  Automatic recognition of the quality of movement in human beings is a
challenging task, given the difficulty both in defining the constraints that
make a movement correct, and the difficulty in using noisy data to determine if
these constraints were satisfied. This paper presents a method for the
detection of deviations from the correct form in movements from physical
therapy routines based on Hidden Markov Models, which is compared to Dynamic
Time Warping. The activities studied include upper an lower limbs movements,
the data used comes from a Kinect sensor. Correct repetitions of the activities
of interest were recorded, as well as deviations from these correct forms. The
ability of the proposed approach to detect these deviations was studied.
Results show that a system based on HMM is much more likely to determine if a
certain movement has deviated from the specification.
