Modeling dynamic human control strategy (HCS), or human skill
through learning is becoming an increasingly popular paradigm in many
different research areas, such as intelligent vehicle systems, virtual
reality, and space robotics. Validating the fidelity of such models
requires that we compare the dynamic trajectories generated by the HCS
model in the control feedback loop to the original human control
data. To this end, we have developed a stochastic similarity measure -
based on Hidden Markov Model (HMM) analysis - capable of comparing
dynamic, multi-dimensional trajectories. In this paper, we first
derive and demonstrate properties of the proposed similarity measure
for stochastic systems. We then apply the similarity measure to
real-time human driving data by comparing different control strategies
for different individuals. Finally, we show that the similarity
measure outperforms the more traditional Bayes classifier in correctly
grouping driving data from the same individual.
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