A Hierarchical Hidden Markov Model to support activities of daily living with an assistive robotic walker

This paper proposes a Hierarchical Hidden Markov Model (HHMM) framework as the most suitable tool to exploit the interactions between an intelligent mobility aid and their human operator. The framework presented is capable of learning a mixed array of the Activities of Daily Living (ADL) that the typical user of these supportive devices would normally engage in, both navigational and non-navigational in nature, and provide assistance as and when required. The main contribution of this paper is the demonstration of how this probabilistic tool capable of modelling behaviours at multiple levels of abstraction is a natural embodiment of machine intelligence to support user activities. Effectiveness of the proposed HHMM framework is evaluated with a number of healthy volunteers using a conventional rolling walker equipped with sensing and navigational aids whilst operating in a structured environment resembling a home. A comparison with more traditional discriminative models and mixed generative-discriminative models is also presented to provide a complete picture that highlights the benefits of the proposed approach.