Algorithm 1 Generate features for n datapoints of Class x.

Require:  $n,x,{\mu }_{time},{\sigma }_{time},{\mu }_{age},{\sigma }_{age},{\mu }_{bmi},{\sigma }_{bmi}$ Require:  ${\mu }_{weight},{\sigma }_{weight},{\mu }_{height},{\sigma }_{height},{\%}_{female}$  $time\leftarrow sort(X\sim \mathcal{N}({\mu }_{time},{\sigma }_{time}^2))$  $age\leftarrow sort(Y\sim \mathcal{N}({\mu }_{age},{\sigma }_{age}^2))$  $age\leftarrow insertVariability\left(Y\right)$  $bmi\leftarrow sort(Z\sim \mathcal{N}({\mu }_{bmi},{\sigma }_{bmi}^2))$  $bmi\leftarrow insertVariability\left(Z\right)$  $weight\leftarrow sort(W\sim \mathcal{N}({\mu }_{weight},{\sigma }_{weight}^2))$  $gender\leftarrow S_i=0\forall i\in \{0,n\ast {\%}_{female}\}\wedge S_i=1\forall i\in \{n\ast {\%}_{female}+1,n\}$  $\{bmi,weight,gender\}\leftarrow insertVariability\left(\right\{Z,W,S\left\}\right)$  $height\leftarrow H=\sqrt{\frac{W}{Z}}$ Ensure:  ${\mu }_{height}={\mu }_H \mathbf{and} {\sigma }_{height}={\sigma }_H$