- data[3714:3720,keep_cols]
#>                 time           x          y           z
#> 1  0.000000e+00 secs  0.00000000 0.01483725 -0.04451175
#> 2  0.000000e+00 secs -0.02993743 0.02993743 -0.08981229
#> 3  2.384186e-07 secs -0.04529950 0.04529950 -0.06039934
#> 4  0.000000e+00 secs -0.04569391 0.06092522  0.07615652
#> 5 -2.384186e-07 secs -0.04608725 0.03072483  0.06144967
#> 6  0.000000e+00 secs -0.07746947 0.03098779  0.07746947
#> 7  0.000000e+00 secs -0.01562500 0.00000000  0.06250000

actipy resampling can be odd

If we look at the uniform sampling output of actipy, we can see some odd values of 0:

tail(udata, n = 20)
#> # A tibble: 20 × 4
#>    time                          x       y      z
#>    <dttm>                    <dbl>   <dbl>  <dbl>
#>  1 2013-02-26 06:58:27.969  0       0      -0.969
#>  2 2013-02-26 06:58:27.980  0.0156 -0.0312 -0.969
#>  3 2013-02-26 06:58:27.990  0      -0.0156 -0.984
#>  4 2013-02-26 06:58:28.000 -0.0312  0      -0.984
#>  5 2013-02-26 06:58:28.009 -0.0156  0      -0.969
#>  6 2013-02-26 06:58:28.019 -0.0156  0      -0.969
#>  7 2013-02-26 06:58:28.030 -0.0156 -0.0156 -0.984
#>  8 2013-02-26 06:58:28.039  0      -0.0156 -0.984
#>  9 2013-02-26 06:58:28.049 -0.0156 -0.0156 -0.984
#> 10 2013-02-26 06:58:28.059 -0.0156 -0.0156 -0.984
#> 11 2013-02-26 06:58:28.070 -0.0312  0      -0.969
#> 12 2013-02-26 06:58:28.079 -0.0156 -0.0156 -0.969
#> 13 2013-02-26 06:58:28.089 -0.0156 -0.0156 -0.984
#> 14 2013-02-26 06:58:28.099 -0.0156 -0.0156 -0.984
#> 15 2013-02-26 06:58:28.110 -0.0156 -0.0156 -0.984
#> 16 2013-02-26 06:58:28.119 -0.0156 -0.0156 -0.984
#> 17 2013-02-26 06:58:28.129 -0.0156 -0.0156 -0.984
#> 18 2013-02-26 06:58:28.140 -0.0156 -0.0156 -0.969
#> 19 2013-02-26 06:58:28.150 -0.0156 -0.0312 -0.969
#> 20 2013-02-26 06:58:28.160 -0.0156 -0.0312 -0.984

We do not see these issues similarly in GGIRread output

tail(r_data, n = 18)
#> # A tibble: 18 × 4
#>    time                          x        y      z
#>    <dttm>                    <dbl>    <dbl>  <dbl>
#>  1 2013-02-26 06:58:27.970 -0.0247 -0.00657 -0.969
#>  2 2013-02-26 06:58:27.980 -0.0168 -0.0144  -0.969
#>  3 2013-02-26 06:58:27.990 -0.0156 -0.0156  -0.975
#>  4 2013-02-26 06:58:28.000 -0.0156 -0.0156  -0.983
#>  5 2013-02-26 06:58:28.009 -0.0156 -0.0156  -0.984
#>  6 2013-02-26 06:58:28.019 -0.0156 -0.0156  -0.984
#>  7 2013-02-26 06:58:28.029 -0.0156 -0.0156  -0.984
#>  8 2013-02-26 06:58:28.039 -0.0156 -0.0156  -0.984
#>  9 2013-02-26 06:58:28.049 -0.0156 -0.0156  -0.984
#> 10 2013-02-26 06:58:28.059 -0.0156 -0.0156  -0.984
#> 11 2013-02-26 06:58:28.069 -0.0156 -0.0156  -0.984
#> 12 2013-02-26 06:58:28.079 -0.0156 -0.0156  -0.984
#> 13 2013-02-26 06:58:28.089 -0.0156 -0.0156  -0.977
#> 14 2013-02-26 06:58:28.099 -0.0156 -0.0156  -0.969
#> 15 2013-02-26 06:58:28.109 -0.0156 -0.0231  -0.969
#> 16 2013-02-26 06:58:28.119 -0.0156 -0.0310  -0.969
#> 17 2013-02-26 06:58:28.129 -0.0156 -0.0312  -0.976
#> 18 2013-02-26 06:58:28.139 -0.0156 -0.0312  -0.984

Conclusion

If users are aiming to replicate the output from OMGUI, GGIRread will not give it, but actipy can, but the data should likely be resampled. I’m not sure how this affects downstream analysis overall. The actipy uniform resampling has its own issues. If possible, maybe allow users to get the raw data out and do resampling on their own.

df = reticulate::py_list_packages()
df$version[df$package == "actipy"]
#> [1] "3.8.0"

^{Created on 2026-06-23 with reprex v2.1.1}

Session info

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.4.0 (2024-04-24)
#>  os       macOS 26.5.1
#>  system   x86_64, darwin20
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       America/New_York
#>  date     2026-06-23
#>  pandoc   3.8.3 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/x86_64/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package      * version    date (UTC) lib source
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#>  assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.4.0)
#>  bit            4.6.0      2025-03-06 [1] CRAN (R 4.4.1)
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#>  cli            3.6.6      2026-04-09 [1] CRAN (R 4.4.3)
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#>  crayon         1.5.3      2024-06-20 [1] CRAN (R 4.4.0)
#>  curl           7.1.0      2026-04-22 [1] CRAN (R 4.4.3)
#>  data.table     1.18.4     2026-05-06 [1] CRAN (R 4.4.3)
#>  dichromat      2.0-0.1    2022-05-02 [1] CRAN (R 4.4.0)
#>  digest         0.6.39     2025-11-19 [1] CRAN (R 4.4.1)
#>  dplyr        * 1.2.1      2026-04-03 [1] CRAN (R 4.4.3)
#>  evaluate       1.0.5      2025-08-27 [1] CRAN (R 4.4.1)
#>  farver         2.1.2      2024-05-13 [1] CRAN (R 4.4.0)
#>  fastmap        1.2.0      2024-05-15 [1] CRAN (R 4.4.0)
#>  feather      * 0.4.0      2025-12-06 [1] CRAN (R 4.4.1)
#>  forcats      * 1.0.0      2023-01-29 [1] CRAN (R 4.4.0)
#>  fs             2.1.0      2026-04-18 [1] CRAN (R 4.4.3)
#>  gargle         1.5.2      2023-07-20 [1] CRAN (R 4.4.0)
#>  generics       0.1.4      2025-05-09 [1] CRAN (R 4.4.1)
#>  GGIRread     * 1.0.8      2026-03-11 [1] CRAN (R 4.4.3)
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#> ──────────────────────────────────────────────────────────────────────────────

[vincentvanhees]

Thanks for sharing your observations John. Hereby my reflections:

1. The OMGUI csv data (dropbox link in your code) appears to have more variation in sample to sample timestamp differences (standard deviation 0.0006 seconds) compared with readAxivity output (standard deviation 0.00000004 seconds). Could it be that you are using the non-resampled OMGUI data? If yes, then I would not consider that a gold standard.
2. Axivity documentation does not provide guidance on how to deal with faulty data blocks other than the irregular sampling rate. GGIRread's readAxivity makes an elaborate attempt to take care of this issue. The test file you used seems healthy as readAxivity returns an empty QClog object, meaning no problems detected, so this may not explain the differences you observed but it is worth keeping in mind when doing this comparison in larger datasets.
3. Interpolation type may indeed play a role when comparing the different implementations.
4. Consider plotting all time series to detect time shifts and discrepancies in time gaps. I expect this to be especially relevant when faulty data blocks exists and are not correctly handled.
5. Before facilitating the option to turn off resampling, it would be good to understand if commenting out the internal resampling step truly provides more comparable output between non-resampled omgui and non-resampled readAxivity.

[muschellij2]

1. I have added the resampled data - which has even more oddities than I expected.
2. I think that's a great point about bad blocks - I unfortunately have no guidance on this.
3. Interpolation for sure has an effect. I am discussing over at zeroes in resampling_hz = "uniform" OxWearables/actipy#69 that linear interpolation may be worthwhile.
4. Big plot - any thoughts on doing VM vs. individual axes
5. Given some of these differences, I am apt to read in raw data with actipy an do my own resampling or use uniform.

It's still unclear how this affects downstream analysis (e.g. non-wear, activity levels).

ap = reticulate::py_require(c("actipy", "pyarrow"))
ap = reticulate::import("actipy", convert = FALSE)
library(tidyverse)
#> Warning: package 'ggplot2' was built under R version 4.4.3
#> Warning: package 'tibble' was built under R version 4.4.1
#> Warning: package 'tidyr' was built under R version 4.4.1
#> Warning: package 'purrr' was built under R version 4.4.3
#> Warning: package 'dplyr' was built under R version 4.4.3
#> Warning: package 'stringr' was built under R version 4.4.1
library(GGIRread)
#> Warning: package 'GGIRread' was built under R version 4.4.3
library(feather)
#> Warning: package 'feather' was built under R version 4.4.1
library(googledrive)
library(clock)
#> 
#> Attaching package: 'clock'
#> The following object is masked from 'package:lubridate':
#> 
#>     as_date

options(digits.secs = 3L)

Read in CWA File

Example CWA file

This is the example data from the Axivity web page

zip_file = tempfile(fileext = ".zip")
url = "https://github.com/openmovementproject/openmovement/raw/refs/heads/master/Downloads/AX3/longitudinal_data.zip"
download.file(url, zip_file, mode = "wb")
file = unzip(zip_file, exdir = tempdir())
file
#> [1] "/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpQd2qDb/longitudinal_data.cwa"

RESAMPLED CSV file from OMGUI

This is the data output from passing in the RESAMPLED CWA to get a CSV from using OMGUI. This is the gold standard in the sense that Axivity lists it on the website as the software to use.
But we will see, there are some issues with missing data in the tail.

csv_file = tempfile(fileext = ".csv.gz")
csv_url = "https://www.dropbox.com/scl/fi/o5amq3cx71w1c9x58jwyt/longitudinal_data.resampled.csv.gz?rlkey=nrbvpnlonmdlyfg3awci0yctj&dl=1"
curl::curl_download(csv_url, csv_file)

# This is the output to compare the output
om_gui_output = readr::read_csv(csv_file)
#> Rows: 59834078 Columns: 4
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> dbl  (3): Accel-X (g), Accel-Y (g), Accel-Z (g)
#> dttm (1): Time
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
colnames(om_gui_output) = c("time", "x", "y", "z")
om_gui_output = om_gui_output |>
  mutate(time = with_tz(time, Sys.timezone()))

Reading in CWA Data using GGIRread

Here is reading the data using GGIRread

r_list = readAxivity(file, end = Inf, progressBar = TRUE, desiredtz = "UTC", configtz = "UTC",
                     loadbattery = FALSE)
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r_data = r_list$data |>
  select(time, x, y, z)
r_data = r_data |>
  mutate(time = as.POSIXct(time, tz = Sys.timezone()))
r_data = r_data |>
  as_tibble()

Using actipy

Here we will use a call out to actipy from PyPi to compare to GGIRread

read_actipy = function(file, resample_hz = FALSE) {
  ap = reticulate::import("actipy", convert = FALSE)

  #' Read in the device without processing
  data = ap$read_device(
    file,
    lowpass_hz = FALSE,
    calibrate_gravity = FALSE,
    detect_nonwear = FALSE,
    resample_hz = resample_hz)
  summary = reticulate::py_get_item(data, 1L)
  summary = reticulate::py_to_r(summary)

  #' Grab the data
  data = reticulate::py_get_item(data, 0L)

  #' Here, instead of converting from Python to R, we can use feather as an intermediary because DateTimeIndex from `pandas` has some conversion issues in `reticulate`.
  tfile = tempfile(fileext = ".feather")
  data$to_feather(tfile)
  rm(data); gc()
  data = feather::read_feather(tfile)
  file.remove(tfile)

  attr(data, "header") = summary
  attr(data, "sample_rate") = summary$ResampleRate
  if (is.null(attr(data, 'sample_rate'))) {
    attr(data, "sample_rate") = summary$SampleRate
  }
  print(summary)

  tz(data$time) = Sys.timezone()
  # time goes first
  data = data |>
    dplyr::select(time, x, y, z)
  data
}

data = read_actipy(file, resample_hz = "uniform")
#> Reading file...Reading file... Done! (13.08s)
#> Converting to dataframe...Converting to dataframe... Done! (8.97s)
#> Quality control...Quality control... Done! (7.69s)
#> Resampling...Resampling... Done! (24.87s)
#> $Filename
#> [1] "/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpQd2qDb/longitudinal_data.cwa"
#> 
#> $`Filesize(MB)`
#> [1] 245.5
#> 
#> $Device
#> [1] "Axivity"
#> 
#> $DeviceID
#> [1] 1696
#> 
#> $ReadErrors
#> [1] 0
#> 
#> $SampleRate
#> [1] 100
#> 
#> $ReadOK
#> [1] 1
#> 
#> $StartTime
#> [1] "2013-02-19 13:00:04"
#> 
#> $EndTime
#> [1] "2013-02-26 11:58:28"
#> 
#> $NumTicks
#> [1] 60337200
#> 
#> $`WearTime(days)`
#> [1] 6.957221
#> 
#> $`DataSpan(days)`
#> [1] 6.957221
#> 
#> $NumInterrupts
#> [1] 0
#> 
#> $Covers24hOK
#> [1] 1
#> 
#> $ResampleRate
#> [1] 100
#> 
#> $NumTicksAfterResample
#> [1] 60110388
raw_data = read_actipy(file, resample_hz = FALSE)
#> Reading file...Reading file... Done! (13.68s)
#> Converting to dataframe...Converting to dataframe... Done! (2.27s)
#> Quality control...Quality control... Done! (7.28s)
#> $Filename
#> [1] "/var/folders/1s/wrtqcpxn685_zk570bnx9_rr0000gr/T//RtmpQd2qDb/longitudinal_data.cwa"
#> 
#> $`Filesize(MB)`
#> [1] 245.5
#> 
#> $Device
#> [1] "Axivity"
#> 
#> $DeviceID
#> [1] 1696
#> 
#> $ReadErrors
#> [1] 0
#> 
#> $SampleRate
#> [1] 100
#> 
#> $ReadOK
#> [1] 1
#> 
#> $StartTime
#> [1] "2013-02-19 13:00:04"
#> 
#> $EndTime
#> [1] "2013-02-26 11:58:28"
#> 
#> $NumTicks
#> [1] 60337200
#> 
#> $`WearTime(days)`
#> [1] 6.957221
#> 
#> $`DataSpan(days)`
#> [1] 6.957221
#> 
#> $NumInterrupts
#> [1] 0
#> 
#> $Covers24hOK
#> [1] 1

Checking results

Comparing actipy and OMGUI

all.equal(om_gui_output, data)
#>  [1] "Attributes: < Names: 1 string mismatch >"                         
#>  [2] "Attributes: < Length mismatch: comparison on first 2 components >"
#>  [3] "Attributes: < Component 2: Modes: numeric, list >"                
#>  [4] "Attributes: < Component 2: Lengths: 59834078, 16 >"               
#>  [5] "Attributes: < Component 2: names for current but not for target >"
#>  [6] "Attributes: < Component 2: target is numeric, current is list >"  
#>  [7] "Component \"time\": Numeric: lengths (59834078, 60110388) differ" 
#>  [8] "Component \"x\": Numeric: lengths (59834078, 60110388) differ"    
#>  [9] "Component \"y\": Numeric: lengths (59834078, 60110388) differ"    
#> [10] "Component \"z\": Numeric: lengths (59834078, 60110388) differ"
all.equal(om_gui_output, raw_data)
#>  [1] "Attributes: < Names: 1 string mismatch >"                         
#>  [2] "Attributes: < Length mismatch: comparison on first 2 components >"
#>  [3] "Attributes: < Component 2: Modes: numeric, list >"                
#>  [4] "Attributes: < Component 2: Lengths: 59834078, 14 >"               
#>  [5] "Attributes: < Component 2: names for current but not for target >"
#>  [6] "Attributes: < Component 2: target is numeric, current is list >"  
#>  [7] "Component \"time\": Numeric: lengths (59834078, 60337200) differ" 
#>  [8] "Component \"x\": Numeric: lengths (59834078, 60337200) differ"    
#>  [9] "Component \"y\": Numeric: lengths (59834078, 60337200) differ"    
#> [10] "Component \"z\": Numeric: lengths (59834078, 60337200) differ"

Comparing GGIRread and OMGUI

all.equal(om_gui_output, r_data)
#> [1] "Attributes: < Component \"row.names\": Numeric: lengths (59834078, 60110386) differ >"
#> [2] "Component \"time\": Numeric: lengths (59834078, 60110386) differ"                     
#> [3] "Component \"x\": Numeric: lengths (59834078, 60110386) differ"                        
#> [4] "Component \"y\": Numeric: lengths (59834078, 60110386) differ"                        
#> [5] "Component \"z\": Numeric: lengths (59834078, 60110386) differ"

Comparing Data Features

We can see the comparison of rows, and that again GGIR output is close to the resampled actipy output.
The sizes of the data are quite different from the raw due to non-uniform resampling.

nrow(data)
#> [1] 60110388
nrow(raw_data)
#> [1] 60337200
nrow(r_data)
#> [1] 60110386
nrow(om_gui_output)
#> [1] 59834078

The OMGUI output for resampled data interpolates the first minute fully (e.g. adds for seconds).

range(data$time)
#> [1] "2013-02-19 08:00:04.28 EST" "2013-02-26 06:58:28.16 EST"
range(raw_data$time)
#> [1] "2013-02-19 08:00:04.28 EST" "2013-02-26 06:58:28.16 EST"
range(r_data$time)
#> [1] "2013-02-19 08:00:04.28 EST" "2013-02-26 06:58:28.13 EST"
range(om_gui_output$time)
#> [1] "2013-02-19 08:00:00.000 EST" "2013-02-26 06:12:20.769 EST"

Missing End Data

It looks as though there are 46 minutes missing from the end of the data for OMGUI

max(data$time)
#> [1] "2013-02-26 06:58:28.16 EST"
max(raw_data$time)
#> [1] "2013-02-26 06:58:28.16 EST"
max(om_gui_output$time)
#> [1] "2013-02-26 06:12:20.769 EST"
max(r_data$time)
#> [1] "2013-02-26 06:58:28.13 EST"

The first sets of data look similar, but still different:

head(data)
#> # A tibble: 6 × 4
#>   time                          x       y       z
#>   <dttm>                    <dbl>   <dbl>   <dbl>
#> 1 2013-02-19 08:00:04.289  0.0625 -0.0469 -0.922 
#> 2 2013-02-19 08:00:04.299 -0.969  -0.0469  0.0469
#> 3 2013-02-19 08:00:04.310 -1.02   -0.0469  0.0469
#> 4 2013-02-19 08:00:04.319 -1.03   -0.0469  0.0469
#> 5 2013-02-19 08:00:04.329 -1.03   -0.0469  0.0469
#> 6 2013-02-19 08:00:04.339 -1.03   -0.0469  0.0469
head(raw_data)
#> # A tibble: 6 × 4
#>   time                          x       y       z
#>   <dttm>                    <dbl>   <dbl>   <dbl>
#> 1 2013-02-19 08:00:04.289  0.0625 -0.0469 -0.922 
#> 2 2013-02-19 08:00:04.299 -0.969  -0.0469  0.0469
#> 3 2013-02-19 08:00:04.310 -1.02   -0.0469  0.0469
#> 4 2013-02-19 08:00:04.319 -1.03   -0.0469  0.0469
#> 5 2013-02-19 08:00:04.329 -1.03   -0.0469  0.0469
#> 6 2013-02-19 08:00:04.339 -1.03   -0.0469  0.0469
head(r_data)
#> # A tibble: 6 × 4
#>   time                          x       y       z
#>   <dttm>                    <dbl>   <dbl>   <dbl>
#> 1 2013-02-19 08:00:04.289  0.0625 -0.0469 -0.922 
#> 2 2013-02-19 08:00:04.299 -0.969  -0.0469  0.0469
#> 3 2013-02-19 08:00:04.309 -1.02   -0.0469  0.0469
#> 4 2013-02-19 08:00:04.319 -1.03   -0.0469  0.0469
#> 5 2013-02-19 08:00:04.329 -1.03   -0.0469  0.0469
#> 6 2013-02-19 08:00:04.339 -1.03   -0.0469  0.0462

And look quite different for the OMGUI output

head(om_gui_output |> filter(time >= ymd_hms("2013-02-19 08:00:04.280", tz = Sys.timezone())))
#> # A tibble: 6 × 4
#>   time                         x       y       z
#>   <dttm>                   <dbl>   <dbl>   <dbl>
#> 1 2013-02-19 08:00:04.289  0.207 -0.0586 -1.07  
#> 2 2013-02-19 08:00:04.299 -0.802 -0.0586 -0.0959
#> 3 2013-02-19 08:00:04.309 -0.848 -0.0586 -0.0959
#> 4 2013-02-19 08:00:04.319 -0.863 -0.0586 -0.0959
#> 5 2013-02-19 08:00:04.328 -0.863 -0.0586 -0.0959
#> 6 2013-02-19 08:00:04.338 -0.863 -0.0586 -0.0959

Just showing the head for the individual minutes inferring (not comparison)

head(om_gui_output)
#> # A tibble: 6 × 4
#>   time                        x       y      z
#>   <dttm>                  <dbl>   <dbl>  <dbl>
#> 1 2013-02-19 08:00:00.000 0.146 -0.0117 -0.143
#> 2 2013-02-19 08:00:00.009 0.146 -0.0117 -0.143
#> 3 2013-02-19 08:00:00.019 0.146 -0.0117 -0.143
#> 4 2013-02-19 08:00:00.029 0.146 -0.0117 -0.143
#> 5 2013-02-19 08:00:00.039 0.146 -0.0117 -0.143
#> 6 2013-02-19 08:00:00.049 0.146 -0.0117 -0.143

Again we see the tail to be cut off for OMGUI for some reason

tail(data)
#> # A tibble: 6 × 4
#>   time                          x       y      z
#>   <dttm>                    <dbl>   <dbl>  <dbl>
#> 1 2013-02-26 06:58:28.110 -0.0156 -0.0156 -0.984
#> 2 2013-02-26 06:58:28.119 -0.0156 -0.0156 -0.984
#> 3 2013-02-26 06:58:28.129 -0.0156 -0.0156 -0.984
#> 4 2013-02-26 06:58:28.140 -0.0156 -0.0156 -0.969
#> 5 2013-02-26 06:58:28.150 -0.0156 -0.0312 -0.969
#> 6 2013-02-26 06:58:28.160 -0.0156 -0.0312 -0.984
tail(raw_data)
#> # A tibble: 6 × 4
#>   time                          x       y      z
#>   <dttm>                    <dbl>   <dbl>  <dbl>
#> 1 2013-02-26 06:58:28.110 -0.0156 -0.0156 -0.984
#> 2 2013-02-26 06:58:28.119 -0.0156 -0.0156 -0.984
#> 3 2013-02-26 06:58:28.129 -0.0156 -0.0156 -0.984
#> 4 2013-02-26 06:58:28.140 -0.0156 -0.0156 -0.969
#> 5 2013-02-26 06:58:28.150 -0.0156 -0.0312 -0.969
#> 6 2013-02-26 06:58:28.160 -0.0156 -0.0312 -0.984
tail(r_data)
#> # A tibble: 6 × 4
#>   time                          x       y      z
#>   <dttm>                    <dbl>   <dbl>  <dbl>
#> 1 2013-02-26 06:58:28.089 -0.0156 -0.0156 -0.977
#> 2 2013-02-26 06:58:28.099 -0.0156 -0.0156 -0.969
#> 3 2013-02-26 06:58:28.109 -0.0156 -0.0231 -0.969
#> 4 2013-02-26 06:58:28.119 -0.0156 -0.0310 -0.969
#> 5 2013-02-26 06:58:28.129 -0.0156 -0.0312 -0.976
#> 6 2013-02-26 06:58:28.139 -0.0156 -0.0312 -0.984
tail(om_gui_output)
#> # A tibble: 6 × 4
#>   time                         x      y         z
#>   <dttm>                   <dbl>  <dbl>     <dbl>
#> 1 2013-02-26 06:12:20.720 0.0120 0.0330 -0.000488
#> 2 2013-02-26 06:12:20.730 0.0120 0.0330 -0.000488
#> 3 2013-02-26 06:12:20.740 0.0120 0.0330 -0.000488
#> 4 2013-02-26 06:12:20.750 0.0120 0.0330 -0.000488
#> 5 2013-02-26 06:12:20.759 0.0120 0.0330 -0.000488
#> 6 2013-02-26 06:12:20.769 0.0120 0.0330 -0.000488

And the values to be quite different for the same timeframe

end_time = max(om_gui_output$time)
tail(data |> filter(time <= end_time))
#> # A tibble: 6 × 4
#>   time                        x     y      z
#>   <dttm>                  <dbl> <dbl>  <dbl>
#> 1 2013-02-26 06:12:20.710 0.969 0.188 -0.125
#> 2 2013-02-26 06:12:20.720 0.969 0.188 -0.141
#> 3 2013-02-26 06:12:20.729 0.969 0.188 -0.125
#> 4 2013-02-26 06:12:20.740 0.984 0.188 -0.141
#> 5 2013-02-26 06:12:20.750 0.969 0.188 -0.125
#> 6 2013-02-26 06:12:20.759 0.984 0.188 -0.141
tail(raw_data |> filter(time <= end_time))
#> # A tibble: 6 × 4
#>   time                        x     y      z
#>   <dttm>                  <dbl> <dbl>  <dbl>
#> 1 2013-02-26 06:12:20.717 0.969 0.188 -0.141
#> 2 2013-02-26 06:12:20.726 0.969 0.188 -0.125
#> 3 2013-02-26 06:12:20.736 0.984 0.188 -0.141
#> 4 2013-02-26 06:12:20.746 0.969 0.188 -0.125
#> 5 2013-02-26 06:12:20.756 0.984 0.188 -0.141
#> 6 2013-02-26 06:12:20.766 0.984 0.188 -0.125
tail(r_data |> filter(time <= end_time))
#> # A tibble: 6 × 4
#>   time                        x     y      z
#>   <dttm>                  <dbl> <dbl>  <dbl>
#> 1 2013-02-26 06:12:20.710 0.969 0.188 -0.134
#> 2 2013-02-26 06:12:20.720 0.976 0.188 -0.132
#> 3 2013-02-26 06:12:20.730 0.977 0.188 -0.133
#> 4 2013-02-26 06:12:20.740 0.976 0.188 -0.132
#> 5 2013-02-26 06:12:20.750 0.984 0.188 -0.133
#> 6 2013-02-26 06:12:20.759 0.976 0.188 -0.125
tail(om_gui_output)
#> # A tibble: 6 × 4
#>   time                         x      y         z
#>   <dttm>                   <dbl>  <dbl>     <dbl>
#> 1 2013-02-26 06:12:20.720 0.0120 0.0330 -0.000488
#> 2 2013-02-26 06:12:20.730 0.0120 0.0330 -0.000488
#> 3 2013-02-26 06:12:20.740 0.0120 0.0330 -0.000488
#> 4 2013-02-26 06:12:20.750 0.0120 0.0330 -0.000488
#> 5 2013-02-26 06:12:20.759 0.0120 0.0330 -0.000488
#> 6 2013-02-26 06:12:20.769 0.0120 0.0330 -0.000488

Confirming that the data is not simply static after that point

data |> filter(time > end_time) |> ggplot(aes(x = time, y = x)) + geom_step()

Another random time check

time_start = ymd_hms("2013-02-19 08:00:41.230", tz = Sys.timezone())
time_end = ymd_hms("2013-02-19 08:00:41.300", tz = Sys.timezone())
(sub_data = data |> filter(between(time, time_start, time_end)) |> select(time, x,y,z))
#> # A tibble: 8 × 4
#>   time                         x       y      z
#>   <dttm>                   <dbl>   <dbl>  <dbl>
#> 1 2013-02-19 08:00:41.230 -0.703 -0.109  -0.359
#> 2 2013-02-19 08:00:41.240 -0.703 -0.0938 -0.406
#> 3 2013-02-19 08:00:41.249 -0.734 -0.0625 -0.5  
#> 4 2013-02-19 08:00:41.259 -0.781 -0.0156 -0.562
#> 5 2013-02-19 08:00:41.270 -0.828  0.0469 -0.484
#> 6 2013-02-19 08:00:41.279 -0.875  0.0781 -0.422
#> 7 2013-02-19 08:00:41.289 -0.953  0.109  -0.344
#> 8 2013-02-19 08:00:41.299 -0.969  0.109  -0.281
(sub_raw_data = raw_data |> filter(between(time, time_start, time_end)) |> select(time, x,y,z))
#> # A tibble: 8 × 4
#>   time                         x       y      z
#>   <dttm>                   <dbl>   <dbl>  <dbl>
#> 1 2013-02-19 08:00:41.230 -0.703 -0.109  -0.359
#> 2 2013-02-19 08:00:41.240 -0.703 -0.0938 -0.406
#> 3 2013-02-19 08:00:41.249 -0.734 -0.0625 -0.5  
#> 4 2013-02-19 08:00:41.259 -0.781 -0.0156 -0.562
#> 5 2013-02-19 08:00:41.270 -0.828  0.0469 -0.484
#> 6 2013-02-19 08:00:41.279 -0.875  0.0781 -0.422
#> 7 2013-02-19 08:00:41.289 -0.953  0.109  -0.344
#> 8 2013-02-19 08:00:41.299 -0.969  0.109  -0.281
(sub_r_data = r_data |> filter(between(time, time_start, time_end)) |> select(time, x,y,z))
#> # A tibble: 8 × 4
#>   time                         x       y      z
#>   <dttm>                   <dbl>   <dbl>  <dbl>
#> 1 2013-02-19 08:00:41.230 -0.703 -0.0945 -0.404
#> 2 2013-02-19 08:00:41.240 -0.733 -0.0638 -0.496
#> 3 2013-02-19 08:00:41.250 -0.780 -0.0172 -0.560
#> 4 2013-02-19 08:00:41.259 -0.827  0.0453 -0.486
#> 5 2013-02-19 08:00:41.269 -0.874  0.0776 -0.423
#> 6 2013-02-19 08:00:41.279 -0.952  0.109  -0.344
#> 7 2013-02-19 08:00:41.289 -0.969  0.109  -0.281
#> 8 2013-02-19 08:00:41.299 -0.922  0.0781 -0.281
(sub_om_gui_output = om_gui_output |> filter(between(time, time_start, time_end)) |> select(time, x,y,z))
#> # A tibble: 8 × 4
#>   time                         x       y      z
#>   <dttm>                   <dbl>   <dbl>  <dbl>
#> 1 2013-02-19 08:00:41.230 -0.678 -0.0742 -0.371
#> 2 2013-02-19 08:00:41.240 -0.686 -0.0513 -0.432
#> 3 2013-02-19 08:00:41.250 -0.725 -0.0144 -0.533
#> 4 2013-02-19 08:00:41.259 -0.773  0.0405 -0.555
#> 5 2013-02-19 08:00:41.269 -0.818  0.0974 -0.460
#> 6 2013-02-19 08:00:41.279 -0.876  0.126  -0.393
#> 7 2013-02-19 08:00:41.289 -0.944  0.151  -0.312
#> 8 2013-02-19 08:00:41.299 -0.932  0.139  -0.268

Direct Data Comparison

sub_data - sub_r_data
#>                 time           x           y           z
#> 1  0.000000e+00 secs  0.00000000 -0.01483725  0.04451175
#> 2  0.000000e+00 secs  0.02993743 -0.02993743  0.08981229
#> 3 -2.384186e-07 secs  0.04529950 -0.04529950  0.06039934
#> 4  0.000000e+00 secs  0.04569391 -0.06092522 -0.07615652
#> 5  2.384186e-07 secs  0.04608725 -0.03072483 -0.06144967
#> 6  0.000000e+00 secs  0.07746947 -0.03098779 -0.07746947
#> 7  0.000000e+00 secs  0.01562500  0.00000000 -0.06250000
#> 8  0.000000e+00 secs -0.04687500  0.03125000  0.00000000
sub_data - sub_om_gui_output
#>                 time         x         y         z
#> 1  0.000000e+00 secs -0.025391 -0.035156  0.011963
#> 2  0.000000e+00 secs -0.016846 -0.042480  0.025391
#> 3 -2.384186e-07 secs -0.009277 -0.048096  0.032715
#> 4  9.999275e-04 secs -0.007812 -0.056152 -0.007812
#> 5  1.000166e-03 secs -0.010010 -0.050537 -0.024658
#> 6  9.999275e-04 secs  0.001221 -0.048096 -0.028564
#> 7  9.999275e-04 secs -0.009277 -0.041260 -0.031494
#> 8  9.999275e-04 secs -0.036621 -0.029541 -0.013672
sub_r_data - sub_om_gui_output
#>                time           x            y           z
#> 1 0.0000000000 secs -0.02539100 -0.020318749 -0.03254875
#> 2 0.0000000000 secs -0.04678343 -0.012542571 -0.06442129
#> 3 0.0000000000 secs -0.05457650 -0.002796498 -0.02768434
#> 4 0.0009999275 secs -0.05350591  0.004773216  0.06834452
#> 5 0.0009999275 secs -0.05609725 -0.019812166  0.03679167
#> 6 0.0009999275 secs -0.07624847 -0.017108214  0.04890547
#> 7 0.0009999275 secs -0.02490200 -0.041260000  0.03100600
#> 8 0.0009999275 secs  0.01025400 -0.060791000 -0.01367200

During this time, no resampling was done, so same as non-resampled data

sub_data - sub_raw_data
#>     time x y z
#> 1 0 secs 0 0 0
#> 2 0 secs 0 0 0
#> 3 0 secs 0 0 0
#> 4 0 secs 0 0 0
#> 5 0 secs 0 0 0
#> 6 0 secs 0 0 0
#> 7 0 secs 0 0 0
#> 8 0 secs 0 0 0

Conclusion

OMGUI resampling has it’s own odd issues

df = reticulate::py_list_packages()
df$version[df$package == "actipy"]
#> [1] "3.8.0"

^{Created on 2026-06-25 with reprex v2.1.1}

Session info

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[vincentvanhees]

In terms of trusting the code. @l-k- and I independently spent a lot of time on it in 2023/2024 as can be seen from the commit history. No guarantee for correctness, but I would be surprised if really major bugs were now discovered.

I do not have all the notes by hand, but do remember that we primarily focussed on the .cwa format documentation as our guide when reviewing the code.

I just had a quick look at the same example file you used above. To me it is most informative to zoom in on a tiny fraction of the data and inspect whether exact patterns align. I see a 1 sample time shift between OMGUI csv and readAxivity. When I adjust for this by shifting the timestamp column 1 sample backward I get (for samples 5000 to 5600):

Next, I noticed that only 98 out of these 600 timestamps are identical, indicating that OMGUI somehow interpolates to slightly different times compared with readAxivity. If yes, then that may then also explain different values in acceleration signal because of the resampling process.

The resampling function is covered by a unit-test which to me captures the essence of what it needs to do. Comparing this with actipy code for interpolation may help to exclude this as possible source of the observed differences.

Researchers have used this version of readAxivity on UKBiobank data in several publications and no suspicious patterns in endpoints were observed. Further, visual inspection of the signals as the one I shared above did not reveal anything concerning other than some doubts about the correctness of OMGUI csv exports. So, I see no reason to doubt the correctness of readAxivity.