Computes risk scores for compensating muscles based on biomechanical overuse principles, integrating network topology, duration of compensation, and loading intensity.
Usage
mskCompensationRiskScore(
compensation_result,
hg = NULL,
duration_weeks = 0,
load_intensity = c("low", "moderate", "high")
)Arguments
- compensation_result
An
"MSKCompensation"object frommskDetectCompensation().- hg
An MSKHypergraph object (NULL loads default).
- duration_weeks
Numeric, how long compensation has been occurring.
- load_intensity
Character, one of "low", "moderate", "high".
Value
An S3 object of class "MSKCompensationRisk" with:
- per_muscle_risk
Data.frame of per-muscle risk scores
- overall_risk
Numeric overall risk score
- overall_category
Character risk category
- highest_risk_muscle
Name of highest-risk muscle
- recommendation
Clinical action recommendation
Risk Model
Per compensating muscle: risk = z_excess * degree_normalized * duration_factor * load_factor where z_excess = max(0, z_score - z_threshold), degree_normalized = hyperedgeDegree / mean_degree, duration_factor = 1 + log(1 + duration_weeks), load_factor = intensity multiplier (0.5, 1.0, 1.5).
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)
risk <- mskCompensationRiskScore(result, hg = hg,
duration_weeks = 4, load_intensity = "moderate")
risk
#> MSK Compensation Risk Assessment
#> =================================
#> Overall risk: 0.000 (LOW)
#>
#> Recommendation: No compensating muscles detected. Continue monitoring.