Algorithm 1 Is Intoxicated?

1:$\mathbf{Input}:\mathit{Model}\text{—}\mathit{Intoxication\; Detection\; Model}$ 2:$\mathbf{Input}:\mathit{sSober}\text{—}\mathit{Gait\; Measurements\; while\; Sober}$ 3:$\mathbf{Input}:\mathit{sSuspect}\text{—}\mathit{Suspected\; Gait\; Measurements}$ 4:$\mathbf{Input}:\mathit{Threshold}\text{—}\mathit{Confidence\; threshold}$ 5:$\mathbf{Output}:\mathit{Boolean}\text{—}\mathit{True}/\mathit{False\; for\; intoxication}$ 6:procedure isIntoxicated? 7:    $\mathit{fSober}\left[\right]=\mathit{features}\left(\mathit{sSober}\right)$ 8:    $\mathit{fSuspect}\left[\right]=\mathit{features}\left(\mathit{sSuspect}\right)$ 9:    $\mathit{n}=\mathit{length}\left(\mathit{fSober}\right)$ 10:    $\mathit{difference}\left[\right]=\mathit{newarray}\left[\mathit{n}\right]$ 11:    for ($\mathit{i}=0;\mathit{i}<\mathit{n};\mathit{i}++)$ do 12:        $\mathit{difference}\left[\mathit{i}\right]=\mathit{fSuspect}\left[\mathit{i}\right]$−$fSober\left[i\right]$ 13:    $Probability=Model.classify\left(difference\right)$ 14:    $\mathbf{return}(Probability>Threshold)$