Signs of life detection using wireless passive radar

Non-contact devices for monitoring signs of life have attracted a lot of attention in recent years for applications in security, emergency and disaster situations. Current devices however, generally utilize bespoke active systems to transmit large bandwidth signals. In this paper, a real-Time phase extraction method based on passive Wi-Fi radar is proposed for detecting the chest movements associated with a person breathing. Since the monitored movements are of low amplitude and small Doppler shift, this method uses the phase variation rather than traditional range-Doppler processing. The processing is based on time domain cross correlation, with the addition of a Hampel filter for outlier detection and removal. In this paper the basic passive Wi-Fi model and limitations of traditional cross ambiguity function for signs of life detection are first introduced. The phase extraction method is then described followed by experimental results and analysis. Detection of breathing for a stationary person is shown in both in-room and through wall scenarios using both the Wi-Fi beacon and data transmissions. This is believed to be the first demonstration of signs of life detection using phase extraction in passive radar and extends the capability of such systems into a wide range of new applications

Respiration and Activity Detection based on Passive Radio Sensing in Home Environments

The pervasive deployment of connected devices in modern society has significantly changed the nature of the wireless landscape, especially in the license free industrial, scientific and medical (ISM) bands. This paper introduces a deep learning enabled passive radio sensing method that can monitor human respiration and daily activities through leveraging unplanned and ever-present wireless bursts in the ISM frequency band, and can be employed as an additional data input within healthcare informatics. Wireless connected biomedical sensors (Medical Things) rely on coding and modulating of the sensor data onto wireless (radio) bursts which comply with specific physical layer standards like 802.11, 802.15.1 or 802.15.4. The increasing use of these unplanned connected sensors has led to a pell-mell of radio bursts which limit the capacity and robustness of communication channels to deliver data, whilst also increasing inter-system interference. This paper presents a novel methodology to disentangle the chaotic bursts in congested radio environments in order to provide healthcare informatics. The radio bursts are treated as pseudo noise waveforms which eliminate the requirement to extract embedded information through signal demodulation or decoding. Instead, we leverage the phase and frequency components of these radio bursts in conjunction with cross ambiguity function (CAF) processing and a Deep Transfer Network (DTN). We use 2.4GHz 802.11 (WiFi) signals to demonstrate experimentally the capability of this technique for human respiration detection (including through-the-wall), and classifying everyday but complex human motions such as standing, sitting and falling

Awireless passive radar system for real-time through-wall movement detection

In this paper, a reconfigurable real-time passive wireless detection system is described. The system is based on software-defined radio (SDR) architecture. The signal processing method and processing flow that enable through-wall target detection are introduced. The high-speed noise and interference mitigation methods implemented in the system for through-wall target detection are also described. A series of experimental results are presented for both large and small human body movements in through-wall scenarios. It is shown that the high-resolution Doppler event history implemented in the system enables the system to recognize and distinguish a range of body movements. The results demonstrate that this real-time SDR-based wireless detection system is a low-cost solution for human movement and recognition, with a range of applications

A real-time high resolution passive WiFi Doppler-radar and its applications

The design and implementation of a real-time passive high Doppler resolution radar system is described in this paper. Batch processing and pipelined processing flow are introduced for reducing the processing time to enable real-time display. The proposed method is implemented on a software defined radio (SDR) platform. Two experiments using this system are described: one based on small human body motions and another one on hand gesture detection. The results from these experiments show that the proposed system can be used in a range of application scenarios such as eHealth, human-machine interaction and high accuracy indoor target tracking

Doppler based detection of multiple targets in passive WiFi radar using undetermined blind source separation

Passive approaches for detecting and localizing people in wireless environments have attracted significant attention because of its diverse application in healthcare, security and robotics in recent years. However, within indoor environments multiple people moving in close proximity to each other often impedes the utility of such approaches. In this paper we present a new method for identifying multiple human targets in Wi-Fi passive radar systems using only a single receive channel to detect Doppler returns. The technique is based on tree-structure sparse underdetermined blind source separation and utilizes proximal alternating methods in a convex optimization field. Firstly, we show proof-of-principle simulation results for two targets moving within a typical indoor scenario and compare the results with those from the well-known independent component analysis (ICA). Secondly, we validate the simulation outputs using real-world experimental data. The results demonstrate the effectiveness of the proposed technique for device-free detection of multiple targets in the indoor wireless landscape

Migration Patterns, Use of Stopover Areas, and Austral Summer Movements of Swainson\u27s Hawks

From 1995 to 1998, we tracked movements of adult Swainson’s Hawks (Buteo swainsoni), using satellite telemetry to characterize migration, important stopover areas, and movements in the austral summer. We tagged 46 hawks from July to September on their nesting grounds in seven U.S. states and two Canadian provinces. Swainson’s Hawks followed three basic routes south on a broad front, converged along the east coast of central Mexico, and followed a concentrated corridor to a communal area in central Argentina for the austral summer. North of 20°N, southward and northward tracks differed little for individuals from east of the continental divide but differed greatly (up to 1700 km) for individuals from west of the continental divide. Hawks left the breeding grounds mid-August to mid-October; departure dates did not differ by location, year, or sex. Southbound migration lasted 42 to 98 days, northbound migration 51 to 82 days. Southbound, 36% of the Swainson’s Hawks departed the nesting grounds nearly 3 weeks earlier than the other radio-marked hawks and made stopovers 9.0–26.0 days long in seven separate areas, mainly in the southern Great Plains, southern Arizona and New Mexico, and northcentral Mexico. The birds stayed in their nonbreeding range for 76 to 128 days. All used a core area in central Argentina within 23% of the 738 800-km2 austral summer range, where they frequently moved long distances (up to 1600 km). Conservation of Swainson’s Hawks must be an international effort that considers habitats used during nesting and non-nesting seasons, including migration stopovers

Assessing the safety of physical rehabilitation in critically ill patients: a Delphi study

Background Physical rehabilitation of critically ill patients is implemented to improve physical outcomes from an intensive care stay. However, before rehabilitation is implemented, a risk assessment is essential, based on robust safety data. To develop this information, a uniform definition of relevant adverse events is required. The assessment of cardiovascular stability is particularly relevant before physical activity as there is uncertainty over when it is safe to start rehabilitation with patients receiving vasoactive drugs. Methods A three-stage Delphi study was carried out to (a) define adverse events for a general ICU cohort, and (b) to define which risks should be assessed before physical rehabilitation of patients receiving vasoactive drugs. An international group of intensive care clinicians and clinician researchers took part. Former ICU patients and their family members/carers were involved in generating consensus for the definition of adverse events. Round one was an open round where participants gave their suggestions of what to include. In round two, participants rated their agreements with these suggestions using a five-point Likert scale; a 70% consensus agreement threshold was used. Round three was used to re-rate suggestions that had not reached consensus, whilst viewing anonymous feedback of participant ratings from round two. Results Twenty-four multi-professional ICU clinicians and clinician researchers from 10 countries across five continents were recruited. Average duration of ICU experience was 18 years (standard deviation 8) and 61% had publications related to ICU rehabilitation. For the adverse event definition, five former ICU patients and one patient relative were recruited. The Delphi process had a 97% response rate. Firstly, 54 adverse events reached consensus; an adverse event tool was created and informed by these events. Secondly, 50 risk factors requiring assessment before physical rehabilitation of patients receiving vasoactive drugs reached consensus. A second tool was created, informed by these suggestions. Conclusions The adverse event tool can be used in studies of physical rehabilitation to ensure uniform measurement of safety. The risk assessment tool can be used to inform clinical practise when risk assessing when to start rehabilitation with patients receiving vasoactive drugs. Trial registration This study protocol was retrospectively registered on https://www.researchregistry.com/ (researchregistry2991)