accelerometer | R Documentation |
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.
accelerometer
A tibble with 12245 observations of 25 variables.
ID of participant, from 1-22.
Name of the machine that the participant was using. Levels are "LEG PRESS" "LEG EXTENSION" "LEG CURL" "ABDUCTOR" ADDUCTOR" "LOWER BACK" "TOTAL ABDOMINAL" "VERTICAL TRACTION" and "CHEST PRESS"
Participants did two sets of each machine. This is numeric 1-2.
Participants repeated each exercise 10 times in a set. This variable describes whether the observation is related to concentric contraction, eccocentric contraction, contraction for a single rep, and total time-under-tension
Length of time in ms that the raters estimated the contraction to last from video.
Estimated time (ms) contraction lasted as computed by one of two smartphones.
Mean of two video timings.
Mean of two smartphone timings.
abs(smartphones_mean - video_rater_mean)/video_rater_mean
Difference between video an smartphone estimate.
Mean of video and smartphone means.
One of Con, Ecc, Rep or TuT. Redundant given contraction_mode
Difference of video raters/smartphones in ms
Hampel outliers
Demographic data of participants
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."
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235156
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/R3ZKYH
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