library(readxl)
library(dplyr)
library(tidyr)
library(openxlsx)
library(janitor)
library(purrr)#CONFIGURATION: SET TARGET PARTICIPANT ID
PARTICIPANT_ID <- 2 Define directories
base_dir <- "/Users/vivi/UT/Thesis/Data/Xsens/Participants"
xsens_prep_dir <- file.path(base_dir, "Movement Preparation Period", paste0("ID", PARTICIPANT_ID))
eprime_clean_file <- file.path("/Users/vivi/UT/Thesis/Data/E-Prime", "rt_cleaned_merged.xlsx")
if (!dir.exists(xsens_prep_dir)) {
dir.create(xsens_prep_dir, recursive = TRUE)
message(paste("Created Xsens Preparation output directory:", xsens_prep_dir))
}Xsens Movement Preparation Preprocessing
message(paste("Processing movement preparation data for Participant ID:", PARTICIPANT_ID))
process_participant_prep <- function(pid) {
message(paste("Starting processing for Subject", pid, "..."))
# 1. Detect and filter files for the current participant
# Using recursive=TRUE to search the base_dir and all subdirectories
all_files <- list.files(base_dir, pattern = sprintf("ID%s-[0-9]+\\.xlsx$", pid),
recursive = TRUE, full.names = TRUE)
# Filter files to ensure we only get ID{pid}-{Block}.xlsx files
participant_files <- all_files[grepl(sprintf("/ID%s-[0-9]+\\.xlsx$", pid), all_files)]
# Extract block numbers from filenames
block_numbers <- as.numeric(str_extract(participant_files, "(?<=ID[0-9]-)[0-9]+"))
if (length(participant_files) == 0) {
warning(sprintf("No files found for Subject %s in %s. Skipping.", pid, base_dir))
return(NULL)
}
# 2. Read, Clean, and Combine Data from Separate Sheets
combined_data <- purrr::map2_dfr(
participant_files, block_numbers,
~{
# Read Center of Mass and Markers sheets and clean names (e.g., 'co_m_pos_x')
com <- read_excel(.x, sheet = "Center of Mass") %>% clean_names()
markers <- read_excel(.x, sheet = "Markers") %>% clean_names()
# Merge based on "frame" column
merged_df <- left_join(com, markers, by = "frame") %>%
mutate(Subject = as.integer(pid), Block = .y)
# Ensure marker column is named consistently
if (!"marker_text" %in% colnames(merged_df)) {
if ("ns1_marker" %in% colnames(merged_df)) {
merged_df <- merged_df %>% rename(marker_text = ns1_marker)
} else {
warning(sprintf("Marker column not found in Block %s.", .y))
return(NULL)
}
}
return(merged_df)
}
)
if (is.null(combined_data) || nrow(combined_data) == 0) {
message(sprintf("No valid data combined for Subject %s.", pid))
return(NULL)
}
# 3. Trial Numbering and Numeric Conversion
combined_data <- combined_data %>%
mutate(marker_text = as.numeric(trimws(as.character(marker_text))))
combined_data_numbered <- combined_data %>%
group_by(Subject, Block) %>%
mutate(
is_go = (abs(marker_text - 27) < 0.001),
Trial = as.integer(cumsum(replace_na(is_go, FALSE))),
# Add a simple row index *before* filtering to keep track of row order within the block
row_index = row_number()
) %>%
ungroup()
# 4. Filter to Movement PREPARATION Period (150 frames BEFORE GO)
# Find the global index of every GO marker (within the block/subject grouping)
go_indices_df <- combined_data_numbered %>%
filter(is_go) %>%
# Select the index *of the row that has the GO marker*
select(Subject, Block, Trial, go_index = row_index)
# Calculate the start and end row indices for the 150-frame window
prep_windows <- go_indices_df %>%
# Skip Trial 0 if it exists (though Trial should be >= 1 here)
filter(Trial > 0) %>%
mutate(
start_index = go_index - 201, # The row 150 frames *before* GO
end_index = go_index - 1 # The row immediately before GO
) %>%
# Use row_number() to generate a consistent trial index for the join later
select(Subject, Block, Trial, start_index, end_index)
# Now, join the main data back to these index windows and filter
prep_data <- purrr::map_dfr(1:nrow(prep_windows), ~ {
window <- prep_windows[.x, ]
combined_data_numbered %>%
# Filter to the specific index range for this trial
filter(
Subject == window$Subject,
Block == window$Block,
row_index > window$start_index,
row_index <= window$end_index
) %>%
# Add the trial number and block number from the window (crucial if data is missing)
mutate(
Trial = window$Trial,
Block = window$Block,
# Recalculate the row index within the 150-frame preparation period
row_in_trial = row_number()
)
})
# DEBUG CHECK: Ensure we have data
if (nrow(prep_data) == 0) {
message(sprintf("ERROR: Preparation period filter produced zero rows for Subject %s.", pid))
# Check if this error is because the start_index was <= 0
if (any(prep_windows$start_index <= 0)) {
message("NOTE: Found Trials where the GO marker occurs too early in the data block.")
}
return(NULL)
}
# # 5. Center-of-Mass Calculations (RMS is calculated per trial time series)
final_df <- prep_data %>%
# 5a. Calculate Time-Series Vector Magnitudes
mutate(
COM_pos = sqrt(co_m_pos_x^2 + co_m_pos_y^2 + co_m_pos_z^2),
COM_vel = sqrt(co_m_vel_x^2 + co_m_vel_y^2 + co_m_vel_z^2),
COM_acc = sqrt(co_m_acc_x^2 + co_m_acc_y^2 + co_m_acc_z^2)
) %>%
group_by(Subject, Block, Trial) %>%
mutate(
# 5b. Calculate Trial-Level RMS Summary (RMS values are repeated for all rows in the trial)
RMS_acc = sqrt(mean(COM_acc^2, na.rm = TRUE)),
RMS_vel = sqrt(mean(COM_vel^2, na.rm = TRUE)),
RMS_pos = sqrt(mean(COM_pos^2, na.rm = TRUE)),
.groups = 'drop'
) %>%
# Select final columns, including the new magnitude columns
select(
Subject, Block, Trial, row_in_trial, time_code = "frame", Marker = marker_text,
co_m_acc_x, co_m_acc_y, co_m_acc_z, # Keep component columns for Lyapunov
COM_pos, COM_vel, COM_acc,
RMS_acc, RMS_vel, RMS_pos # Keep the trial-level RMS columns
)
# 6. Write to Files
# File 1: Time Series Data (Movement Preparation Period)
out_file_ts <- file.path(xsens_prep_dir, sprintf("ID%s_preparation_timeseries.xlsx", pid))
write.xlsx(final_df, out_file_ts)
message(paste("Successfully wrote time series data to:", out_file_ts))
message(sprintf("Successfully processed and saved all files for Subject %s.", pid))
return(final_df) # Return the processed data frame
}
# Execution
processed_data <- process_participant_prep(PARTICIPANT_ID)Xsens and E-Prime Merge
message(paste("Starting merge for Participant ID:", PARTICIPANT_ID))
# 1. Grab the preparation-period time-series file
preparation_list <- list.files(
path = xsens_prep_dir,
pattern = sprintf("^ID%s_preparation_timeseries\\.xlsx$", PARTICIPANT_ID),
full.names = TRUE
)
if (length(preparation_list) == 0) {
stop(sprintf("Preparation time-series file for ID %s not found.", PARTICIPANT_ID, xsens_prep_dir))
}
# Read and format the Xsens time-series data
preparation_df <- read_excel(preparation_list[1]) %>%
mutate(
Subject = as.factor(Subject),
Block = factor(Block, levels = as.character(1:5)),
Trial = factor(Trial, levels = as.character(1:48)),
time_code = as.character(time_code),
# Ensure all CoM columns are numeric
across(starts_with("COM_"), as.numeric),
# Ensure all RMS columns are numeric
across(starts_with("RMS_"), as.numeric)
)
# 2. Import and simplify RT data (E-Prime)
rt_data <- read_excel(eprime_clean_file) %>%
filter(subject == PARTICIPANT_ID) %>%
mutate(
# NOTE: Assuming the E-Prime file has columns 'subject', 'session', 'trial'
Subject = as.factor(subject),
Block = factor(session, levels = as.character(1:5)),
Trial = factor(trial, levels = as.character(1:48)),
Sequence = as.factor(sequence)
) %>%
# Keep one row per trial
distinct(Subject, Block, Trial, .keep_all = TRUE) %>%
# Select the required merge keys
select(Subject, Block, Trial, Sequence, trial.RTS)
# 3. Merge Xsens time-series with RT data
preparation_merged <- preparation_df %>%
left_join(rt_data, by = c("Subject", "Block", "Trial")) %>%
filter(!is.na(Sequence)) %>%
select(
Subject, Block, Trial, Sequence, row_in_trial, time_code, Marker,
co_m_acc_x, co_m_acc_y, co_m_acc_z, COM_acc,
RMS_acc, RMS_vel, RMS_pos, trial.RTS
)
# 4. Write out the merged file
out_file_merged <- file.path(xsens_prep_dir, sprintf("ID%s_preparation_merged.xlsx", PARTICIPANT_ID))
write.xlsx(preparation_merged, out_file_merged)
message(paste("Successfully merged Xsens preparation time-series with RT data."))
message(paste("File saved to:", out_file_merged))