INTRODUCTION After the release of autonomous virtual reality headsets such as Meta Oculus Quest or HTC Vive Focus in the recent years, immersion technologies have become more affordable. With the announced rise of the metaverse, Virtual Reality (VR) will be a game changer within the entertainment industry but not only. Indeed, many VR applications are dedicated to physical activity, addressing sports like boxing, fencing, climb or fitness. The potential, in terms of motion analytics, has been under-investigated knowing that the headset and the two hand controllers provide 6-axis positional data at a 60Hz frequency at least. Coupled with an external depth camera, 3D positions of skeleton joints can be aggregated to the real-time data flow. With an appropriate software architecture, consumer virtual reality could move from pure entertainment to a flexible platform for motion analytics. METHODS In order to develop an open and device-agnostic software platform for motion analytics in virtual reality, we chose to embrace the open standards defined by the World Wide Web Consortium (W3C) such as WebSockets and WebXR. Acting as the back-end component, a Node.js application serves VR applications as web applications based on the open source framework A-Frame. As a consequence, any device running a web browser (desktop computer, mobile phone, VR headsets) can launch such VR applications. The same server-side application acts also as a WebSockets server enabling real-time communication between clients (devices playing the VR application) and other software components. This functionality allows, for instance, broadcasting data over the World Wide Web (WWW) for wireless monitoring and recording positional data for real-time or offline motion analytics. RESULTS We developed a test application to validate the whole infrastructure. With the VR headset on, the user sees virtual hands in place of his controllers. He must hit several targets that appear randomly in the virtual environment. The time to reach each target is sent with positional data over the WWW. A desktop web browser connected to the same server is able to stream the VR content thanks to the broadcasted data flow. Plus, another web interface is able to draw charts to visualize motion analytics. CONCLUSION Following the massive adoption of virtual reality on a consumer level, we developed a software architecture relying on open standards defined by the W3C to move this technology from entertainment to motion analytics with a device-agnostic approach. This is the guaranty to deploy this solution on a large scale without any restrictions coming from manufacturers. The positional data, under-investigated for the moment, could provide relevant information in terms of motion analytics.