Head-based interactions are very handy for virtual reality (VR) head-worn display (HWD) systems. We provide implications for the design of interactive auditory mediated reality systems enabling users to be safe, comfortable and less isolated from their surroundings, while re-gaining agency over their sense of hearing.
Usability and workload did not differ significantly for the two granularities but a set of four states can be considered sufficient for most scenarios, namely: ”muted”, ”quieter”, ”louder” and ”unchanged”, allowing for smoother interaction flows.
A follow-up study (n = 12) compared a reduced interaction granularity with a continuous one in VR. In a week-long event sampling study (n = 12), we found that users mostly desire muting or a distinct "quiet but- audible" volume for sound sources. Mediation of sounds through smarter headphones (e.g., hearables) could address this by providing nonuniform interaction with sounds while retaining a comfortable, yet informative soundscape. Many people utilize audio equipment to escape from noises around them, leading to the desired isolation but also dangerously reduced awareness. Our results suggest that CNNs do not directly learn useful visual features with end-to-end training from raw images alone instead, a better approach is to first extract optical flow explicitly and then train CNNs to integrate optical flow and visual information. We propose several approaches based on Convolutional Neural Networks (CNNs) that combine raw images and optical flow fields to learn to distinguish regions with optical flow caused by global ego-motion from those caused by other motion in a scene. More specifically, we estimate head (camera) motion from egocentric video, which can be further used to infer non-verbal behaviors such as head turns and nodding in multimodal interactions.
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In this paper, we investigate how to exploit egocentric vision to infer multimodal behaviors from people wearing head-mounted cameras. The recent availability of lightweight, wearable cameras allows for collecting video data from a "first-person' perspective, capturing the visual world of the wearer in everyday interactive contexts. Finally the rate of change dependent on the head angle tends to lead to fairly long task completion times, although in theory it offers a good combination of speed and accuracy. The technique with constant rate of change after crossing the head angle threshold was simple and functional, but slow when the rate of change is adjusted to suit beginners.
Direct mapping of head orientation to list position is fast and easy to understand, but the signal-to-noise ratio of eye and head position measurement limits the possible range. We found that the haptic feedback had no noticeable effect in objective metrics, but it sometimes improved user experience. We compared three different techniques for mapping the head turn to scrolling a list of numbers with and without haptic feedback. The eye and head movements were combined so that one can select the object to interact by looking at it and then change a setting in that object by turning the head horizontally. We investigated the use of eye tracking in this context in two experiments. The best way to construct user interfaces for smart glasses is not yet known.
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