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WiFi sensing has received recent and significant interest from academia, industry, healthcare professionals, and other caregivers (including family members) as a potential mechanism to monitor our aging population at a distance without deploying devices on users’ bodies. In particular, these methods have the potential to detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems arises from practical advantages including its ease of operation indoors as well as ready compliance from monitored individuals. Unlike other sensing methods, such as wearables, camera-based imaging, and acoustic-based solutions, WiFi technology is easy to implement and unobtrusive. This paper reviews the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, including untapped areas of research and related trends. This work aims to provide an overarching view in understanding the technology and discusses its use-cases from a perspective that considers hardware, advanced signal processing, and data acquisition. [ABSTRACT FROM AUTHOR]

This article provides a new benchmark dataset for 3-D point cloud classification in which the manually labeled human activity data exceeds 100 point clouds per frame and is capable of meeting the training needs for data-intensive learning approaches. In this study, a case study is considered for evaluating the benchmark using a deep long short-term memory (LSTM) neural network, which demonstrated a significant performance improvement over the state-of-the-art human activity recognition (HAR) area. To date, numerous types of collection devices have been used in the recognition of human activities. However, due to the scarcity of training data, the task of 3-D point cloud labeling has not yet made significant progress. To overcome this challenge, it is aimed to deduce this data requirements gap, allowing deep-learning methods to reach their full potential in 3-D point cloud tasks. The dataset used for this process is comprised of dense point clouds acquired with the static ground sensor by the NodeNs company-supported multiple input multiple output (MIMO) radar (NodeNs ZERO 60 GHz IQ radar). It contains multiple types of human being data ranging from one to four individuals and encompasses a range of human action scenarios, including standing, sitting, picking up, falling, and walking. Furthermore, it also investigated sensor locations and requirements for human being data collection that is from a single subject to multiple subjects, as well as identified and analyzed various sensing devices and applications that collect activity data. In this regard, a thorough study is conducted on several benchmark datasets, examining sensors, characteristics, activity categories, and other data. Finally, it compares and analyzes the activity recognition methods used in several benchmark datasets based on the current study. Unlike existing devices, the new NodeNs sensor provides more accessible and straightforward point cloud data to capture human movement information. Depending on an advanced detection algorithm to process point cloud data, it achieved more than 95% accuracy on the benchmark dataset. [ABSTRACT FROM AUTHOR]

This paper investigates the practical engineering problem of traffic sensors placement on stretched highways with ramps. Since it is virtually impossible to install bulky traffic sensors on each highway segment, it is crucial to find placements that result in optimized network-wide, traffic observability. Consequently, this results in accurate traffic density estimates on segments where sensors are not installed. The substantial contribution of this paper is the utilization of control-theoretic observability analysis—jointly with integer programming—to determine traffic sensor locations based on the nonlinear dynamics and parameters of traffic networks. In particular, the celebrated asymmetric cell transmission model is used to guide the placement strategy jointly with observability analysis of nonlinear dynamic systems through Gramians. Thorough numerical case studies are presented to corroborate the proposed theoretical methods and various computational research questions are posed and addressed. The presented approach can also be extended to other models of traffic dynamics. [ABSTRACT FROM AUTHOR]

Non-invasive indoor human activity detection using radio waves has attracted the interest of researchers, contributing to a range of new applications including smart healthcare. Localisation of activities can assist in developing advanced healthcare systems able to identify the location of patients. Radio frequencies have been shown in numerous studies as a non-invasive method to identify human activity. This is achieved by observing the signal propagation described in the Channel State Information (CSI). This paper presents experimental results using Universal Software-Defined Radio Peripheral (USRP) devices to identify and localise a single human subject performing activities by utilizing the CSI of radio frequencies. The experiments are carried out to retrieve CSI samples observing a single subject perform no-activity, sitting, standing, and leaning forward actions in various positions in a room. Additional CSI is captured for the subject walking in two directions across the observed area. Giving a total of 6 activities spanning the monitored area. CSI is also collected while the monitored area is empty for further comparison. Artificial intelligence is used to make classifications on collected CSI. The proposed approach uses a Super Learner (SL) algorithm that can identify the location of different activities with 96% accuracy, outperforming existing benchmark approaches. [ABSTRACT FROM AUTHOR]

State estimation (SE) in water distribution networks (WDNs), the problem of estimating all unknown network heads and flows given select measurements, is challenging due to the nonconvexity of hydraulic models and significant uncertainty from water demands, network parameters, and measurements. To this end, a probabilistic modeling for SE in WDNs is proposed. After linearizing the nonlinear hydraulic WDN model, the proposed probabilistic SE (PSE) shows that the covariance matrix of unknown system states (unmeasured heads and flows) can be linearly expressed by the covariance matrix of three uncertainty sources (i.e., measurement noise, network parameters, and water demands). Instead of providing deterministic results for unknown states, the proposed PSE approach: 1) regards the system states and uncertainty sources as random variables and yields variances of individual unknown states; 2) considers thorough modeling of various types of valves and measurement scenarios in WDNs; and 3) is also useful for uncertainty quantification, extended period simulations, and confidence limit analysis. The effectiveness and scalability of the proposed approach are tested using several WDN case studies. [ABSTRACT FROM AUTHOR]