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. 2020 Apr 18;20(8):2322. doi: 10.3390/s20082322
a Sum vectored, zero mean acceleration signal vector
a′ Sum vectored, acceleration signal vector
ax Acceleration signal vector along x-axis
ay Acceleration signal vector along y-axis
az Acceleration signal vector along z-axis
a(n) Acceleration signal at sample n
Ai Wavelet approximation coefficients at level i
β Signal spectral exponent
d Fractional difference parameter
d˜ Fractional integration coefficient
dc Drift constant in stationary test
Di Wavelet detail coefficients at level i
e(n) Innovation process in stationary test
ϵ(n) Random variable
fs Sampling frequency
fd Fractal dimension
γ Autoregressive coefficient with value < 1
Γ Gamma Function
H Hurst exponent
H0 Null hypothesis in stationary test
H1 Alternative hypothesis in stationary test
i Wavelet transform level
I(d) Integrated part of ARIMA/ARFIMA model
k Shift/translation index
L Lag operator
λ Deterministic trend with coefficient
μa Mean of sum vectored, zero mean acceleration signal vector
μa′ Mean of sum vectored acceleration signal vector
μDi Mean of wavelet detail coefficients at level i
μs Mean of accelerometer first order difference signal
n Signal sample number
N Total number of samples
ω Angular frequency
p-value Confidence value for unit root tests
pmax,ADF Maximum delay value for ADF test
pmax,KPSS Maximum delay value for KPSS test
ϕi,k(n) Scaling wavelet function with shift index k, at level i
ψi,k(n) Mother wavelet function with shift index k, at level i
q Number of random perturbations
r Number of autoregressive terms
s(n) First order difference, accelerometer signal
S(ω) Power spectrum density
σa2 Variance of sum vectored, zero mean acceleration signal vector
σa′2 Variance of sum vectored acceleration signal vector
σDi2 Variance of wavelet detail coefficients at level i
θp General moving average coefficients
v1(n) Stationary process
v2(n) Independent and identically distributed process with 0 mean
ζr General autoregressive coefficients