Extended Reality (XR) technology -such as virtual and augmented reality -is now widely used in Human Computer Interaction (HCI), social science and psychology experimentation. However, these experiments are predominantly deployed in-lab with a co-present researcher. Remote experiments, without co-present researchers, have not flourished, despite the success of remote approaches for non-XR investigations. This paper summarises findings from a 30-item survey of 46 XR researchers to understand perceived limitations and benefits of remote XR experimentation. Our thematic analysis identifies concerns common with non-XR remote research, such as participant recruitment, as well as XR-specific issues, including safety and hardware variability. We identify potential positive affordances of XR technology, including leveraging data collection functionalities builtin to HMDs (e.g. hand, gaze tracking) and the portability and reproducibility of an experimental setting. We suggest that XR technology could be conceptualised as an interactive technology and a capable data-collection device suited for remote experimentation. CCS CONCEPTS• Human-centered computing → Mixed / augmented reality; Virtual reality.

Abstract-Todays smartphones come equipped with a range of advanced sensors capable of sensing motion, orientation, audio as well as environmental data with high accuracy. With the existence of application distribution channels such as the Apple App Store and the Google Play Store, researchers can distribute applications and collect large scale data in ways that previously were not possible. Motivated by the lack of a universal, multiplatform sensing library, in this work we present the design and implementation of SensingKit, an open-source continuous sensing system that supports both iOS and Android mobile devices. One of the unique features of SensingKit is the support of the latest beacon technologies based on Bluetooth Smart (BLE), such as iBeacon TM and Eddystone TM . We evaluate and compare the power consumption of each supported sensor individually, using an iPhone 5S device running on iOS 9. We believe that this platform will be beneficial to all researchers and developers who plan to use mobile sensing technology in large-scale experiments.

The role of affective states in cognitive performance has long been an area of interest in cognitive science. Recent research in game-based cognitive training suggest that cognitive games should incorporate real-time adaptive mechanisms. These adaptive mechanisms would change the game's difficulty according to the player's performance in order to provide appropriate challenges and thus, achieve a real cognitive improvement. However, these mechanisms currently ignore the effects of valence and arousal on the player's cognitive skills. In this paper we investigate how working memory (WM) performance is affected when playing a VR game, and the effects of valence and arousal in this context. To this aim, a custom video game was created for Desktop and VR. Three difficulty levels were designed to evoke different levels of arousal while maintaining the same memory load for each difficulty level. We found an improvement in WM performance when playing in VR compared to Desktop. This effect was particularly pronounced in those with a low WM capacity. Significantly higher levels of valence and arousal were self-reported when playing in VR. We explore the impact that reported affective states could have in the player's WM performance. We suggest that high levels of arousal and positive valence can lead players to a flow state [1] that may have a positive impact on the player's WM performance.

Researchers have examined crowd behavior in the past by employing a variety of methods including ethnographic studies, computer vision techniques and manual annotation based data analysis. However, because of the resources to collect, process and analyze data, it remains difficult to obtain large data sets for study. In an attempt to alleviate this problem, researchers have recently used mobile sensing, however this technique is currently only able to detect either stationary or moving crowds with questionable accuracy. In this work we present a system for detecting stationary interactions inside crowds using the Received Signal Strength Indicator of Bluetooth Smart (BLE) sensor, combined with the Motion Activity of each device. By utilizing Apple's iBea-con™ implementation of Bluetooth Smart, we are able to detect the proximity of users carrying a smartphone in their pocket. We then use an algorithm based on graph theory to predict interactions inside the crowd and verify our findings using video footage as ground truth. Our approach is particularly beneficial to the design and implementation of crowd behavior analytics, design of influence strategies, and algorithms for crowd reconfiguration.

No abstract