In response to torso-oriented magnetic localization tasks that require the system to have interactivity and flexibility with guaranteed accuracy, a novel bio-inspired magnetic sensor array configuration is proposed in this paper. Precisely, the ideas of the natural characteristics of octopus flexible tentacles and the "wrap" morphology are integrated into the design of the magnetic localization system based on the sensor array method. It is worth mentioning that such a design enhances the interactivity and flexibility of the localization system compared to the general planar sensor array strategy. Apart from the concept introduction, the geometry analysis of the proposed configuration is presented based on the constant curvature model. Besides, the magnetic localization algorithm for the system is presented by constructing a magnetic tracking optimization function. Eventually, the proposed concept and developed algorithm are examined in the sensor-array-simulation environment to manifest their effectiveness and applicability. The experimental results indicate that the octopus-inspired-configuration sensor array achieves a mean accuracy at a centimeter-level in our cases, and has better accuracy with a mean value of ē as 0.0178 m and ${\overline {SQR} _{ave{\text{ }}}}$ as 0.0883 for the center interest space compared to general planar configuration one. Moreover, the effect of the configuration error is analyzed. These results verify the feasibility and superiority of the proposed concept and hold significant practical significance in addressing the challenge associated with magnetic localization tasks toward the clinical application scenarios.

The unprecedented advance of wireless human sensing is enabled by the proliferation of the deep learning techniques, which, however, rely heavily on the completeness and representativeness of the data patterns contained in the training set. Thus, deep learning based wireless human perception models usually fail when the human subject is conducting activities that are unseen during the model training. To address this problem, we propose a novel wireless signal augmentation framework, named mmGPE, for Generalized mmWave-based Pose Estimation. In mmGPE, we adopt a physical simulator to generate mmWave FMCW signals. However, due to the imperfect simulation of the physical world, there is a big gap between the signals generated by the physical simulator and the real-world signals collected by the mmWave radar. To tackle this challenge, we propose to integrate the physical signal simulation with deep learning techniques. Specifically, we develop a deep learning-based signal refiner in mmGPE that is capable of bridging the gap and generating realistic signal data. Through extensive evaluations on a COTS mmWave testbed, our mmGPE system demonstrates high accuracy in generating human meshes for unseen activities.

The recent proliferation of various wireless sensing systems and applications demonstrates the advantages of radio frequency (RF) signals over traditional camera-based solutions that are faced with various challenges, such as occlusions and poor lighting conditions. Towards the ultimate goal of imaging human body using RF signals, researchers have been exploring the possibility of constructing the human mesh, a structure capturing not only the pose but also the shape of the human body, from RF signals. In this paper, we introduce M4esh, a novel system that utilizes commercial millimeter wave (mmWave) radar for multi-subject 3D human mesh construction. Our M4esh system can detect and track the subjects on a 2D energy map by predicting the subject bounding boxes on the map, and tackle the subjects' mutual occlusion through utilizing the location, velocity and size information of the subjects' bounding boxes from the previous frames as a clue to estimate the bounding box in the current frame. Through extensive experiments on a real-world COTS millimeter-wave testbed, we show that our proposed M4esh system can accurately localize the subjects and generate their human meshes, which demonstrate the superior effectiveness of the proposed M4esh system.

Stereoscopic visual fatigue (SVF) due to prolonged immersion in the virtual environment can lead to negative user experience, thus hindering the development of virtual reality (VR) industry. Previous studies have focused on investigating the evaluation indicators associated with SVF, while few studies have been conducted to reveal the underlying neural mechanism, especially in VR applications. In this paper, a modified Go/NoGo paradigm was adopted to induce SVF in VR environment with Go trials for maintaining participants' attention and NoGo trials for investigating the neural effects under SVF. Random dot stereograms (RDSs) with 11 disparities were presented to evoke the depth-related visual evoked potentials (DVEPs) during 64-channel EEG recordings. EEG datasets collected from 15 participants in NoGo trials were selected to conduct individual processing and group analysis, in which the characteristics of the DVEPs components for various fatigue degrees were compared and independent components were clustered to explore the original cortex areas related to SVF. Point-by-point permutation statistics revealed that DVEPs sample points from 230 ms to 280 ms (component P2) in most brain areas changed significantly when SVF increased. Additionally, independent component analysis (ICA) identified that component P2 which originated from posterior cingulate cortex and precuneus, was associated statistically with SVF. We believe that SVF is rather a conscious status concerning the changes of self-awareness or self-location awareness than the performance reduction of retinal image processing. Moreover, we suggest that indicators representing higher conscious state may be a better indicator for SVF evaluation in VR environments.

A brain-computer interface (BCI) facilitates bypassing the peripheral nervous system and directly communicating with surrounding devices. Navigation technology using BCI has developed—from exploring the prototype paradigm in the virtual environment (VE) to accurately completing the locomotion intention of the operator in the form of a powered wheelchair or mobile robot in a real environment. This paper summarizes BCI navigation applications that have been used in both real and VEs in the past 20 years. Horizontal comparisons were conducted between various paradigms applied to BCI and their unique signal-processing methods. Owing to the shift in the control mode from synchronous to asynchronous, the development trend of navigation applications in the VE was also reviewed. The contrast between highlevel commands and low-level commands is introduced as the main line to review the two major applications of BCI navigation in real environments: mobile robots and unmanned aerial vehicles (UAVs). Finally, applications of BCI navigation to scenarios outside the laboratory; research challenges, including human factors in navigation application interaction design; and the feasibility of hybrid BCI for BCI navigation are discussed in detail.

Motor imagery brain-computer interfaces have a wide range of promising applications in medical, entertainment and home applications, however, such problems as few motor imagery brain-computer paradigms and weak EEG features need to be addressed. In order to explore good experimental paradigms to induce users to perform motor imagery tasks more effectively, provide more effective EEG features and save training time and increase system robustness, this paper designs a novel motor imagination brain-computer interaction paradigm, which combines virtual reality and haptic stimulation to provide synchronized visual-haptic feedback to the system and enhance the user's motor imagination ability. The experimental results show that there are significant differences between the traditional paradigms and the proposed paradigm in terms of event-related spectral perturbations, which are more obvious in the 10-20Hz and 25-30Hz in the proposed, and the embodiment scores in the proposed paradigm are also higher. In summary, the proposed paradigm can improve users' motor imagery ability by enhancing their embodiments.

Underwater motion recognition using acoustic wireless networks has a promisingly potential to be applied to the diver activity monitoring and aquatic animal recognition without the burden of expensive underwater cameras which have been used by the image-based underwater classification techniques. However, accurately extracting features that are independent of the complicated underwater environments such as inhomogeneous deep seawater is a serious challenge for underwater motion recognition. Velocities of target body (VTB) during the motion are excellent environment independent features for WiFi-based recognition techniques in the indoor environments, however, VTB features are hard to be extracted accurately in the underwater environments. The inaccurate VTB estimation is caused by the fact that the signal propagates along with a curve instead of a straight line as the signal propagates in the air. In this paper, we propose an underwater motion recognition mechanism in the inhomogeneous deep seawater using acoustic wireless networks. To accurately extract velocities of target body features, we first derive Doppler Frequency Shift (DFS) coefficients that can be utilized for VTB estimation when signals propagate deviously. Secondly, we propose a dynamic self-refining (DSR) optimization algorithm with acoustic wireless networks that consist of multiple transmitter-receiver links to estimate the VTB. Those VTB features can be utilized to train the convolutional neural networks (CNN). Through the simulation, estimated VTB features are evaluated and the testing recognition results validate that our proposed underwater motion recognition mechanism is able to achieve high classification accuracy.

This paper presents WiPose, the first 3D human pose construction framework using commercial WiFi devices. From the pervasive WiFi signals, WiPose can reconstruct 3D skeletons composed of the joints on both limbs and torso of the human body. By overcoming the technical challenges faced by traditional camera-based human perception solutions, such as lighting and occlusion, the proposed WiFi human sensing technique demonstrates the potential to enable a new generation of applications such as health care, assisted living, gaming, and virtual reality. WiPose is based on a novel deep learning model that addresses a series of technical challenges. First, WiPose can encode the prior knowledge of human skeleton into the posture construction process to ensure the estimated joints satisfy the skeletal structure of the human body. Second, to achieve cross environment generalization, WiPose takes as input a 3D velocity profile which can capture the movements of the whole 3D space, and thus separate posture-specific features from the static objects in the ambient environment. Finally, WiPose employs a recurrent neural network (RNN) and a smooth loss to enforce smooth movements of the generated skeletons. Our evaluation results on a real-world WiFi sensing testbed with distributed antennas show that WiPose can localize each joint on the human skeleton with an average error of 2.83cm, achieving a 35% improvement in accuracy over the state-of-the-art posture construction model designed for dedicated radar sensors.

Driven by the development of machine learning and the development of wireless techniques, lots of research efforts have been spent on the human activity recognition (HAR). Although various deep learning algorithms can achieve high accuracy for recognizing human activities, existing works lack of a theoretical performance upper bound which is the best accuracy that is only limited by the influencing factors in wireless networks such as indoor physical environments and settings of wireless sensing devices regardless of any HAR algorithm. Without the understanding of performance upper bound, mistakenly configuring the influencing factors can reduce the HAR accuracy drastically no matter what deep learning algorithms are utilized. In this paper, we propose the HAR performance upper bound which is the minimum classification error probability that doesn't depend on any HAR algorithms and can be considered as a function of influencing factors in wireless sensing networks for CSI based human activity recognition. Since the performance upper bound can capture the impacts of influencing factors on HAR accuracy, we further analyze the influences of those factors with varying situations such as through the wall HAR and different human activities by MATLAB simulations.