1,506 research outputs found
Anti-Fall: A Non-intrusive and Real-time Fall Detector Leveraging CSI from Commodity WiFi Devices
Fall is one of the major health threats and obstacles to independent living
for elders, timely and reliable fall detection is crucial for mitigating the
effects of falls. In this paper, leveraging the fine-grained Channel State
Information (CSI) and multi-antenna setting in commodity WiFi devices, we
design and implement a real-time, non-intrusive, and low-cost indoor fall
detector, called Anti-Fall. For the first time, the CSI phase difference over
two antennas is identified as the salient feature to reliably segment the fall
and fall-like activities, both phase and amplitude information of CSI is then
exploited to accurately separate the fall from other fall-like activities.
Experimental results in two indoor scenarios demonstrate that Anti-Fall
consistently outperforms the state-of-the-art approach WiFall, with 10% higher
detection rate and 10% less false alarm rate on average.Comment: 13 pages,8 figures,corrected version, ICOST conferenc
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
Towards a Robust WiFi-based Fall Detection with Adversarial Data Augmentation
Recent WiFi-based fall detection systems have drawn much attention due to
their advantages over other sensory systems. Various implementations have
achieved impressive progress in performance, thanks to machine learning and
deep learning techniques. However, many of such high accuracy systems have low
reliability as they fail to achieve robustness in unseen environments. To
address that, this paper investigates a method of generalization through
adversarial data augmentation. Our results show a slight improvement in deep
learning-systems in unseen domains, though the performance is not significant.Comment: Will appear in Proceedings of the 54th Annual Conference on
Information Sciences and Systems (CISS2020
A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition
Despite recent advances, fast and reliable Human Activity Recognition in confined space is still an open problem related to many real-world applications, especially in health and biomedical monitoring. With the ubiquitous presence of Wi-Fi networks, the activity recognition and classification problems can be solved by leveraging some characteristics of the Channel State Information of the 802.11 standard. Given the well-documented advantages of Deep Learning algorithms in solving complex pattern recognition problems, many solutions in Human Activity Recognition domain are taking advantage of those models. To improve the time and precision of activity classification of time-series data stemming from Channel State Information, we propose herein a fast deep neural model encompassing concepts not only from state-of-the-art recurrent neural networks, but also using convolutional operators with added randomization. Results from real data in an experimental environment show promising results
FarSense: pushing the range limit of WiFi-based respiration sensing with CSI ratio of two antennas
International audienceThe past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense-the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. 1 We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications
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
Traffic characteristics mechanism for detecting rogue access point in local area network
Rogue Access Point (RAP) is a network vulnerability involving illicit usage of wireless access point in a network environment. The existence of RAP can be identified using network traffic inspection. The purpose of this thesis is to present a study on the use of local area network (LAN) traffic characterisation for typifying wired and wireless network traffic through examination of packet exchange between sender and receiver by using inbound packet capturing with time stamping to indicate the existence of a RAP. The research is based on the analysis of synchronisation response (SYN/ACK), close connection respond (FIN/ACK), push respond (PSH/ACK), and data send (PAYLOAD) of the provider’s flags which are paired with their respective receiver acknowledgment (ACK). The timestamp of each pair is grouped using the
Equal Group technique, which produced group means. These means were then categorised into three zones to form zone means. Subsequently, the zone means were used to generate a global mean that served as a threshold value for identifying RAP. A network testbed was developed from which real network traffic was captured and analysed. A mechanism to typify wired and wireless LAN traffic using the analysis of the global mean used in the RAP detection process has been proposed. The research calculated RAP detection threshold value of 0.002 ms for the wired IEEE 802.3 LAN, while wireless IEEE 802.11g is 0.014 ms and IEEE 802.11n is 0.033 ms respectively. This study has contributed a new mechanism for detecting a RAP through traffic characterisation by examining packet communication in the LAN environment. The
detection of RAP is crucial in the effort to reduce vulnerability and to ensure integrity
of data exchange in LA
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