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

Takamitsu Matsubara, Jaime Valls Miro, Daisuke Tanaka, James Poon, Kenji Sugimoto
2015 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)  
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
more » ... ws 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.
doi:10.1109/roman.2015.7333580 dblp:conf/ro-man/MatsubaraMTPS15 fatcat:a7f3czrpnnbkdntkcg5af5ryeu