Scientists at Nanyang Technological University (NTU) in Singapore developed a system for tracking human movement and activity in metaverse environments using Wi-Fi signals.
The invention promises to overcome the constraints of current tracking methods that rely on body-worn sensors or external cameras. These approaches are limited in their ability to gather data from particular areas on the body due to obstructions and inadequate lighting.
The approach developed by the NTU team has an impact on Wi-Fi signals’ ability to penetrate walls and detect minute movements. The data is then sent into artificial intelligence, which interprets the signals and models full-body motion and activity.
Prior initiatives encountered significant challenges, including the necessity for huge labeled datasets to train AI algorithms. To address this, the researchers developed an unsupervised learning technique known as “MaskFi.”
MaskFi enables models to be trained with less input and iteratively refined until a high degree of accuracy is achieved. During testing, the system obtained roughly 97% accuracy in relevant studies.
NTU researchers developed novel technology that improves metaverse experiences by allowing for precise whole-body monitoring as well as better avatar control and ambient interactions.
Beyond gaming and social uses, this breakthrough has the potential to improve health monitoring, elder care, and security surveillance while maintaining anonymity. Their work, which uses Wi-Fi infrastructure and AI, pioneers simple, interoperable tracking modalities that promise benefits across a wide range of industries, from healthcare to entertainment.
