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Object detection plays a pivotal role in various fields, for example, a smart traffic system relies on the detected results for decision-making. However, existing studies predominately utilize optical camera and LiDAR, which exhibit limitations in adverse outdoor environments, such as foggy weather. To address these challenges, millimeter-waves (mmWaves) attract researchers’ attention to detect objects in severe conditions since they can work effectively in low-visibility conditions and overcome small obstacles. Yet, previous mmWave-based works have shown limited performance, such as no shape information for objects. Therefore, we design and implement a two-stage system,mmBox, to accurately predict bounding boxes with depth for vehicles and pedestrians, which first generates heatmaps in different dimensions and then leverages a deep learning model to extract features for predictions. To evaluate the performance ofmmBox, we collected real-world mmWave reflections from urban traffic intersections and dense-fog environments. The extensive evaluation metrics show remarkable accuracy and the low latency of our model. 

We presentCoSense, a system that enables coexistence of networking and sensing on next-generation millimeter-wave (mmWave) picocells for traffic monitoring and pedestrian safety at intersections in all weather conditions. Although existing wireless signal-based object detection systems are available, they suffer from limited resolution and their outputs may not provide sufficient discriminatory information in complex scenes, such as traffic intersections.CoSenseproposes using 5G picocells, which operate at mmWave frequency bands and provide higher data rates and higher sensing resolution than traditional wireless technology. However, it is difficult to run sensing applications and data transfer simultaneously on mmWave devices due to potential interference, and using special-purpose sensing hardware can prohibit deployment of sensing applications to a large number of existing and future inexpensive mmWave devices. Additionally, mmWave devices are vulnerable to weak reflectivity and specularity challenges, which may result in loss of information about objects and pedestrians. To overcome these challenges,CoSensedesign customized deep learning models that not only can recover missing information about the target scene but also enable coexistence of networking and sensing. We evaluateCoSenseon diverse data samples captured at traffic intersections and demonstrate that it can detect and locate pedestrians and vehicles, both qualitatively and quantitatively, without significantly affecting the networking throughput. 

In this work, we proposeMiSleep, a deep learning augmented millimeter-wave (mmWave) wireless system to monitor human sleep posture by predicting the 3D location of the body joints of a person during sleep. Unlike existing vision- or wearable-based sleep monitoring systems,MiSleepis not privacy-invasive and does not require users to wear anything on their body.MiSleepleverages knowledge of human anatomical features and deep learning models to solve challenges in existing mmWave devices with low-resolution and aliased imaging, and specularity in signals.MiSleepbuilds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since a practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction,MiSleepdesigns a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn, and predict the posture only during the rest states. We evaluateMiSleepwith real data collected from Commercial-Off-The-Shelf mmWave devices for 8 volunteers of diverse ages, genders, and heights performing different sleep postures. We observe thatMiSleepidentifies the toss-turn events start time and duration within 1.25 s and 1.7 s of the ground truth, respectively, and predicts the 3D location of body joints with a median error of 1.3 cm only and can perform even under the blankets, with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.