Application of hidden Markov model topology estimation to repetitive lifting data

Vasko et al. (see IEEE Proc. ICASSP '96, vol.6, p.3578-82, 1996) presented an algorithm that estimates the topology of a hidden Markov model (HMM) given a set of time series data. The algorithm iteratively prunes state transitions from a large general HMM topology and selects a topology based on a likelihood criterion and a heuristic evaluation of complexity. We apply the algorithm to estimate the dynamic structure of human body motion data from a repetitive lifting task. The estimated topology for low back pain patients was different from the topology for a control subject group. The body motions of patients tend not to change over the task, but the body motions of control subjects change systematically.