This paper explores the communication of emotion through shape using Morph-UX, a web-based application for emotive form design which uses physiological features correlated with emotional experience as input to control objects attributes in real-time. Centered on the body, our approach aims to provide visual feedback as to how bodily sensations associated with the expression of emotions can alter, transform and modulate shapes. Preliminary results from a 30-participant user study show that shapes generated using Morph-UX accurately convey users' experienced emotional valence and arousal. We describe the design and development of Morph-UX and highlight how embodied shapes can be used to communicate affect, ideas and intentions. Implications for further exploration of physiological signals as a design material are also discussed.

This paper presents an overview of curve clustering from the similarity characteristics perspective, with a goal of providing useful advice and references regarding fundamental concepts that are accessible to the broad community of curve clustering practitioners. We introduce a new taxonomy of curve clustering by proposing four major similarity characteristics. We reviewed some contributions to curve clustering with respect to their similarity characteristics along with their applications. Lastly, we give an in-depth discussion of the overall challenges in this field with respect to similarity characteristics, highlight open research questions and discuss guidelines for further progress.

Practitioners in many fields of human-computer interaction are now using physiological data to measure different aspects of user experience. The dynamic nature of physiological data offers a continuous window to the users and allows a better understanding of their experience while interacting with a system. However, in order to be truly informative, physiological signals need to be closely linked to users’ behaviors and interaction states. This paper presents an analysis method that provides a direct visual interpretation of users’ physiological signals when interacting with an interface. The proposed physiological heatmap tool uses eyetracking data along with physiological signals to identify regions where users are experiencing different emotional and cognitive states with a higher frequency. The method was evaluated in an experiment with 44 participants. Results show that physiological heatmaps are able to identify emotionally significant regions within an interface better than standard gaze heatmaps. Applications of the method to different fields of HCI research are also discussed.

Information seeking activities have been broadly categorized into two types: exploratory and directed search. Likewise, product-related information is processed either by alternative or by attribute. The current literature does not specifically describe the effect of online information-seeking activities on the type of information processing employed by a consumer. The present study seeks to investigate this relationship, as well as the effect of repeat visits to a website as an antecedent of information seeking. An eye-tracking experiment was performed to test these relationships. Results indicate that participants with a greater proportion of time spent looking at directed-search elements had longer visual sweeps across the attributes of a song, indicating a greater degree of alternative-based processing. Additionally, repeat website visits resulted in a marginally higher proportion of time spent performing directed search. The implication of the findings in relation to website customization according to users’ information searching behavior is discussed.

In this paper, we present an off-the-shelf UX evaluation tool which contextualizes users' physiological and behavioral signals while interacting with a system. The proposed tool triangulates users' gaze data with inferred users' cognitive and emotional states to produce user experience (UX) heatmaps, which show where users were looking when they experienced specific cognitive and emotional states. Results show that for a given cognitive state (i.e., cognitive load), the proposed UX heatmap was able to effectively highlight the areas where users experienced different levels of cognitive load on an interface. The proposed tool enables the visual analysis of users' various emotional and cognitive states for specific areas on a given interface, and also to compare users' states across multiple interfaces, which should be useful for both UX researchers and practitioners.