Possibilistic activity recognition with uncertain observations to support medication adherence in an assisted ambient living setting

摘要

A recent trend in healthcare is to motivate patients to self-manage their health conditions in home-based settings. Self-management programs guide and motivate patients to achieve self-efficacy in the self-management of their disease through a regime of educational and behavioural modification strategies. To improve self-management programs effectiveness and efficacy, we must consider Ambient Assisted Living (AAL) technologies (smart environments, activity recognition, aid acts planning), since they alleviate issues related to unreliable self-reported data by monitoring self-management activities. To improve self-management programs in smart environments, it is necessary to recognize the occupant behaviour from observed data. Observed data/attributes generated from various sources (sensors, questionnaires, low-level activity recognition) are certain to uncertain (imprecise, incomplete, missing), where several values are plausible instead of only one. Thus, activity recognition must consider heterogeneous observations (sources’ types) and uncertainty in the activity recognition inputs (observations). To address this challenge, we propose an activity recognition approach based on possibilistic network classifiers with uncertain observations. We believe that this is the first work to consider possibilistic network classifiers for the recognition of activities in smart environments using uncertain observations. We have validated the approach on 780 synthetic scenarios illustrating behaviours related to medication adherence. The activity classifiers, based on knowledge and beliefs about the activities related to medication adherence, can correctly recognize 79% of an activity current state, which is comparable with approaches based on data-driven naïve Bayesian classifiers. Furthermore, the classification performance only decreases when we have highly partial to complete ignorance about the observations values. Hence, the validations results show the interest of activity recognition based on possibilistic network classifiers for handling uncertain observations.