103 research outputs found
MEDUSA: Scalable Biometric Sensing in the Wild through Distributed MIMO Radars
Radar-based techniques for detecting vital signs have shown promise for
continuous contactless vital sign sensing and healthcare applications. However,
real-world indoor environments face significant challenges for existing vital
sign monitoring systems. These include signal blockage in non-line-of-sight
(NLOS) situations, movement of human subjects, and alterations in location and
orientation. Additionally, these existing systems failed to address the
challenge of tracking multiple targets simultaneously. To overcome these
challenges, we present MEDUSA, a novel coherent ultra-wideband (UWB) based
distributed multiple-input multiple-output (MIMO) radar system, especially it
allows users to customize and disperse the into sub-arrays.
MEDUSA takes advantage of the diversity benefits of distributed yet wirelessly
synchronized MIMO arrays to enable robust vital sign monitoring in real-world
and daily living environments where human targets are moving and surrounded by
obstacles. We've developed a scalable, self-supervised contrastive learning
model which integrates seamlessly with our hardware platform. Each attention
weight within the model corresponds to a specific antenna pair of Tx and Rx.
The model proficiently recovers accurate vital sign waveforms by decomposing
and correlating the mixed received signals, including comprising human motion,
mobility, noise, and vital signs. Through extensive evaluations involving 21
participants and over 200 hours of collected data (3.75 TB in total, with 1.89
TB for static subjects and 1.86 TB for moving subjects), MEDUSA's performance
has been validated, showing an average gain of 20% compared to existing systems
employing COTS radar sensors. This demonstrates MEDUSA's spatial diversity gain
for real-world vital sign monitoring, encompassing target and environmental
dynamics in familiar and unfamiliar indoor environments.Comment: Preprint. Under Revie
Enhancing system performance through objective feature scoring of multiple persons' breathing using non-contact RF approach
Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system’s performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system’s performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively
SoK: Inference Attacks and Defenses in Human-Centered Wireless Sensing
Human-centered wireless sensing aims to understand the fine-grained
environment and activities of a human using the diverse wireless signals around
her. The wireless sensing community has demonstrated the superiority of such
techniques in many applications such as smart homes, human-computer
interactions, and smart cities. Like many other technologies, wireless sensing
is also a double-edged sword. While the sensed information about a human can be
used for many good purposes such as enhancing life quality, an adversary can
also abuse it to steal private information about the human (e.g., location,
living habits, and behavioral biometric characteristics). However, the
literature lacks a systematic understanding of the privacy vulnerabilities of
wireless sensing and the defenses against them.
In this work, we aim to bridge this gap. First, we propose a framework to
systematize wireless sensing-based inference attacks. Our framework consists of
three key steps: deploying a sniffing device, sniffing wireless signals, and
inferring private information. Our framework can be used to guide the design of
new inference attacks since different attacks can instantiate these three steps
differently. Second, we propose a defense-in-depth framework to systematize
defenses against such inference attacks. The prevention component of our
framework aims to prevent inference attacks via obfuscating the wireless
signals around a human, while the detection component aims to detect and
respond to attacks. Third, based on our attack and defense frameworks, we
identify gaps in the existing literature and discuss future research
directions
Inferring Cognitive Load using Wireless Signals
From not disturbing a focused programmer, to entertaining a restless commuter waiting for a train, ubiquitous computing devices could greatly enhance their interaction with humans, should these devices only be aware of the user's cognitive load. However, current means of assessing cognitive load are, with a few exceptions, based on intrusive methods requiring physical contact of the measurement equipment and the user. In this thesis we propose Wi-Mind, a system for remote cognitive load assessment through wireless sensing. Wi-Mind is based on a software-defined radio-based radar that measures sub-millimeter movements related to a person's breathing and heartbeats, which, in turn allow us to infer the person's cognitive load. We built the system and tested it with 23 volunteers being engaged in different tasks. Results show that while Wi-Mind manges to detect whether one is engaged in a cognitively demanding task, the inference of the exact cognitive load level remains challenging
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