Algorithm 1 CAAB approach
1:	Input: Activity performance features
2:	Output: Statistical activity features
3:	Initialize: Feature vector
4:	//T1 and T2 are two consecutive clinical testing time points
5:	Given: T1,T2
6:	Given: skip size = 1
7:	while T1 < (T2 − W) do
8:	for each activity performance feature do:
9:	Place a window of size W at T1.
10:	Remove missing observations and detrend based on the observations that fall into this window.
11:	Calculate the variance, autocorrelation, skewness, kurtosis and change features (Algorithm 2) using the observations in the window.
12:	Append these values to the feature vector.
13:	T2 = T1 + skip size
14:	end foreach
15:	end while
16:	return average(Feature matrix)