Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.

/pdf/elderly-fall-detection-systems-a-literature-survey-9nx87469di.pdf

Elderly Fall Detection Systems: A Literature Survey

We propose a platform for the integration of passive radio sensing and vision technologies into a cloud-IoT framework that performs real-time channel quality information (CQI) time series processing and analytics. Radio sensing and vision technologies allow to passively detect and track objects or persons by using radio waves as probe signals that encode a 2-D/3-D view of the environment they propagate through. View reconstruction from the received radio signals, or CQI, is based on real-time data processing tools, that combine multiple radio measurements from possibly heterogeneous IoT networks. The proposed platform is designed to efficiently store and analyze CQI time series of different types and provides formal semantics for CQI data manipulation-ontology models (OMs). Post-processed data can be then accessible to third parties via JSON-REST calls. Finally, the proposed system supports the reconfiguration of CQI data collection based on the respective application. The performance of the proposed tools are evaluated through two experimental case studies that focus on assisted living applications in a smart-space environment and on driver behavior recognition for in-car control services. Both studies adopt and compare different CQI manipulation models and radio devices as supported by current and future (5G) standards.

/pdf/a-cloud-iot-platform-for-passive-radio-sensing-challenges-3i5ex0pj1m.pdf

A Cloud-IoT Platform for Passive Radio Sensing: Challenges and Application Case Studies

Device-free hand gesture is one of the most natural ways to interact with everyday objects. However, existing WiFi-based gesture recognition solutions are typically restricted to indoor environments due to limited outdoor coverage. Furthermore, to achieve high sampling rates, they may interfere with normal data transmissions. In this paper, we aim to develop a robust dynamic gesture interaction system that can be ubiquitously deployed using Long-term Evolution (LTE) mobile terminals. Through both empirical studies and in-depth analysis using the Fresnel zone model, we reveal the key factors that contribute to the repeatability and discernibility of gestures. We show that the optimal location and orientation to perform gestures indeed exist and can be identified without prior knowledge of the position of LTE base stations (BSs) relative to a terminal. Guided by the design principles derived from Fresnel zone characteristics around a 4G terminal, we design highly repeatable and discernible gestures with salient received signal profiles. A gesture interaction system has been developed and implemented to achieve robust recognition with this careful design. Extensive experiments have been conducted in both indoor and outdoor environments, for different relative placements of mobile terminal and BS, and with different users. The proposed system can automatically identify the direction of BSs with a median error of less than 15 degrees and achieve gesture recognition accuracy as high as 98% in all scenarios without the need to acquire any training data.

/pdf/robust-dynamic-hand-gesture-interaction-using-lte-terminals-3ajfh3qcan.pdf

Robust Dynamic Hand Gesture Interaction using LTE Terminals

Worker monitoring and protection in collaborative robot (cobots) industrial environments requires advanced sensing capabilities and flexible solutions to monitor the movements of the operator in close proximity of moving robots. Collaborative robotics is an active research area where Internet of Things (IoT) and novel sensing technologies are expected to play a critical role. Considering that no single technology can currently solve the problem of continuous worker monitoring, the article targets the development of an IoT multisensor data fusion (MDF) platform. It is based on an edge-cloud architecture that supports the combination and transformation of multiple sensing technologies to enable the passive and anonymous detection of workers. Multidimensional data acquisition from different IoT sources, signal preprocessing, feature extraction, data distribution, and fusion, along with machine learning (ML) and computing methods are described. The proposed IoT platform also comprises a practical solution for data fusion and analytics. It is able to perform opportunistic and real-time perception of workers by fusing and analyzing radio signals obtained from several interconnected IoT components, namely, a multiantenna WiFi installation (2.4–5 GHz), a sub-THz imaging camera (100 GHz), a network of radars (122 GHz) and infrared sensors (8–13 ${\mu }\text{m}$ ). The performance of the proposed IoT platform is validated through real use case scenarios inside a pilot industrial plant in which protective human–robot distance must be guaranteed considering latency and detection uncertainties.

A Multisensory Edge-Cloud Platform for Opportunistic Radio Sensing in Cobot Environments

We consider the recognition of activities from passive entities by analysing radio-frequency (RF)-channel fluctuation. In particular, we focus on the recognition of activities by active Software-defined-radio (SDR)-based Device-free Activity Recognition (DFAR) systems and investigate the localisation of activities performed, the generalisation of features for alternative environments and the distinction between walking speeds. Furthermore, we conduct case studies for Received Signal Strength (RSS)-based active and continuous signal-based passive systems to exploit the accuracy decrease in these related cases. All systems are compared to an accelerometer-based recognition system.

Leveraging RF-channel fluctuation for activity recognition: Active and passive systems, continuous and RSSI-based signal features

While most RF-sensing approaches proposed in the literature rely on short-distance indoor point-to- point instrumentations, actual large-scale installation of RF sensing suggests the use of ubiquitously available cellular systems In particular, the 5th generation of the wireless communication standard (5G) is envisioned as a universal communication means also for Internet of Things devices In this paper, we investigate environmental perception capabilities in cellular systems, with a special focus on the upcoming 5G communications standard In particular, we analyze the perception capabilities of existing cellular installations in the GSM band, which is expected to be used for 5G IoT Our instrumentation exploits a passive system capitalizing on environmental RF-noise In addition, utilizing a prototypical 5G system with 52 OFDM carriers over 1248 MHz bandwidth at 345 GHz, we consider the impact of the number and choice of channels and compare the recognition performance with acceleration-based sensing Our results in realistic settings with five subjects suggest that accurate recognition of activities and environmental situations can be a reliable implicit service of future 5G installations

/pdf/5g-ubiquitous-sensing-passive-environmental-perception-in-1gzkm5z6rd.pdf

5G Ubiquitous Sensing: Passive Environmental Perception in Cellular Systems

Structure-based discovery of conformationally selective inhibitors of the serotonin transporter

Smart cities require perception of environmental situation and peoples activities in order to trigger smart response and interaction. A seamless, non-intrusive means of environmental perception is Radio-based recognition since it promises ubiquitous reach and does not require any on-body worn devices or any form of active collaboration from the monitored subjects. Previous work has considered high-accuracy recognition from specialized equipment as well as utilization of WiFi CSI signals. However, these solutions can only generate silos within which recognition is performed, limited to building or organization scale.

Smart City Environmental Perception from Ambient Cellular Signals

A consistent effort has been made to provide fast, secure and uninterrupted mobile connectivity around the world. Mobile network operators use the private Interconnection network (IPX) to communicate with each other and with other service providers for international roaming and a large range of services. In LTE/4G, many core network nodes are involved in the communication and connection set-up for the subscriber in roaming scenarios. Currently, Diameter based protocols and the S9 interface are rolled out on the IPX network. We analyze the roaming interface (S9) in the LTE networks which is used for communicating charging, service control and QoS control signaling messages. This research explores Diameter Protocol features for the charging mechanisms and describes how manipulation in policy control and charging rules can influence the subscriber plan and services. The concept has been implemented and tested using a specification conformant LTE emulator. To mitigate the attack we will describe approaches and protection strategies that can be deployed.

Isha Singh

Papers

5G Ubiquitous Sensing: Passive Environmental Perception in Cellular Systems

Structure-based discovery of conformationally selective inhibitors of the serotonin transporter

Smart City Environmental Perception from Ambient Cellular Signals

Roaming Interface Signaling Security for LTE Networks