This paper develops an approach for on-line segmentation of whole body human motion patterns during human motion observation and learning. A Hidden Markov Model is used to represent the incoming data sequence, where each model state represents the probability density estimate over a window of the data. Based on the assumption that data belonging to the same motion primitive will have the same underlying distribution, the segmentation is implemented by finding the optimum state sequence over the developed model. The basic algorithm is modified to add the capability for modifying the model based on known motion primitives. The inclusion of such scaffolding motion primitives can improve the performance of the basic segmentation algorithm. The modified algorithm is tested on a corpus of continuous human motion data to show the efficacy of the proposed approach.