Track Longitudinal MSK Metrics Across Timepoints

Computes per-timepoint RMS activation, synergy decomposition (W/H/VAF), and optionally CMC for a series of EMG measurements taken at different timepoints during rehabilitation.

Usage

mskLongitudinalTracker(
  timepoints,
  hg = NULL,
  emg_mapping = NULL,
  metrics = c("rms", "synergy"),
  sr = NULL
)

Arguments

timepoints

Named list of EMG matrices (time x channels). Names should be timepoint labels (e.g., "T0", "T1", "T2").

hg

An MSKHypergraph object (NULL loads default).

emg_mapping

Optional pre-computed data.frame from emgToMSKMapping().

metrics

Character vector of metrics to compute (default: c("rms", "synergy")). Options: "rms", "synergy", "mean_activation", "peak_activation".

sr

Optional sampling rate in Hz (overrides detected values).

Value

An S3 object of class "MSKLongitudinalTracker" with:

metrics_table

data.frame (timepoint, muscle, metric_name, value)

timepoint_labels

character vector

n_timepoints

integer

synergy_series

list of synergy results per timepoint

activation_series

matrix (n_muscles x n_timepoints)

Examples

if (FALSE) { # \dontrun{
emg_t0 <- matrix(abs(rnorm(400)), 100, 4)
emg_t1 <- matrix(abs(rnorm(400)), 100, 4)
colnames(emg_t0) <- colnames(emg_t1) <- paste0("muscle_", 1:4)
tracker <- mskLongitudinalTracker(list(T0 = emg_t0, T1 = emg_t1))
} # }