Motion segmentation and identification methods tend to assume that joint angle data are readily available for processing. The standard data acquisition method, motion capture, is not feasible for real-world deployment, such as in physical rehabilitation clinics, due to space, environment and cost constraints. In this paper, a feature-guided Hidden Markov model segmentation algorithm is adapted to segment and identify human motion from acceleration and gyroscopic data instead of position data. Acceleration and gyroscopic data are readily obtainable through small lightweight sensors, and would serve as a more appropriate sensor system for a rehabilitation clinic than a motion capture system.