Algorithm 2.

Empirical Distribution(Σ₁, Σ₂, N_P)

1:	// Build empirical distribution Dîst of the test statistic.
2:	Σ1, Σ2 = two sets of activity curves
3:	Np = number of permutations
4:	initialize Dîst as Np × m matrix	▷ m is # activity distributions in the activity curves
5:	initialize i = 0
6:	while i < Np do :
7:	Shuffle the activity curves.
8:	Generate aggregated activity curves CΣ1 and CΣ2 by aggregating the distributions in Σ1, Σ2
9:	Using the time interval-based alignment technique, align the two aggregated activity curves to obtain an alignment vector Γ
10:	for all alignment pairs (u, u) in Γ do :
11:	Find a distance SDKL(D1,u‖D2,u) between uth activity distributions in two activity curves.
12:	Insert SDKL (D1,u‖D2,u) to empirical distribution Dîst at location [i, u].
13:	end for
14:	i = i+1
15:	end while
16:	return Dîst