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Radiobølger som fanger menneskelig aktivitet

Ahmed Abdelmonem Abdelgawwad fra Fakultet for teknologi og realfag ved UiA disputerer for ph.d.-graden med avhandlingen «Synthetic Micro-Doppler Signatures of Non-Stationary Channels for the Design of Human Activity Recognition Systems» mandag 16. august 2021. (Foto: Privat)

The outcomes of this dissertation pave the way towards simulation-based human activity recognition systems.

Ahmed Abdelmonem Abdelgawwad

Ph.d.-kandidat

Ahmed Abdelmonem Abdelgawwad fra Fakultet for teknologi og realfag ved UiA disputerer for ph.d.-graden med avhandlingen «Synthetic Micro-Doppler Signatures of Non-Stationary Channels for the Design of Human Activity Recognition Systems» mandag 16. august 2021.

Han har fulgt doktorgradsprogrammet ved Fakultet for teknologi og realfag ved UiA, med spesialisering i IKT.

Ahmed Abdelmonem Abdelgawwad har forsket på hvordan radiobølger kan følge med på om for eksempel mennesker beveger seg i en bolig, uten at mennesket bærer en sender. Eller om gjenstander beveger seg, for den del.

Et praktisk anvendelsesområde er eldre som bor alene, der signalene kan lese om den eldre beveger seg eller har falt på gulvet. Radiobølgene identifiserer micro-doppler-signaturer - det vil si at de identifiserer og skiller bevegelser fra hverandre. Det kan gjøres gjennom vegger.

Slik oppsummerer kandidaten selv avhandlingen:

Synthetic Micro-Doppler Signatures of Non-Stationary Channels for the Design of Human Activity Recognition Systems

The research interest in human activity recognition (HAR) to provide ambient assisted living (AAL) for elders living alone has increased.

As a result, several HAR systems have been proposed. The categories of HAR systems fall into two types: wearable and non-wearable systems.

Radio-frequency (RF)-based HAR systems are considered among the non-wearable types. They do not violate the users’ privacy. Moreover, they do not require to be worn by the user. Furthermore, they are capable of sensing human activities through walls of buildings. That is why they catch more interest nowadays.

Captures non-stationary behavior

RF-based HAR systems capture the complex non-stationary behaviors of indoor channels influenced by human activities.

Micro-Doppler signatures and time variant mean Doppler shifts (TV-MDSs) are considered among the extracted features used to train the human activity classifiers (HACs).

The current approach is to train HACs by using measured RF-sensing data. The collection of such measured data is expensive and time-consuming. Furthermore, the collected data is not reproducible. An alternative approach is to generate the micro-Doppler signatures and the TV-MDSs of non-stationary channel simulation models for training the HACs.

The main aim of this dissertation is to generate synthetic micro-Doppler signatures and TV-MDSs to train the HACs. This is achieved by developing non-stationary fixed-to-fixed (F2F) indoor channel models. Such models provide an in-depth understanding of the channel parameters that influence the micro-Doppler signatures and TV-MDSs. Hence, the proposed non-stationary channel models help to generate the micro-Doppler signatures and the TV-MDSs, which fit those of the collected measurement data.

From 2D to 3D F2F channel models

First, we start with a simple two-dimensional (2D) non-stationary F2F channel model with fixed and moving scatterers. Such a model assumes that the moving scatterers are moving in 2D geometry with simple time variant (TV) trajectories and they have the same height as the transmitter and the receiver antennas. The model of the Doppler shifts caused by the moving scatterers in 2D space is provided. The micro-Doppler signature of this model is explored by employing the spectrogram of which a closed-form expression is derived. Moreover, we demonstrate how the TV-MDSs can be computed from the spectrograms.

The aforementioned model is extended to provide two three-dimensional (3D) nonstationary F2F channel models. Such models allow simulating the micro-Doppler signatures influenced by the 3D trajectories of human activities, such as walking and falling. Moreover, expressions of the trajectories of these human activities are also given. Approximate solutions of the spectrograms of these channels are provided by approximating the Doppler shifts caused by the human activities into linear piecewise functions of time. The impact of these activities on the micro-Doppler signatures and the TV-MDSs of the simulated channel models is explored.

Measured channels included

The work done in this dissertation is not limited to analyzing micro-Doppler signatures and the TV-MDSs of the simulated channel models, but also includes those of the measured channels.

The channel-state-information (CSI) software tool installed on commercial-off-the-shelf (COTS) devices is utilized to capture complex channel transfer function (CTF) data under the influence of human activities. To mitigate the TV phase distortions caused by the clock asynchronization between the transmitter and receiver stations, a back-to-back (B2B) connection is employed. Models of the measured CTF and its true phases are also shown.

The true micro-Doppler signatures and TV-MDSs of the measured CTF are analyzed. The results showed that the CSI tool is reliable to validate the proposed channel models. This allows the micro-Doppler signatures and the TV-MDSs extracted from the data collected with this tool to be used to train the HACs.

Two non-stationary CSI models

Inertial measurement units (IMUs) can be used to capture the trajectories of moving objects or human activities.

In this dissertation, two IMU-driven non-stationary CSI models are presented. Such models can be fed with trajectories collected by the IMUs to simulate realistic micro-Doppler signatures and the TV-MDSs.

The aim of the first (second) model is to capture the influence of the trajectory of a rigid body (moving human) on the micro-Doppler signatures and the TV-MDSs of CSI channels. Frameworks are proposed for processing the IMU data to compute the trajectories and feed them to the CSI model to simulate the micro-Doppler signatures and the TV-MDSs. Both of IMU data and CSI data are recorded simultaneously to validate the proposed channel models. Then, the micro-Doppler signatures and the TV-MDSs of the IMU-driven models and CSI data are evaluated.

The results show that there are strong agreements between the micro-Doppler signatures and the TV-MDSs of the IMU-driven model and the measured CSI.

The outcomes of this dissertation pave the way towards simulation-based HAR systems.

Disputasfakta:

Prøveforelesning og disputas finner sted digitalt i konferanseprogrammet Zoom:

Disputasen blir ledet av dekan Michael Rygaard Hansen, Fakultet for teknologi og realfag, Universitetet i Agder.

Prøveforelesning kl 10:15

Disputas kl 12:15

Oppgitt emne for prøveforelesning«Contact-Free Human RF-Sensing: Basic Principles, Challenges, and Solutions»

Tittel på avhandling«Synthetic Micro-Doppler Signatures of Non-Stationary Channels for the Design of Human Activity Recognition Systems»

Søk etter avhandlingen i AURA - Agder University Research Archive, som er et digitalt arkiv for vitenskapelige artikler, avhandlinger og masteroppgaver fra ansatte og studenter ved Universitetet i Agder. AURA blir jevnlig oppdatert.

Avhandlingen er tilgjengelig her:

https://hdl.handle.net/11250/2767221

 

KandidatenAhmed Abdelmonem Abdelgawwad (1991, Minya, Egypt) Bachelorgrad (2013) og mastergrad (2015) i Information engineering and technology fra the German University in Cairo (GUC). "The master’s degree was related to the complexity reduction of 3D ray-tracing for indoor coverage solutions. His research interests include network modeling and simulation, channel modeling for fall detection systems, non-stationary channel models, and time-frequency analysis for non-stationary channel models". I dag arbeider han i STRYDE i Asker, Norge - se linkedIn.

Opponenter:

Førsteopponent: Professor Klaus David, Universität Kassel, Tyskland 

Annenopponent: Førsteamanuensis Stephan Sigg, Aalto-universitetet, Finland 

Bedømmelseskomitéen er ledet av professor Christian Omlin, Institutt for informasjons- og kommunikasjonsteknologi, Universitetet i Agder

Veiledere i doktorgradsarbeidet var professor Matthias Pätzold, UiA (hovedveileder) og førsteamanuensis Bjørn Olav Hogstad, NTNU (medveileder)

Opponent ex auditorio:

Disputasleder inviterer til spørsmål ex auditorio i innledningen i disputasen, med tidsfrister. Det er en forutsetning at opponenten har lest avhandlingen. Disputasleders e-post er tilgjengelig i chat-funksjonen under disputasen. Spørsmål om ex auditorio kan sendes til disputasleder Michael Rygaard Hansen på e-post michael.r.hansen@uia.no