Defining Haptic Experience: Foundations for Understanding, Communicating, and Evaluating HX

Haptic technology is maturing, with expectations and evidence that it will contribute to user experience (UX). However, we have very little understanding about how haptic technology can influence people’s experience. Researchers and designers need a way to understand, communicate, and evaluate haptic technology’s effect on UX. From a literature review and two studies – one with haptics novices, the other with expert hapticians – we developed a theoretical model of the factors that constitute a good haptic experience (HX). We define HX and propose its constituent factors: design parameters of Timeliness, Density, Intensity, and Timbre; the cross-cutting concern of Personalization; usability requirements of Utility, Causality, Consistency, and Saliency; and experiential factors of Harmony, Expressivity, Autotelics, Immersion, and Realism as guiding constructs important for haptic experience. This model will help guide design and research of haptic systems, inform language around haptics, and provide the basis for evaluative instruments, such as checklists, heuristics, or questionnaires.We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), [funding reference number 2019-06589

An Ensemble Classifier Based on Three-Way Decisions for Social Touch Gesture Recognition

Social touch is an important form of social interaction. In Human Robot Interaction (HRI), touch can provide additional information to other modalities, such as audio, visual. One of the application is the robot therapy that has great social significance. In this paper, an ensemble classifier based on threeway decisions is proposed to recognize touch gestures. Firstly, features are extracted from on six perspectives and four classifiers are constructed on different scales with different pre-processing methods. . Then an ensemble classifier is used to combine the four classifiers to classify the gestures. The proposed method is tested on the public Corpus of Social Touch (Cost) dataset. The experiments results not only verify the validity of our method but also show the better accuracy of our ensemble classifier