No abstract

Even though human activities may involve physical injuries, there is not much discussion in the academy of how ubiquitous computing could assess the risk related to them. This paper proposes an approach to evaluate the risk of activities considering two factors: actions that compose activities and user performance in such activities. Risk management based on composed actions is measured through the analysis of the frequency of each action for a particular user, so that we are able to capture his customary behaviour. The evaluation of the user's performance is accomplished by addressing performance properties, such as: attention, duration, effectiveness, etc.Our work has its foundations in the Activity Theory for modeling activities allowing the mediation by tools of the interactions between the subject and the environment and in the behavioural model Skill-Rule-Knowledge for understanding the subject's behaviours. To validate our model we developed a case study to demonstrate the functioning of our work. In this scenario we analyze how activities are detected, actions are evaluated and the performance is inferred. At last, an analysis of the final risk for the activity is presented.

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.

Abstract. The skill level of a person in processing information, reacting to his/her surroundings and decision making for performing an activity is determined by the allocation of the mental resources demanded by such activity. When the allocation is inappropriate, there is a higher possibility for some accident to occur. Thus, one can notice that the cognitive workload spent by the person is an important variable that can take him to a risky situation. Since it is not possible to measure the cognitive workload spent by a person during the performance of an activity directly, we noticed the need to evaluate the level of his/her performance in order to be possible to infer the cognitive workload used. So, we propose the creation of a model to classify the cognitive workload based on the behavioral model skill-rule-knowledge and the relations of performance properties with the context surrounding the person. The evaluation of the model was made using a public dataset and the results showed a promising approach for the classification of human performances.