op_remove_keypoints: Remove Keypoints Based on Various Criteria

In duet: Analysing Non-Verbal Communication in Dyadic Interactions from Video Data

Remove Keypoints Based on Various Criteria

Description

This function removes keypoints and their corresponding columns based on several criteria: user-specified keypoints, low total confidence values over time, exceeding a threshold of missing/zero values, or if all data for a keypoint is missing (i.e., all zeros).

Usage

op_remove_keypoints(
  df,
  remove_specific_keypoints = NULL,
  remove_undetected_keypoints = FALSE,
  remove_keypoints_total_confidence = NULL,
  remove_keypoints_missing_data = NULL,
  apply_removal_equally = TRUE
)

Arguments

df	A data frame containing the data to process. Keypoint columns are expected to include x, y, and c (confidence) columns with corresponding indices.
remove_specific_keypoints	Character vector. Specifies the keypoint indices (e.g., "1") to remove. This will automatically remove corresponding x, y, and c columns for those indices. Default is NULL.
remove_undetected_keypoints	Logical. If TRUE, removes keypoints where all confidence values are zero across all rows. Default is FALSE.
remove_keypoints_total_confidence	Numeric or FALSE. A threshold for the mean confidence values. Keypoints with a mean confidence below this threshold will be removed. If set to FALSE, behaves as NULL. Default is NULL.
remove_keypoints_missing_data	Numeric or FALSE. A threshold (between 0 and 1) for the percentage of missing or zero values. Columns exceeding this threshold will be removed. If set to FALSE, behaves as NULL. Default is NULL.
apply_removal_equally	Logical. If TRUE, the same columns will be removed across all rows of the dataset. If FALSE, removal criteria are applied separately for each combination of person and region. Default is TRUE.

Value

A data frame with specified keypoints and corresponding columns removed.

Examples

# Load example data from the package
data_path <- system.file("extdata/csv_data/dyad_1/A_body.csv", package = "duet")
df <- read.csv(data_path)

# Remove keypoints based on various criteria
result <- op_remove_keypoints(
  df = df,
  remove_specific_keypoints = c("1", "2"), # Remove specific keypoints (e.g., keypoints 1 and 2)
  remove_undetected_keypoints = TRUE,      # Remove keypoints with all zero confidence
  remove_keypoints_total_confidence = 0.5, # Remove keypoints with mean confidence below 0.5
  remove_keypoints_missing_data = 0.2,     # Remove keypoints with >20% missing data
  apply_removal_equally = TRUE             # Apply removal equally across the dataset
)

# Display the result
print(result)

duet documentation built on April 3, 2025, 5:52 p.m.