Most patients requiring neurorehabilitation continue training at home without supervision of their therapists. They have troubles such as loss of motivation, routine difficulty, and lack of a guide to execute a task. We propose to support these patients with an application that follows the Magic Mirror paradigm, using a Natural User Interface and Microsoft Kinect. The software is aimed at patients suffering from various types of upper limb dysfunction. It is composed of three routines that allow patients to train their injured upper limbs to reach and grasp objects. The performed tests show the proposed application suitability. Therapists as well as adults with upper limb dysfunction participated in the study. These patients undergo rehabilitation at the Institute of Neurosurgery in Santiago, Chile. The application includes nine functions that received positive evaluations.

Model-based evaluation has been widely used in HCI. However, current predictive models are insufficient to evaluate Natural User Interfaces based on touchless hand gestures. The purpose of this paper is to present a model based on KLM to predict performance time for doing tasks using this interface type. The required model operators were defined considering the temporal structure of hand gestures (i.e. using gesture units) and performing a systematic bibliographic review. The times for these operators were estimated by a user study consisting of various parts. Finally, the model empirical evaluation gave acceptable results (root-mean-square error = 10%, R2 = 0.936) when compared to similar models developed for other interaction styles. Thus, the proposed model should be helpful to software designers to carry out usability assessments by predicting performance time without user participation.

Interfaces based on mid-air gestures often use a one-to-one mapping between gestures and commands, but most remain very basic. Actually, people exhibit inherent intrinsic variations for their gesture articulations because gestures carry dependency with both the person producing them and the specific context, social or cultural, in which they are being produced. We advocate that allowing applications to map many gestures to one command is a key step to give more flexibility, avoid penalizations, and lead to better user interaction experiences. Accordingly, this paper presents our results on mid-air gesture variability. We are mainly concerned with understanding variability in mid-air gesture articulations from a pure user-centric perspective. We describe a comprehensive investigation on how users vary the production of gestures under unconstrained articulation conditions. The conducted user study consisted in two tasks. The first one provides a model of user conception and production of gestures; from this study we also derive an embodied taxonomy of gestures. This taxonomy is used as a basis for the second experiment, in which we perform a fine grain quantitative analysis of gesture articulation variability. Based on these results, we discuss implications for gesture interface designs.

The lack of interactivity in the classroom has been identified as an important barrier to make the learning processes more effective. Although several instructional strategies and dynamics have been proposed, including the use of supporting technology, the problem remains open. This paper explores the feasibility of using touchless hand gestures (THG) during lectures in order to promote and ease the students' participation, and thus increasing their interactivity. Particularly, this work focuses on supporting the students' participation from their own spot in the classroom, as a strategy to help them feel comfortable and increase their participation during lectures. After conducting a literature review, no proposals or guidelines helping developers create THG-based applications to support such a participation strategy for the classroom scenario were found. Therefore, the authors used the literature review and their own experience to define a set of design aspects that should be considered when developing this type of applications. The suitability of this design guideline was evaluated with a user study with two applications. The quantitative and qualitative results show the students' participation from their spot could be feasible of being used in practice and it could increase the interaction during lectures. Moreover, the proposed design guidelines are shown to be suitable to support the development of these applications.

Quantitative user models such as CLC, Isokoski’s and KLM have been used to estimate the production time of mouse and pen interactions (pointing, clicking, selecting, drawing, writing). In this paper, we assess if these models can be adapted to estimate the production time of touchless hand gestures (air figures of letters and numbers). New parameters were added to the existing models with empirical values drawn from experiments with users. Two metrics were used to evaluate model quality: strength of the relationship between estimated and observed times, and percentage root mean square error. The obtained results support the hypothesis that CLC, Isokoski’s and KLM can be adapted to touchless hand gestures. The paper contributes with model modifications and parameters required to estimate the production times of touchless hand gestures.