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Camera-based interfaces in mobile devices are starting to be used in games and apps, but few works have evaluated them in terms of usability or user perception. Due to the changing nature of mobile contexts, this evaluation requires extensive studies to consider the full spectrum of potential users and contexts. However, previous works usually evaluate these interfaces in controlled environments such as laboratory conditions, therefore, the findings cannot be generalized to real users and real contexts. In this work, we present a robust camera-based interface for mobile entertainment. The interface detects and tracks the user’s head by processing the frames provided by the mobile device’s front camera, and its position is then used to interact with the mobile apps. First, we evaluate the interface as a pointing device to study its accuracy, and different factors to configure such as the gain or the device’s orientation, as well as the optimal target size for the interface. Second, we present an in the wild study to evaluate the usage and the user’s perception when playing a game controlled by head motion. Finally, the game is published in an application store to make it available to a large number of potential users and contexts and we register usage data. Results show the feasibility of using this robust camera-based interface for mobile entertainment in different contexts and by different people.

We used a target-selection task to evaluate head-tracking as an input method on a mobile device. The procedure used a non-ISO Fitts' law task since targets were randomly positioned from trial to trial. Due to a non-constant amplitude within each sequence of trials, throughput was calculated using two methods of data aggregation: by sequence of trials using the mean amplitude and by common A-W conditions. For each data set, we used four methods for calculating throughput. The grand mean for throughput calculated using the division of means and the adjustment for accuracy was 0.74 bps, which is 45% lower than the value obtained using an ISO task. We recommend calculating throughput using the division of means (and not the slope reciprocal from the regression model) and with the adjustment for accuracy. We present design recommendation for non-ISO tasks: Keep amplitude and target width constant within each sequence of trials and use strategies to avoid or remove reaction time. CCS CONCEPTS • Human-centered computing → User studies; HCI theory, concepts and models;

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