CLASSIFYING CHRONIC LOWER BACK PAIN GROUPS USING A TIME SERIES MODEL OF LIFTING

TL;DRAbstract

A classification procedure was developed that uses hidden Markov models (HMMs) to identify sub-groups within a chronic lower back pain (CLBP) patient population based on their time series of lifting patterns during a repetitive lifting task. Based on clinical observations of a repetitive lifting task, our approach assumed that the patient population was composed of two groups: one group that performed lifts more similar to controls than to other patients and another group that lifted differently from control subjects. Two HMMs were designed to describe the repetitive lifting data, one derived from the control subject data and one derived from the CLBP subject data. The HMMs were designed based on the results of a data reduction procedure that reduced and combined the multidimensional lifting parameters into discrete lifting patterns using factor analysis and cluster analysis. Simulation studies were performed to demonstrate that the HMMs could reliably identify subjects from one group

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