Automatic Detection of Deviations in Human Movements Using HMM: Discrete vs Continuous

Automatic detection of correct performance of movements in humans is the core of coaching and rehabilitation applications. Human movement can be studied in terms of sequential data by using different sensor technologies. This representation makes it possible to use models that use sequential data to determine if executions of a certain activity are close enough to the specification or if they must be considered to be erroneous. One of the most widely used approaches for characterization of sequential data are Hidden Markov Models (HMM). They have the advantage of being able to model processes based on data from noisy sources. In this work we explore the use of both discrete and continuous HMMs to label movement sequences as either according to a specification or deviated from it. The results show that the majority of sequences are correctly labeled by the technique, with an advantage for continuous HMM.