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Socially Assistive Robots used for elderly care are required to determine the location of a person and to approach him/her in order to provide assistance. Human tracking systems are applied to detect and track people that are already in the proximity of the robot, while its limited field of view makes the user easily lost. Moreover, navigation algorithms typically need the availability of reliable sensors on the robot and the possibility of marking possible user locations. On the contrary, in this work, we investigate the opportunity to use a reactive control mechanism for detecting and approaching people. Our approach is tested on two commercial mobile robots that present a different sensors configuration and by using off-the-shelf algorithms for people localization and tracking. Results show the feasibility of the approach with respect to the considered domain that does not require precise positioning, but hopes for a real application of such low-cost robot into the wild. Features of the considered robots and their impact on performance are also discussed.

Social Assistive Robots may potentially distract the user from his/her current activity while performing non-interactive tasks. In this work, we consider a robotic system approaching an elder man, in order to monitor his/her behavior, as the human is occupied in carrying out a specific daily activity. We conducted a pilot study to assess the possibility of an automatic evaluation of the human awareness and how the robot presence interferes with his/her current activity. First conclusions have been obtained comparing questionnaire results with video analysis.

Social Assistive Robots are mainly designed for tasks requiring the interaction with the user. However, they could also be involved in other non-interactive tasks which execution may potentially distract the user from his/her current activity. In this direction, we aim at evaluating the disengagement levels caused by a robot's movements in the human's surroundings. In particular, we consider a robotic system approaching an elder person to monitor his/her behavior, while he/she is occupied in carrying out a specific daily activity. We conducted a classic video analysis of human behaviors in order to identify relevant non-verbal disengagement signals such as the human gaze and pose variation when he/she is approached by the robot. Results obtained from ambient cameras are compared with the ones from the robot camera showing a moderate correlation between them. Additionally, the role of other contextual factors, such as the activity posture, approaching distance, and cognitive load are discussed in the direction of an automatic evaluation of the user's distraction to be used by the robot to plan its motion.