An Efficient Automatic Gait Anomaly Detection Method Based on Semisupervised Clustering

Algorithm 4

The pseudocodes of the gait classification.

	Algorithm: GAIT CLASSIFICATION
	Input: (1) Disjoint λ sets of gait feature vectors with preassigned class labels
	(2) Gait feature vector set of the individuals involved in the previous run
	(3) The gait features vector of the current detecting individuals
	Output: the class labels of .
	Method:
	1. Cluster the with BC-COP-K-means algorithm with K = 2
	2. If is assigned to the cluster with fewer members
	Classify to the group of individuals with abnormal gaits.
	Else
	Classify to the group of individuals with normal gaits.