We propose a millimeter-wave (mmWave) wireless signal-based sleep monitoring system aimed at providing information about a person's sleep by detecting sleep events, such as bed entry and exit times, as well as the duration of bed stay. It overcomes the limitations of existing vision-based systems by operating in low-light conditions without invading privacy. It uses spatial-temporal information and signal processing techniques to determine the duration, and our preliminary results indicate that our system can accurately detect bed events.

In this work, we propose MiSleep , 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, MiSleep is not privacy-invasive and does not require users to wear anything on their body. MiSleep leverages 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. MiSleep builds 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, MiSleep designs 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 evaluate MiSleep with real data collected from Commercial-Off-The-Shelf mmWave devices for eight volunteers of diverse ages, genders, and heights performing different sleep postures . We observe that MiSleep identifies 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.

We propose Argosleep, a millimeter-wave (mmWave) wireless sensors based sleep posture monitoring system that predicts the 3D location of body joints of a person during sleep. Argosleep leverages deep learning models and knowledge of human anatomical features to solve challenges with low-resolution, specularity, and aliasing in existing mmWave devices. Argosleep builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction, Argosleep designs 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 evaluate Argosleep with real data collected from COTS mmWave devices for 8 volunteers of diverse ages, gender, and height performing different sleep postures. We observe that Argosleep identifies the toss-turn events accurately and predicts 3D location of body joints with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.

We propose mmSleep, a millimeter-wave (mmWave) wireless signal based sleep posture monitoring system that can assist in tracking 3D location of body joints of a person during sleep. mmSleep overcomes the limitations of existing vision-based sleep monitoring and can work under low-light without being privacy-invasive. mmSleep uses a customized Convolutional Neural Network to learn diverse sleep postures, and our preliminary results show that mmSleep can consistently predict 3D joint locations with high accuracy.

Respiratory diseases, like Asthma, COPD, have been a significant public health challenge over decades. Portable spirometers are effective in continuous monitoring of respiratory syndromes out-of-clinic. However, existing systems are either costly or provide limited information and require extra hardware. In this paper, we present mmFlow, a low-barrier means to perform at-home spirometry tests using 5G smart devices. mmFlow works like regular spirometers, where a user forcibly exhales onto a device; but instead of relying on special-purpose hardware, mmFlow leverages built-in millimeter-wave technology in general-purpose, ubiquitous mobile devices. mmFlow analyzes the tiny vibrations created by the airflow on the device surface and combines wireless signal processing with deep learning to enable a software-only spirometry solution. From empirical evaluations, we find that, when device distance is fixed, mmFlow can predict the spirometry indicators with performance comparable to inclinic spirometers with <5% prediction errors. Besides, mmFlow generalizes well under different environments and human conditions, making it promising for out-of-clinic daily monitoring.