Visual Quality results showed better results from the deep-learning algorithms in terms of the whole body perception and overall segmentation quality. Results showed no significant differences between the different body representations in terms of presence, with moderate improvements in some Embodiment components between the virtual hands and full-body representations. To the best of our knowledge, this is the first user study focused on evaluating video-based self-avatars to represent the user in a MR scene. This immersive experience was carried out by 30 women and 28 men. The study was performed under three body representation conditions: virtual hands, video pass-through with color-based full-body segmentation and video pass-through with deep learning full-body segmentation. To validate this technology, we designed an immersive VR experience where the user has to walk through a narrow tiles path over an active volcano crater. We present our end-to-end system, including: custom MR video pass-through implementation on a commercial head mounted display (HMD), our deep learning-based real-time egocentric body segmentation algorithm, and our optimized offloading architecture, to communicate the segmentation server with the HMD. In this work we explore the creation of self-avatars through video pass-through in Mixed Reality (MR) applications. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. Furthermore, proposed method is verified in 3D practical application scenarios. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Secondly, Cramér–Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. In this paper, we propose a multi-sensor hybrid method to solve this problem. However, it has been facing the tough problem of accumulative errors and drift. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints.

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