Accelerometer#

One measure that is of interest when working out with weights is the length of time that muscles are engaged in the exercise. The gold standard for measuring this is via a video of the exercise that is evaluated by a human looking at it frame by frame. This data contains such information for participants doing several different common weight training exercises. The same lengths are computed via a novel way using a smartphone accelerometer, which was attached to the weights.

From the authors: “Single repetition, contraction-phase specific and total time-under-tension (TUT) are crucial mechano-biological descriptors associated with distinct morphological, molecular and metabolic muscular adaptations in response to exercise, rehabilitation and/or fighting sarcopenia. However, to date, no simple, reliable and valid method has been developed to measure these descriptors.”

Initialization#

library(fosdata)
data <- fosdata::accelerometer

Accessing fields#

data <- fosdata::accelerometer
difference_video_raters_ms <- data$difference_video_raters_ms # Just a random field in the dataset

Interactive R Sample#

You can use the R editor below to interactively explore the dataset and generate plots. This contains a fully self-contained R environment with fosdata, ggplot2, and dplyr loaded.

No plot generated yet.

LLM instructions#

If using an LLM, you can copy-paste the following instructions to accompany your prompt to inform the model of the fields and their types in the dataset.

The fosdata::accelerometer dataset containing the following fields:

fields[25]{name,type,values}:
  participant,numeric,n/a
  machine,character,n/a
  set,numeric,n/a
  contraction_mode,character,n/a
  time_video_rater_cv_ms,numeric,n/a
  time_video_rater_dg_ms,numeric,n/a
  time_smartphone_1_ms,numeric,n/a
  time_smartphone_2_ms,numeric,n/a
  video_rater_mean_ms,numeric,n/a
  smartphones_mean_ms,numeric,n/a
  relative_difference,numeric,n/a
  difference_video_smartphone_ms,numeric,n/a
  mean_video_smartphone_ms,numeric,n/a
  contraction_mode_levels,character,[Con,Ecc,Rep,TuT]
  difference_video_raters_ms,numeric,n/a
  difference_smartphones_ms,numeric,n/a
  video_smartphone_difference_outlier,logical,[FALSE,TRUE]
  rater_difference_outlier,logical,[FALSE,TRUE]
  smartphone_difference_outlier,logical,[FALSE,TRUE]
  normalized_error_smartphone,numeric,n/a
  participant_age_levels,character,[young,old]
  participant_age_years,numeric,n/a
  participant_height_cm,numeric,n/a
  participant_weight_kg,numeric,n/a
  participant_gender,character,[m,f]

Fields#

Source#

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235156 https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/R3ZKYH