Haptic experiences – technology-mediated experiences of touch – are complex and enigmatic; they are intertwined with the user’s motivations, context, and past experiences and are deeply interwoven with other sensory modalities. To better understand such experiences, we develop the Inference-Design Model for Haptic Experience. The model separates stimulus, sensation, and experience and identifies two diametrical processes: inference and design. Inference is the cognitive process through which humans interpret a sensory environment, while design involves the creation of such a sensory environment. Grounded in philosophical, neuroscientific, and psychological literature, we discuss the formation of haptic experiences facilitated through the structure and components of the Inference-Design Model. We use the model to present six key observations about the inference of haptic experiences and the design processes that enable them. The observations and their implications for researchers contribute to a fundamental understanding of haptic experiences in a model-centric research paradigm.

Designed haptic feedback—technology-mediated touch feedback—has the potential to mediate positive and meaningful experiences. These experiences are rich and complex in nature and thus challenging to design. Established User Experience (UX) and Haptic Experience (HX) models describe the design of experiences; however, they are too general and evaluation-focused to inform haptic experience design. We review 104 publications designing haptic experiences and analyse how researchers consider pragmatic, hedonic, and eudaimonic qualities of haptic experience. Our findings show that researchers mainly engage with the pragmatic qualities of the experience. We thus propose a unified model for HX for understanding the design of haptic experiences, combining key elements of UX and HX research to give haptic designers a tool for thinking about the rich and complex haptic experiences elicited by their designs. This raises open questions for haptic experience research, as designing mediated touch experiences through haptic technology remains challenging.

Mid-air haptic stimulation can enrich user experience during human-computer interaction. However, the design space of such stimuli is large due to the number and range of stimulation parameters. It therefore remains difficult for designers to select a stimulus to induce an intended experience. We derive a mapping for mid-air experiences based on two user studies. In the first study, participants rated 36 stimuli varied across three parameters (frequency, pattern, and repetitions). These ratings allowed us to determine a set of five experientially distinct stimuli. In the second study, participants vocalized their experiences with those five stimuli. This allows us to generate a mapping of 17 sensations and 23 experiences related to the stimuli. Finally, we discuss how the mapping can inform designers and researchers working with mid-air haptic technologies.

The VR community has introduced many object selection and manipulation techniques during the past two decades. Typically, they are empirically studied to establish their benefits over the state-of-the-art. However, the literature contains few guidelines on how to conduct such studies; standards developed for evaluating 2D interaction often do not apply. This lack of guidelines makes it hard to compare techniques across studies, to report evaluations consistently, and therefore to accumulate or replicate findings. To build such guidelines, we review 20 years of studies on VR object selection and manipulation. Based on the review, we propose recommendations for designing studies and a checklist for reporting them. We also identify research directions for improving evaluation methods and offer ideas for how to make studies more ecologically valid and rigorous.

Virtual reality (VR) allows users to interact similarly to how they do in the physical world, such as touching, moving, and pointing at objects. To select objects at a distance, most VR techniques rely on casting a ray through one or two points located on the user’s body (e.g., on the head and a finger), and placing a cursor on that ray. However, previous studies show that such rays do not help users achieve optimal pointing accuracy nor correspond to how they would naturally point. We seek to find features, which would best describe natural pointing at distant targets. We collect motion data from seven locations on the hand, arm, and body, while participants point at 27 targets across a virtual room. We evaluate the features of pointing and analyse sets of those for predicting pointing targets. Our analysis shows an 87% classification accuracy between the 27 targets for the best feature set and a mean distance of 23.56 cm in predicting pointing targets across the room. The feature sets can inform the design of more natural and effective VR pointing techniques for distant object selection.

Technological advances are enabling a new class of augmented reality (AR) applications that use bodies as substrates for input and output. In contrast to sensing and augmenting objects, body-based AR applications track people around the user and layer information on them. However, prototyping such applications is complex, time-consuming, and cumbersome, due to a lack of easily accessible tooling and infrastructure. We present Body LayARs, a toolkit for fast development of body-based AR prototypes. Instead of directly programming for a device, Body LayARs provides an extensible graphical programming environment with a device-independent runtime abstraction. We focus on face-based experiences for headset AR, and show how Body LayARs makes a range of body-based AR applications fast and easy to prototype.