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This work presents a person independent pointing gesture recognition application. It uses simple but effective features for the robust tracking of the head and the hand of the user in an undefined environment. The application is able to detect if the tracking is lost and can be reinitialized automatically. The pointing gesture recognition accuracy is improved by the proposed fingertip detection algorithm and by the detection of the width of the face. The experimental evaluation with eight different subjects shows that the overall average pointing gesture recognition rate of the system for distances up to 250 cm (head to pointing target) is 86.63% (with a distance between objects of 23 cm). Considering just frontal pointing gestures for distances up to 250 cm the gesture recognition rate is 90.97% and for distances up to 194 cm even 95.31%. The average error angle is 7.28◦.
This Paper presents the methodical approach and early findings of the project SEN-TAF (Technology Acceptance by the Elderly to Increase Independence). The project aims to examine the acceptance of robotic systems by elderly people and make early recommendations of necessary features those systems should contain. Based on theoretical approaches of technology acceptance and an empirical study to examine the general need of support of the elderly we developed several scenarios of robot applications. These scenarios are then visualized in animations and simulations to check the preliminary defined acceptance model. Beside these scenarios we survey several other factors which might have an impact of the overall acceptance, e.g. the appearance of the robotic systems (humanoid vs. technical appearance) and the interaction 'mode' (speaking vs. nonspeaking). In addition to these animations and simulations we survey the acceptance of the robotic dog AIBO as early placeholder for future developments in animal robotic systems which could serve as a resource against boredom.