Sequential intention estimation of a mobility aid user for intelligent navigational assistance

This paper proposes an intelligent mobility aid framework aimed at mitigating the impact of cognitive and/or physical user deficiencies by performing suitable mobility assistance with minimum interference. To this end, a user action model using Gaussian Process Regression (GPR) is proposed to encapsulate the probabilistic and nonlinear relationships among user action, state of the environment and user intention. Moreover, exploiting the analytical tractability of the predictive distribution allows a sequential Bayesian process for user intention estimation to take place. The proposed scheme is validated on data obtained in an indoor setting with an instrumented robotic wheelchair augmented with sensorial feedback from the environment and user commands as well as proprioceptive information from the actual vehicle, achieving accuracy in near real-time of ~80%. The initial results are promising and indicating the suitability of the process to infer user driving behaviors within the context of ambulatory robots designed to provide assistance to users with mobility impairments while carrying out regular daily activities.