IMU-based Network Kinematics Analysis

Computes pairwise kinematic coupling from IMU orientation or acceleration data and compares it to the structural adjacency of the MSK bone graph using a Mantel test.

Usage

imuNetworkKinematics(
  imu_data,
  hg = NULL,
  mapping = NULL,
  signal = c("orientation", "acceleration"),
  method = c("correlation", "mutual_info")
)

Arguments

imu_data

A named list of per-sensor data. Each element should be a matrix or data.frame with orientation/acceleration columns. The list names are sensor placement names (e.g., "upper_arm", "thigh"). Alternatively, a single matrix where columns are named "<sensor>_roll", "<sensor>_pitch", "<sensor>_yaw" or "<sensor>_ax", "<sensor>_ay", "<sensor>_az".

hg

An MSKHypergraph object (NULL loads default).

mapping

A pre-computed mapping from imuToMSKMapping() (optional).

signal

Character, which signal to use for coupling: "orientation" (Euler angle differences) or "acceleration" (acceleration magnitude correlation).

method

Character, coupling method: "correlation" or "mutual_info".

Value

A list with:

kinematic_coupling

Pairwise coupling matrix between mapped bones

structural_matrix

Corresponding MSK bone graph adjacency

correlation

Mantel test correlation coefficient

p_value

Permutation p-value

mapped_sensors

Data frame of sensor-to-bone mapping used

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),
  pelvis = matrix(rnorm(n * 3), n, 3))
res <- imuNetworkKinematics(imu, signal = "orientation")
res$mapped_sensors
#>   sensor_name bone_idx bone_name match_quality match_method
#> 1       thigh      121     Femur             1       lookup
#> 2       shank      125     Tibia             1       lookup
#> 3        foot      111    Tarsus             1       lookup
#> 4      pelvis      118     Ilium             1       lookup