In recent years, a large variety of online communication tools have emerged, including social Virtual Reality (VR) platforms for interacting in a virtual world with participants being represented as virtual avatars. Given their popularity, an active area of research focuses on improving the user experience in these virtual experiences. To enable experimentation at large scale on online platforms, it is however essential to collect behavioural data (e.g. movements and audio information). In this work, we present a toolchain that enables the running of experiments using a modified version of the social VR platform Mozilla Hubs. Specifically, our toolkit enables collection and tracking or user positions and movements at a central location, enabling fine-grained analysis of user behaviour during a social VR experience.

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We are facing increasingly pressure on reducing travel and working remotely. Tools that support effective remote communication and collaboration are much needed. Social Virtual Reality (VR) is an emerging medium, which invites multiple users to join a collaborative virtual environment (VE) and has the potential to support remote communication in a natural and immersive way. We successfully organized a CHI 2020 Social VR workshop virtually on Mozilla Hubs, which invited researchers and practitioners to have a fruitful discussion over user representations and ethics, evaluation methods, and interaction techniques for social VR as an emerging immersive remote communication tool. In this CHI 2021 virtual workshop, we would like to organize it again on Mozilla Hubs, continuing the discussion about proxemics, social cues and VE designs, which were identified as important aspects for social VR communication in our CHI 2020 workshop.

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This paper reflects on the development of a multi-sensory clubbing experience which was deployed during a two-day event within the context of the Amsterdam Dance Event in October 2016 in Amsterdam. We present how the entire experience was developed end-to-end and deployed at the event through the collaboration of several project partners from industries such as art and design, music, food, technology and research. Central to the system are smart textiles, namely wristbands equipped with Bluetooth LE sensors which were used to sense people attending the dance event. We describe the components of the system, the development process, the collaboration between the involved entities and the event itself. To conclude the paper, we highlight insights gained from conducting a real world research deployment across many collaborators and stakeholders with different backgrounds.@en

This article showcases our effort to explore the music club of the future. We present the development and results of an end-to-end system which enhances the club-going experience through the use of wearable technology. Each party guest wearing one of the wristbands actively contributes to the overall experience with their movement and location patterns. The system collects acceleration data from each of the attendees in real-time and feeds it into a pluggable network infrastructure, which processes the data, affecting the environment via data visualization or controlling of the light and sound system of a curated space within the club. Finally we describe the results of a two night, 450 person per night deployment.@en

The CWI-ADE2016 Dataset is a collection of more than 40 million Bluetooth Low Energy (BLE) packets and of 14 million accelerometer and temperature samples generated by wristbands that people wore in a nightclub. The data was gathered during Amsterdam Dance Event 2016 in an exclusive club experience curated around human senses, which leveraged technology as a bridge between the club and the guests. Each guest was handed a custom-made wristband with a BLE-enabled device that broadcast movement, temperature and other sensor readings. A network of Raspberry Pi receivers deployed for the occasion captured broadcast packets from wristbands and any other BLE device in the environment. This data provides a full picture of the performance of the real life deployment of a sensing infrastructure and gives insights to designing sensing platforms, understanding networks and crowds behaviour or studying opportunistic sensing. This paper describes an analysis of this dataset and some examples of usage.

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