Fast and accurate motion segmentation and identification methods are required to enable real-time assessment and feedback for physical rehabilitation. Exercise motions exhibit cyclic patterns that can be characterized by simple features, such as zero-velocity crossings or velocity peaks. In this paper, these features are used as framing windows for simultaneous motion segmentation and identification via Hidden Markov models. Comparisons to other segmentation methods show that feature guiding increases the segmentation accuracy and greatly reduces the runtime needed to perform the segmentation.