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Local dynamic stability (short- and long-term divergence)

Source: R/stability-nonlinear.R
localDynamicStability.Rd

Local dynamic stability quantifies how a system responds to small natural perturbations, as the exponential divergence rate of neighbouring trajectories over the short term (0-1 stride, lambda_short) and long term (4-10 strides, lambda_long), following Dingwell & Cusumano (2000). Higher divergence means less stable locomotion.

Usage

localDynamicStability(
  x,
  stride_samples,
  delay = NULL,
  dim = NULL,
  sampling_rate = 1
)

Arguments

x

Numeric time series (e.g. trunk acceleration or a joint angle).

stride_samples

Samples per stride (sets the divergence windows).

delay, dim

Embedding parameters; NULL estimates them.

sampling_rate

Sampling rate in Hz.

Value

A local_dynamic_stability object with lambda_short, lambda_long (per stride), the divergence curve, and embedding info.

References

Dingwell JB, Cusumano JJ (2000). Chaos 10(4):848-863.

See also

maxLyapunovExponent()