IMU-based Impact Prediction

Computes bone stress from IMU acceleration/angular velocity data and propagates it through the MSK incidence matrix to predict muscle vulnerability.

Usage

imuImpactPrediction(
  imu_data,
  hg = NULL,
  mapping = NULL,
  stress_metric = c("acceleration", "angular_velocity", "jerk", "composite"),
  use_proxy = TRUE
)

Arguments

imu_data

A named list of per-sensor data, or a matrix (see imuNetworkKinematics for formats).

hg

An MSKHypergraph object (NULL loads default).

mapping

Pre-computed mapping (optional).

stress_metric

Character: "acceleration" (peak linear acceleration), "angular_velocity" (peak angular velocity), "jerk" (peak rate of change of acceleration), "composite" (weighted combination of acceleration + angular velocity).

use_proxy

Logical, use degree-based proxy for impact deviation (faster) or run full simulation.

Value

A list with:

vulnerability

Named numeric vector of muscle vulnerability scores

bone_stress

Named numeric vector of bone stress

muscle_stress_exposure

Named numeric of stress propagated to muscles

ranking

Data frame ranked by vulnerability (descending)

Examples

set.seed(1)
n <- 200
imu <- list(
  thigh = matrix(rnorm(n * 3), n, 3),
  shank = matrix(rnorm(n * 3), n, 3),
  foot  = matrix(rnorm(n * 3), n, 3))
pred <- imuImpactPrediction(imu, stress_metric = "acceleration")
head(pred$ranking)
#>                      muscle vulnerability stress_exposure impact_deviation
#> 1        Tibialis Posterior      8.346843        3.665007        2.2774425
#> 2 Extensor Digitorum Longus      8.346843        3.665007        2.2774425
#> 3               Psoas Major      4.039158        4.494336        0.8987217
#> 4         Tibialis Anterior      3.293821        3.665007        0.8987217
#> 5           Gluteus Maximus      2.799871        4.494336        0.6229775
#> 6   Flexor Digitorum Longus      2.283217        3.665007        0.6229775