Sanaz Kianoush

Device-Free Radio Vision for Assisted Living: Leveraging wireless channel quality information for human sensing

Wireless propagation is conventionally considered as the enabling tool for transporting information in digital communications. However, recent research has shown that the perturbations of the same electromagnetic (EM) fields that are adopted for data transmission can be used as a powerful sensing tool for device-free radio vision. Applications range from human body motion detection and localization to passive gesture recognition. In line with the current evolution of mobile phone sensing [1], radio terminals are not only ubiquitous communication interfaces, but they also incorporate novel or augmented sensing potential, capable of acquiring an accurate human-scale understanding of space and motion. This article shows how radio-frequency (RF) signals can be employed to provide a device-free environmental vision and investigates the detection and tracking capabilities for potential benefits in daily life.

Leveraging RF signals for human sensing: Fall detection and localization in human-machine shared workspaces

Safe human-machine interactions promote high flexibility in collaborative workspaces. Fall detection and localization of the operator are major issues in ensuring a safe working environment. However, many proposed solutions are not applicable for deployment in industrial environments due to their performance limitations in practical contexts. In this paper, we propose an integrated framework for both localization and fall detection of operators inside a shared workspace that employs radio-frequency (RF) signal analysis in real-time. Multipath and non-line-of-sight (NLOS) scattering that affect RF signal propagation can be leveraged for human sensing in complex workspaces: the proposed system continuously monitors the fluctuations of the RF field across the space by a dense network of WiFi compliant radio devices operating at 2.4GHz. To increase the accuracy of the localization system, a sensor fusion algorithm using Extended Kalman Filter techniques is employed. The proposed method may be used for integrating measurements from both RF nodes and an additional image-based system. For fall detection, a Hidden Markov Model is applied to discern different postures of the operator and to detect a fall event by tracking the fluctuations of the wireless signal quality. Fall detector performances are validated through experimental measurements. The preliminary results confirm the effectiveness of the proposed approach for different body configurations and pre-impact postures to correctly detect a fall event. Finally, some results about sensor fusion for improved operator localization are presented.

Location based routing protocol exploiting heterogeneous primary users in cognitive radio networks

In cognitive radio networks (CRNs), knowledge of the primary users (PUs) position can be used to avoid harmful interference to the primary network, while at the same time be exploited to improve CR performance. In this paper, a localization algorithm is developed to calculate PUs position and a novel location based CR (LCR) routing protocol is proposed that has the following properties: (i) it considers the existence of heterogeneous PUs, (ii) exploits PUs location information, (iii) jointly selects spectrum and route, (iv) protects PUs from interference. Clusters of CRs are defined according to the spectral characteristics in a given location area, and the LCR routing protocol acts in two steps: intra-cluster and inter-cluster. Simulations are conducted in terms of CR end-to-end performance and PUs collision risk. Results reveal the importance of formulating routing protocol in terms of PU protection, which is a unique features in CR networks.

Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces

Fall detection and localization of human operators inside a workspace are major issues in ensuring a safe working environment. Recent research has shown that the perturbations of the radio-frequency (RF) signals commonly adopted for wireless communications can also be used as sensing tools for device-free human motion detection. Device-free RF-based human sensing applications range from tag-less body localization to detection and monitoring of human well-being (e-Health). In this paper, we propose a real-time system for human body motion sensing with special focus on joint body localization and fall detection. The proposed system continuously monitors and processes the RF signals emitted by industry-compliant radio devices operating in the 2.4 GHz ISM band and supporting machine-to-machine communication functions. Human-induced diffraction and multipath phenomena that affect RF signal propagation are leveraged for body localization while for fall detection a hidden Markov model is applied to discern different postures of the operator and to detect safety-relevant events by tracking the received signal strength indicator footprints. Fall detection performances are corroborated by extensive experimental measurements in different settings. In addition, we propose also a sensor fusion tool that is able to integrate the device-free RF-based sensing system within an industrial image sensors framework. Preliminary results, conducted during field trial measurements, confirm the effectiveness of the proposed approach in terms of localization accuracy, and sensitivity/specificity to correctly detect a fall event from preimpact postures.

Pre-deployment performance assessment of device-free radio localization systems

Device-free localization (DFL) systems have emerged in the last years as a powerful technology for tracking mobile targets without the need of radio tags. Perturbations induced by moving objects on the electromagnetic (EM) wavefield generated by a dense wireless network are measured and processed by the DFL system to track target trajectories. Despite several solutions have been explored in the literature, mainly based on fingerprinting approaches, a deep understanding of body-induced effects on the EM fields for target tracking is still missing as well as reliable predictive models for pre-deployment accuracy assessment of DFL systems. The paper makes a first attempt towards the definition and validation of a novel predictive tool that is general enough to be applied to DFL systems with any kind of RF interface, network topology and connectivity degree. An analytical diffraction model is exploited to predict the effect of a human body on the received signal strength field over all the available links and compute fundamental limits to the DFL positioning accuracy. The proposed tool is tailored for 2D human body localization and validated by experimental trials in an indoor environment.

Is someone moving around my cell-phone? Tracing cellular signals for passive motion detection

This paper explores the possibility of turning the cellular radio modem into a passive body motion sensor. Sensing is herein based on the real-time analysis of cellular radio propagation. Unlike WiFi and Bluetooth, all cellular radios are never disconnected as, even in idle mode state, they perform continuous and autonomous measurements of the propagation conditions, namely the cellular signal quality (CSQ). CSQ is constantly updated while searching for any opportunity to reselect a new camped cell and being responsive to paging operations. Body movements in the surrounding of a cellular device are responsible of small but characteristic variations of the CSQ dynamics that might also trigger new reselections. Preliminary experiments are presented based on commercial off-the-shelf (COTS) smart-phone devices. The use of data analytics tools applied to cellular signals is a new topic that has the potential of opening up new research opportunities.

A cooperative localization algorithm exploiting a mobile device in cognitive radio networks

In cognitive radio networks (CRNs), the awareness of the environment is fundamental to efficiently avoid harmful interference toward primary users (PUs) and to improve cognitive radio (CR) performance. Thus, estimating the PUs position, rather than just knowing its presence through traditional spectrum sensing, becomes a key challenge. In this paper a novel cooperative localization algorithm (CLA) that uses few CRs is proposed to estimate PU position in harsh channel condition. CLA algorithm exploits a mobile CR (MCR) with unknown positions that collaborates with other fixed CRs to estimate PU position. To this purpose, the MCR exploits the signal strength measurements obtained from PUs through an energy-detection based spectrum sensing. Since channel parameters are not available in CRNs, a channel estimation technique is also applied. Simulations are conducted to evaluate the performance of the CLA in comparison to existing cooperative and non-cooperative localization algorithms. The MCR path is generated by the random waypoint model (RWP) and a multichannel scenario with shadow fading is implemented. Results show that the CLA outperforms the existing algorithms, due to its moving path independency and its higher localization accuracy. Moreover, simulation results confirm that CLA is robust to noisy channel conditions.

Tracking of frequency selectivity for device-free detection of multiple targets

Device-free localization (DFL) technology exploits target-induced fading over machine-type wireless links for passive object localization and tracking. In most cases, single frequency measurements are employed for DFL systems. However, machine-type communication protocols often employ slotted or time-division hopping policies over multiple channels, i.e. frequencies. In this paper, we propose a DFL system designed to extract and track the locations of the targets that are hidden into multi-frequency received signal strength (RSS) measurements. Due to strong multipath phenomena and in-band interferences, the use of some noisy channels might worsen the localization accuracy. A statistical model is thus proposed to relate RSS values obtained at various frequencies to the target location. Two different approaches based on optimal frequency selection and subspace decomposition are then proposed to identify the frequency measurements that maximize the localization accuracy. Experimental validation in an indoor multipath-limited environment reveals that the approach can improve the accuracy of both single and double target detection and localization with respect to the conventional single channel DFL approach.

A dynamic Bayesian network approach for device-free radio vision: Modeling, learning and inference for body motion recognition

In this paper, a time-varying dynamic Bayesian network model is shown to describe human-induced RF fluctuations for the purpose of non-cooperative and device-free radiobased body motion recognition (radio vision). The technology relies on pre-existing wireless communication network infrastructures and processes channel quality information (CQI) for human-scale sensing. Body movements leave a characteristic footprint on the CQI sequences collected during consecutive radio transmissions over multiple co-located links. Body-induced RF footprints are proved to be effectively characterized by temporarily coupled hidden Markov chains: abrupt changes of body postures make CQIs observed over co-located links temporarily coupled while being uncoupled for slow body movements. Learning and classification/inference problems are discussed based on experimental measurements. Device-free radio vision performances are evaluated for arm gesture and fall detection applications.

Towards a factory-of-things: Channel modeling and deployment assessment in PetroEcuador Esmeraldas oil refinery

Industry 4.0 and industrial Internet of Things (iIoT) trends are pushing towards the transformation of factories to provide more flexible production systems through the use of wireless networks. Technologies enabling the “Factory-of-Things” (FoT) paradigm allow the safe deployment of wireless field devices in industrial plants thanks to their low-battery usage that makes the maintenance cycle quite low, and highly reliable. The widespread adoption of these technologies should be paired with tools for predeployment network design and prediction of the wireless link quality to mimic the planning procedures applied to conventional industrial wired equipment. In factory sites, the strength of the radio signals is impaired by frequency, spatial and time-domain fading that influence the wireless link stability. In this paper, based on an extensive measurement campaign performed inside an active oil refinery, we propose and validate a novel channel model tailored for industrial wireless networks operating over 2.4 GHz and supporting a time-slotted channel hopping (TSCH) policy. Post-layout network performance verification has been finally carried out based on a WirelessHART industry standard system deployed in selected sites.