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

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-tofixed (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. 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. 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-theshelf (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.publishedVersio

Doppler Power Characteristics Obtained from Calibrated Channel State Information for Human Activity Recognition

Author's accepted manuscript.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.acceptedVersio

A Trajectory-Driven 3D Channel Model for Human Activity Recognition

publishedVersio

A non-stationary relay-based 3D MIMO channel model with time-variant path gains for human activity recognition in indoor environments

Extensive research showed that the physiological response of human tissue to exposure to low-frequency electromagnetic fields is the induction of an electric current in the body segments. As a result, each segment of the human body behaves as a relay, which retransmits the radio-frequency (RF) signal. To investigate the impact of this phenomenon on the Doppler characteristics of the received RF signal, we introduce a new three-dimensional (3D) non-stationary channel model to describe the propagation phenomenon taking place in an indoor environment. Here, the indoor space is equipped with a multiple-input multiple-output (MIMO) system. A single person is moving in the indoor space and is modelled by a cluster of synchronized moving point scatterers, which behave as relays. We derive the time-variant (TV) channel transfer function (CTF) with TV path gains and TV path delays. The expression of the TV path gains is obtained from the instantaneous total received power at the receiver side. This TV total received power is expressed as the product of the TV power of the RF signal initially transmitted and received by a body segment and the TV received power of the redirected signal. These TV powers are determined according the free-space path-loss model. Also, a closed-form approximate solution to the spectrogram of the TVCTF is derived. Here, we analyse the effect of the motion of the person and the validity of the relay assumption on the spectrogram, the TV mean Doppler shift (MDS), and the TV Doppler shift (DS) of the TVCTF. Simulation results are presented to illustrate the proposed channel model.publishedVersio

A Non-Stationary Channel Model for the Development of Non-Wearable Radio Fall Detection Systems

The emerging non-wearable fall detection systems rely on processing radio waves reflected off the body of the home user who has no active interaction with the system, increasing the user privacy and acceptability. This paper proposes a nonstationary channel model that is important for the development of such systems. A three-dimensional stochastic trajectory model is designed to capture targeted mobility patterns of the home user. The model is featured with a forward fall mechanism, which is actuated at a random point along the path. A transmitter emits radio waves throughout an indoor propagation environment, while a receiver collects fingerprints of the scattering objects on the emitted waves. The corresponding radio channel is modelled by a process capturing the time-variant Doppler effect caused by the home occupant. The time-frequency behaviour of the non-stationary channel is studied by computing the Doppler power spectral density and by performing spectrogram analysis. The instantaneous mean Doppler shift and Doppler spread are derived and simulated. The model is confirmed with experimental results performed at 5.9 GHz. The results are insightful for developing reliable fall detection algorithms, while the model is useful for studying the impact of different walking/falling patterns on the overall fall detection system performance.A Non-Stationary Channel Model for the Development of Non-Wearable Radio Fall Detection SystemsacceptedVersionNivå

Estimation of the Velocity of a Walking Person in Indoor Environments from mmWave Signals

accepted versionNivå

Fall Detection Using Channel State Information from WiFi Devices

Falls among the independently living elderly population are a major public health worry, leading to injuries, loss of confidence to live independently and even to death. Each year, one in three people aged 65 and older falls and one in five of them suffers fatal or non fatal injuries. Therefore, detecting a fall early and alerting caregivers can potentially save lives and increase the standard of living. Existing solutions, e.g. push-button, wearables, cameras, radar, pressure and vibration sensors, have limited public adoption either due to the requirement for wearing the device at all times or installing specialized and expensive infrastructure. In this thesis, a device-free, low cost indoor fall detection system using commodity WiFi devices is presented. The system uses physical layer Channel State Information (CSI) to detect falls. Commercial WiFi hardware is cheap and ubiquitous and CSI provides a wealth of information which helps in maintaining good fall detection accuracy even in challenging environments. The goals of the research in this thesis are the design, implementation and experimentation of a device-free fall detection system using CSI extracted from commercial WiFi devices. To achieve these objectives, the following contributions are made herein. A novel time domain human presence detection scheme is developed as a precursor to detecting falls. As the next contribution, a novel fall detection system is designed and developed. Finally, two main enhancements to the fall detection system are proposed to improve the resilience to changes in operating environment. Experiments were performed to validate system performance in diverse environments. It can be argued that through collection of real world CSI traces, understanding the behavior of CSI during human motion, the development of a signal processing tool-set to facilitate the recognition of falls and validation of the system using real world experiments significantly advances the state of the art by providing a more robust fall detection scheme

Channel State Information from pure communication to sense and track human motion: A survey

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology

Wi-Fi Sensing: Applications and Challenges

Wi-Fi technology has strong potentials in indoor and outdoor sensing applications, it has several important features which makes it an appealing option compared to other sensing technologies. This paper presents a survey on different applications of Wi-Fi based sensing systems such as elderly people monitoring, activity classification, gesture recognition, people counting, through the wall sensing, behind the corner sensing, and many other applications. The challenges and interesting future directions are also highlighted