Device-free respiratory monitoring has attracted significant attention due to its potential applications in sleep disorders, psychopathology, and cardiology. It enables respiratory monitoring in a device-free and contact-free manner by analyzing the influence pattern of human respiratory on surrounding wireless signals, such as mmWave signals. Although remarkable progress has been achieved in this task when the targets remain stationary, the respiratory monitoring will fail when the target moves freely. In this paper, we propose a device-free real-time respiratory abnormality monitoring method based on mmWave signal segmentation to solve the aforementioned problem. Specifically, we design the physical state assessment strategy to obtain the real-time states of the target, including positional movement, large-scale activities in place, and micro motions in place. We propose the Doppler signal segmentation method to extract the micro motions signals when the target position remains unchanged. We present the multi-frame joint analysis method to obtain the frequency of micro motions based on the extracted micro motion signals, thereby eliminating interference and achieving real-time respiratory abnormality monitoring. To validate the effectiveness of the proposed methods, we conduct extensive experiments on a 77GHz mmWave testbed. The results indicate that the proposed method is feasible for achieving real-time respiratory abnormality monitoring even when the target moves freely.

Device-free gesture recognition using mobile communication signals is a convenient and efficient technology with broad application prospects in smart homes and human-computer interaction. It utilizes the effect of gestures on surrounding signals to achieve gesture recognition. The cell-specific reference signals (CRS) information can be used to achieve the task in close-range training scenarios. However, when gestures are performed at long-range or in non-training scenarios, the recognition performance will significantly degrade. To enhance device-free gesture recognition capability in arbitrary scenarios, we propose the signal quality enhancement algorithm and the gesture spectrogram construction method to solve this problem. Specifically, we superimpose the CRS information from multiple carriers to improve the gesture signal-to-noise ratio and increase the gesture sensing range. Then, we extract the gesture dynamic components from the CRS information and construct gesture spectrograms to represent scenario-independent gesture motion patterns. Using the gesture spectrogram features, we design a deep network to accomplish the gesture recognition task. We built a prototype system on a software-defined radio platform. Experimental results show that our proposed method can effectively increase the gesture sensing range from 30m² to 228m² and achieve an average recognition accuracy of 82.5% for five types of gestures in arbitrary scenarios.

Device-free positioning (DFP) using mmWave signals is an emerging technique that could track a target without attaching any devices. It conducts position estimation by analyzing the influence of targets on their surrounding mmWave signals. With the widespread utilization of mmWave signals, DFP will have many potential applications in tracking pedestrians and robots in intelligent monitoring systems. State-of-the-art DFP work has already achieved excellent positioning performance when there is one target only, but when there are multiple targets, the time-varying target state, such as entering or leaving of the wireless coverage area and close interactions, makes it challenging to track every target. To solve these problems, in this paper, we propose a spatial-temporal analysis method to robustly track multiple targets based on the high precision mmWave point cloud information. Specifically, we propose a high precision spatial imaging strategy to construct fine-grained mmWave point cloud of the targets, design a spatial-temporal point cloud clustering method to determine the target state, and then leverage a gait based identity and trajectory association scheme and a particle filter to achieve robust identity-aware tracking. Extensive evaluations on a 77 GHz mmWave testbed have been conducted to demonstrate the effectiveness and robustness of our proposed schemes.

Human gesture recognition provides great potentials in Human Computer Interaction (HCI), and the wireless or visual signals based technologies have been explored in their respective fields. The intrinsic characteristics of both modalities are complementary to each other, e.g. the wireless signal is robust to illumination changes and occluded conditions but suffers from low space resolution, while the visual signal has high space resolution but vulnerable to scenario variations. Intuitively, integrating the two modalities has potential chance to improve the overall discriminative power. However, existing multi-modal fusion methods could not fully exploit their complementarity to achieve accurate estimation, and also lack physical interpretability. In order to solve this issue, we introduce WiVi-GR: a Wireless-Visual joint representation based accurate Gesture Recognition system, which constructs a complete velocity representation to guarantee robust and accurate gesture recognition. Specifically, we analyze the complementarity of the two modalities in data dimension and spatial-temporal feature resolution, and propose an Interpretable Orthogonal Representation (IOR), which applies multi-channel coding to get image plane velocity, utilizes frequency domain analysis to get radial velocity, and aggregates both to achieve the complete representation of the dynamic pattern. Based on the IOR, we perform a data-level fusion with channel superposition convolutions to accomplish the accurate gesture recognition task. Experimental results show that the proposed WiVi-GR outperforms traditional multi-modal approaches by large margins, especially in small training sample set condition.

Device-free gesture recognition has attracted significant attention due to its potential applications in pervasive interaction. It enables gesture recognition in a device-free and contact-free manner by analyzing the influence pattern of human gestures on surrounding wireless signals, such as mmWave signals. Although remarkable progress has been achieved in this area, the recognition performance will degrade remarkably when gestures are conducted in different scenarios. In this paper, we leverage mmWave signals to design two robust trajectory features, i.e., the trajectory image and the trajectory time-sequence features, that are independent of the conducted scenarios to solve the aforementioned problems. Specifically, we employ the particle filter algorithm to construct the raw trajectory image utilizing range measurements, rotate and enhance the image to obtain the trajectory image feature suitable for recognition by leveraging a public handwriting font image data set as the training set. Additionally, we derive the range of the trajectory relative to a stable point as the trajectory time-sequence feature. With these trajectory features, we design a deep network to perform the gesture recognition task. To validate the effectiveness of the proposed methods, we conduct extensive experiments on a 77GHz mmWave testbed. The results indicate that the two proposed trajectory features are feasible for achieving scenario-independent gesture recognition.

Device-free gesture recognition is a potential noncontact human–computer interaction technique. It leverages the unique influence of the conducted gesture on surrounding wireless signals to accomplish gesture recognition. Existing methods usually leverage doppler spectrogram of the influenced wireless signals to characterize the motion pattern of gestures. These methods have achieved satisfactory accuracy when the gestures are conducted in a relatively fixed location, direction, and speed. However, when gestures are conducted in a different scenario, the recognition accuracy will drop dramatically. In this article, we try to solve this issue by characterizing the gesture motion pattern using a novel robust intrinsic spectrogram, which is independent of the conducted scenario. Specifically, we create a virtual coordinate system in which the coordinates of a gesture trajectory remain unchanged no matter where and how the gesture is conducted. Then, we design a coordinate transformation method to transform the raw doppler spectrogram into the robust intrinsic spectrogram to characterize the intrinsic motion pattern of the gesture. We further feed the intrinsic spectrogram into a deep network to realize gesture recognition. Extensive evaluations on a 77-GHz mmWave testbed show that the proposed method could achieve an average recognize accuracy of 88.4% with ten types of gestures.

The cell association based on the maximum downlink received power is optimal in a conventional homogeneous network, but this association is not particularly suitable for heterogeneous networks (HetNets). Therefore, the concept of downlink and uplink decoupling (DUDE) is proposed for improving uplink performances of HetNets. However, in DUDE association scheme, macro user equipment (MUE) will suffer more interference from the offloaded users in uplink. In this paper, we investigate uplink interference mitigation through frequency reuse and power control schemes. The reverse frequency allocation (RFA) scheme is adopted to alleviate the cross-tier interference by increasing the distance between users of the same frequency. Then, a dynamic distributed power control (DDPC) with user admission control is proposed to further improve the uplink users performance and system spectrum utilization. The DDPC mitigates co-channel interference by dynamically updating transmitting power of active users and new access users, meanwhile it can maintain the link quality of active users above given signal to interference plus noise ratio (SINR) thresholds at all times. The dynamics and performance of the network are investigated through simulation experiments in terms of average SINR, evolution of uplink transmitting power, uplink SINR, and dynamic adjusting factor. The simulation results show that, in comparison with the DUDE scheme and DUDE with RFA scheme, DUDE with RFA and DDPC scheme can achieve a better quality of service (QoS) and uplink data rate performance.