With the rapid development of the Internet of Things (IoT) and the rise of 5G communication networks and automatic driving, millimeter wave (mmWave) sensing is emerging and starts impacting our life and workspace. mmWave sensing can sense humans and objects in a contactless way, providing fine-grained sensing ability. In the past few years, many mmWave sensing techniques have been proposed and applied in various human sensing applications (e.g., human localization, gesture recognition, and vital monitoring). We discover the need of a comprehensive survey to summarize the technology, platforms and applications of mmWave-based human sensing. In this survey, we first present the mmWave hardware platforms and some key techniques of mmWave sensing. We then provide a comprehensive review of existing mmWave-based human sensing works. Specifically, we divide existing works into four categories according to the sensing granularity: human tracking and localization, motion recognition, biometric measurement and human imaging. Finally, we discuss the potential research challenges and present future directions in this area.

Sleep monitoring is essential to people's health and wellbeing, which can also assist in the diagnosis and treatment of sleep disorder. Compared with contact-based solutions, contactless sleep monitoring does not attach any device to the human body; hence, it has attracted increasing attention in recent years. Inspired by the recent advances in Wi-Fi-based sensing, this article proposes a low-cost and nonintrusive sleep monitoring system using commodity Wi-Fi devices, namely, WiFi-Sleep. We leverage the fine-grained channel state information from multiple antennas and propose advanced fusion and signal processing methods to extract accurate respiration and body movement information. We introduce a deep learning method combined with clinical sleep medicine prior knowledge to achieve four-stage sleep monitoring with limited data sources (i.e., only respiration and body movement information). We benchmark the performance of WiFi-Sleep with polysomnography, the gold reference standard. Results show that WiFi-Sleep achieves an accuracy of 81.8%, which is comparable to the state-of-the-art sleep stage monitoring using expensive radar devices.

Understanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive and robust system that supports independent living of older people. The system interprets what a person is doing by deciphering signal fluctuations using radio-frequency identification (RFID) technology and machine learning algorithms. To deal with noisy, streaming, and unstable RFID signals, we develop a compressive sensing dictionary-based approach that can learn a set of compact and informative dictionaries of activities using an unsupervised subspace decomposition. In particular, we devise a number of approaches to explore the properties of sparse coefficients of the learned dictionaries for fully utilizing the emboddied discirminative information on the activity recognition task. Our approach achieves efficient and robust activity recognition via a more compact and robust representation of activities. Extensive experiments conducted in a real-life residential environment demonstrate that our proposed system offers a good overall performance and shows the promising practical potential to underpin the applications for the independent living of the elderly.

LoRa has emerged as a promising Low-Power Wide Area Network (LP-WAN) technology to connect a huge number of Internet-of-Things (IoT) devices. The dense deployment and an increasing number of IoT devices lead to intense collisions due to uncoordinated transmissions. However, the current MAC/PHY design of LoRaWAN fails to recover collisions, resulting in degraded performance as the system scales. This article presents FTrack, a novel communication paradigm that enables demodulation of collided LoRa transmissions. FTrack resolves LoRa collisions at the physical layer and thereby supports parallel decoding for LoRa transmissions. We propose a novel technique to separate collided transmissions by jointly considering both the time domain and the frequency domain features. The proposed technique is motivated from two key observations: (1) the symbol edges of the same frame exhibit periodic patterns, while the symbol edges of different frames are usually misaligned in time; (2) the frequency of LoRa signal increases continuously in between the edges of symbol, yet exhibits sudden changes at the symbol edges. We detect the continuity of signal frequency to remove interference and further exploit the time-domain information of symbol edges to recover symbols of all collided frames. We substantially optimize computation-intensive tasks and meet the real-time requirements of parallel LoRa decoding. We implement FTrack on a low-cost software defined radio. Our testbed evaluations show that FTrack demodulates collided LoRa frames with low symbol error rates in diverse SNR conditions. It increases the throughput of LoRaWAN in real usage scenarios by up to 3 times.

Recent years have witnessed advances of Internet of Things (IoT) technologies and their applications to enable contactless sensing and elderly care in smart homes. Continuous and real-time respiration monitoring is one of the important applications to promote assistive living for elders during sleep and attracted wide attention in both academia and industry. Most of the existing respiration monitoring systems require expensive and specialized devices to sense chest displacement. However, chest displacement is not a direct indicator of breathing and thus false detection may often occur. In this paper, we design and implement a real-time and contactless respiration monitoring system by directly sensing the exhaled airflow from breathing using ultrasound signals with off-the-shelf speaker and microphone. Exhaled airflow from breathing can be regarded as air turbulence, which scatters the sound wave and results in Doppler effect. Our system works as an acoustic radar which transmits sound wave and detects the Doppler effect caused by breathing airflow. We mathematically model the relationship between the Doppler frequency change and the direction of breathing airflow. Based on this model, we design a Minimum Description Length (MDL) based algorithm to effectively capture the Doppler effect caused by exhaled airflow. We conduct extensive experiments with 25 participants (7 elders, 2 young kids and 16 adults, including 11 females and 14 males) in four different rooms. The participants take four different sleep postures (lying on one’s back, on right/left side and on one’s stomach) in different positions of the bed. Experiment results show that our system achieves a median error lower than 0.3 breaths/min (2%) for respiration monitoring and can accurately identify Apnea. The results also demonstrate that the system is robust to different respiration styles (shallow, normal and deep), respiration rate variation, ambient noise, sensing distance variation (within 0.7 m) and transmitted signal frequency variation.

The on-street parking system is an indispensable part of civic projects, as it provides travellers and shoppers with parking spaces. With the recent in-ground sensors deployed throughout the Melbourne CBD, there is a significant problem on how to use the sensor data to manage parking violations and issue infringement notices efficiently in a short time-window. In this paper, we use a large real-world dataset with on-street parking sensor data from the local city council, and establish a formulation of the Travelling Officer Problem with a general probability-based model. We propose two solutions using a spatiotemporal probability model for parking officers to maximize the number of infringing cars caught with limited time cost. Using real-world parking sensor data and Google Maps road network information, the experimental results show that our proposed algorithms outperform the existing patrolling routes.