  Recent advances in emerging technologies such as artificial intelligence and
extended reality have pushed the Metaverse, a virtual, shared space, into
reality. In Metaverse, users can customize virtual avatars to experience a
different life. While impressive, avatar construction requires a lot of data
that manifest users in the physical world from various perspectives, and
wireless sensing data is one of them. For example, machine learning (ML) and
signal processing can help extract information about user behavior from sensing
data, thereby facilitating avatar behavior construction in the Metaverse. This
article presents a wireless sensing dataset to support the emerging research on
Metaverse avatar construction. Rigorously, the existing data collection
platforms and datasets are analyzed first. On this basis, we introduce the
platform used in this paper, as well as the data collection method and
scenario. We observe that the collected sensing data, i.e., channel state
information (CSI), suffers from a phase shift problem, which negatively affects
the extraction of user information such as behavior and heartbeat and further
deteriorates the avatar construction. Therefore, we propose to detect and
correct this phase shift by a sliding window and phase compensation,
respectively, and then validate the proposed scheme with the collected data.
Finally, several research directions related to the avatar construction are
given from the perspective of datasets.
