@article {Lin2012_TNSRE,
	title = {On-line Segmentation of Human Motion for Automated Rehabilitation Exercise Analysis},
	journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
	volume = {22},
	year = {2014},
	pages = {168{\textendash}180},
	abstract = {To enable automated analysis of rehabilitation movements, an approach for accurately identifying and segmenting movement repetitions is required. This paper proposes an approach for on-line, automated segmentation and identification of movement segments from continuous time-series data of human movement. The proposed approach uses a two stage identification and recognition process, based on velocity features and stochastic modeling of each motion to be identified. In the first stage, motion segment candidates are identified based on a characteristic sequence of velocity features such as velocity peaks and zero velocity crossings. In the second stage, Hidden Markov models are used to accurately identify segment locations from the identified candidates. The proposed approach is capable of on-line segmentation and identification, enabling interactive feedback in rehabilitation applications. The approach is validated on a 20 person rehabilitation movement dataset.},
	doi = {10.1109/TNSRE.2013.2259640},
	author = {Lin, J. F. S. and Kuli{\'c}, D.}
}
