Detect Compensatory Activation Patterns — mskDetectCompensation • PhysioMSKNet

Detects compensatory movement patterns by comparing current EMG activation against a baseline, using MSK network topology to identify muscles that are structurally positioned to compensate for injured muscles.

Usage

mskDetectCompensation(
  emg,
  emg_baseline,
  injured_muscles,
  hg = NULL,
  emg_mapping = NULL,
  sr = NULL,
  z_threshold = 1.96,
  neighborhood_order = 2L
)

Arguments

emg: Current EMG data (matrix, SummarizedExperiment, or numeric vector).
emg_baseline: Baseline/pre-injury EMG data (same format as emg).
injured_muscles: Character vector of injured muscle names or integer indices.
hg: An MSKHypergraph object (NULL loads default).
emg_mapping: Optional pre-computed data.frame from emgToMSKMapping().
sr: Optional sampling rate override.
z_threshold: Numeric, z-score threshold for flagging compensation (default: 1.96).
neighborhood_order: Integer, MSK graph distance to search for compensators (default: 2).

Value

An S3 object of class "MSKCompensation" with:

compensating_muscles: Data.frame of muscles showing compensatory activation
injured_status: Data.frame of injured muscle activation status
non_compensating: Data.frame of neighbors that did NOT compensate
compensation_prevalence: Proportion of neighbors showing compensation
network_context: List with neighborhood info

Algorithm

Map EMG channels to MSK muscles via emgToMSKMapping
Compute RMS activation for both current and baseline
Compute z-score of change: z = (current_rms - baseline_rms) / baseline_sd
Identify MSK neighbors of injured muscles within neighborhood_order hops
Flag muscles where they are neighbors AND z-score > z_threshold
Also detect decreased activation in injured muscles (z < -z_threshold)

Examples

if (FALSE) { # \dontrun{
result <- mskDetectCompensation(
  emg = emg_current, emg_baseline = emg_pre,
  injured_muscles = c("Biceps Brachii"),
  z_threshold = 1.96
)
print(result)
} # }