Detect Compensatory Activation Patterns

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

1. Map EMG channels to MSK muscles via emgToMSKMapping

2. Compute RMS activation for both current and baseline

3. Compute z-score of change: z = (current_rms - baseline_rms) / baseline_sd

4. Identify MSK neighbors of injured muscles within neighborhood_order hops

5. Flag muscles where they are neighbors AND z-score > z_threshold

6. Also detect decreased activation in injured muscles (z < -z_threshold)

Examples

C <- matrix(c(1,1,0,0,0, 0,1,1,0,0, 0,0,1,1,0, 0,0,0,1,1, 1,0,0,0,1, 0,1,0,1,0),
            nrow = 6, byrow = TRUE,
            dimnames = list(paste0("bone", 1:6),
              c("Biceps Brachii", "Deltoid", "Trapezius",
                "Triceps Brachii", "Brachialis")))
hg <- MSKHypergraph(C)
set.seed(1); emg_base <- matrix(abs(rnorm(300 * 4)), 300, 4)
set.seed(2); emg <- matrix(abs(rnorm(300 * 4)), 300, 4)
colnames(emg) <- colnames(emg_base) <-
  c("Biceps Brachii", "Deltoid", "Trapezius", "Triceps Brachii")
result <- mskDetectCompensation(emg, emg_base, "Biceps Brachii", hg = hg)
print(result)
#> MSK Compensatory Movement Detection
#> ====================================
#> Injured muscles: Biceps Brachii 
#> Neighborhood order: 2 
#> Z-score threshold: 1.96 
#> 
#> No compensating muscles detected.
#> 
#> Injured muscle status:
#>   Biceps Brachii: z=0.18 (unchanged)
#> 
#> Compensation prevalence: 0.0%