A context-aware model for human activity prediction and risk inference in actions

Even though human activities may result in injuries, there is not much discussion in the academy of how ubiquitous computing could assess such risks. So, this paper proposes a model for the Activity Manager layer of the Activity Project, which aims to predict and infer risks in activities. The model uses the Activity Theory for the composition and prediction of activities. It also infers the risk in actions based on changes in the user’s physiological context caused by the actions, and such changes are modeled according to the Hyperspace Analogue to Context model. Tests were conducted and the developed models outperformed proposals found for action prediction, with an accuracy of 78.69%, as well as for risk situation detection, with an accuracy of 98.94%, showing the efficiency of the proposed solution. Keywords: activities of daily living, Activity Theory, activity recognition, activity prediction, risk in actions.

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