Algorithm 2 Calculation of change feature
1:	Input: Activity performance features
2:	Initialize: CH = [ ]
3:	//T1 and T2 are two consecutive clinical testing time points
4:	Given: T1,T2
5:	Given: skip size = 1
6:	W = window size
7:	while do T1 < (T2 − W):
8:	for each activity performance feature do:
9:	Place window of size W at T1.
10:	Remove missing values that fall into this window.
11:	Put first half of W in the group A and second half in the group B.
12:	//Returns True or False.
13:	change = Hotelling T-test (A,B)
14:	append(CH, change)
15:	T1 = T1 + skip size
16:	end foreach
17:	end while
18:	return average(CH)