2,936 research outputs found
An inclusive survey of contactless wireless sensing: a technology used for remotely monitoring vital signs has the potential to combating COVID-19
With the Coronavirus pandemic showing no signs of abating, companies and governments around the world are spending millions of dollars to develop contactless sensor technologies that minimize the need for physical interactions between the patient and healthcare providers. As a result, healthcare research studies are rapidly progressing towards discovering innovative contactless technologies, especially for infants and elderly people who are suffering from chronic diseases that require continuous, real-time control, and monitoring. The fusion between sensing technology and wireless communication has emerged as a strong research candidate choice because wearing sensor devices is not desirable by patients as they cause anxiety and discomfort. Furthermore, physical contact exacerbates the spread of contagious diseases which may lead to catastrophic consequences. For this reason, research has gone towards sensor-less or contactless technology, through sending wireless signals, then analyzing and processing the reflected signals using special techniques such as frequency modulated continuous wave (FMCW) or channel state information (CSI). Therefore, it becomes easy to monitor and measure the subject’s vital signs remotely without physical contact or asking them to wear sensor devices. In this paper, we overview and explore state-of-the-art research in the field of contactless sensor technology in medicine, where we explain, summarize, and classify a plethora of contactless sensor technologies and techniques with the highest impact on contactless healthcare. Moreover, we overview the enabling hardware technologies as well as discuss the main challenges faced by these systems.This work is funded by the scientific and technological research council of Turkey (TÜBITAK) under grand 119E39
MUSE-Fi: Contactless MUti-person SEnsing Exploiting Near-field Wi-Fi Channel Variation
Having been studied for more than a decade, Wi-Fi human sensing still faces a
major challenge in the presence of multiple persons, simply because the limited
bandwidth of Wi-Fi fails to provide a sufficient range resolution to physically
separate multiple subjects. Existing solutions mostly avoid this challenge by
switching to radars with GHz bandwidth, at the cost of cumbersome deployments.
Therefore, could Wi-Fi human sensing handle multiple subjects remains an open
question. This paper presents MUSE-Fi, the first Wi-Fi multi-person sensing
system with physical separability. The principle behind MUSE-Fi is that, given
a Wi-Fi device (e.g., smartphone) very close to a subject, the near-field
channel variation caused by the subject significantly overwhelms variations
caused by other distant subjects. Consequently, focusing on the channel state
information (CSI) carried by the traffic in and out of this device naturally
allows for physically separating multiple subjects. Based on this principle, we
propose three sensing strategies for MUSE-Fi: i) uplink CSI, ii) downlink CSI,
and iii) downlink beamforming feedback, where we specifically tackle signal
recovery from sparse (per-user) traffic under realistic multi-user
communication scenarios. Our extensive evaluations clearly demonstrate that
MUSE-Fi is able to successfully handle multi-person sensing with respect to
three typical applications: respiration monitoring, gesture detection, and
activity recognition.Comment: 15 pages. Accepted by ACM MobiCom 202
Consumer-facing technology fraud : economics, attack methods and potential solutions
The emerging use of modern technologies has not only benefited society but also attracted fraudsters and criminals to misuse the technology for financial benefits. Fraud over the Internet has increased dramatically, resulting in an annual loss of billions of dollars to customers and service providers worldwide. Much of such fraud directly impacts individuals, both in the case of browser-based and mobile-based Internet services, as well as when using traditional telephony services, either through landline phones or mobiles. It is important that users of the technology should be both informed of fraud, as well as protected from frauds through fraud detection and prevention systems. In this paper, we present the anatomy of frauds for different consumer-facing technologies from three broad perspectives - we discuss Internet, mobile and traditional telecommunication, from the perspectives of losses through frauds over the technology, fraud attack mechanisms and systems used for detecting and preventing frauds. The paper also provides recommendations for securing emerging technologies from fraud and attacks
DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation
There is an undeniable communication barrier between deaf people and people
with normal hearing ability. Although innovations in sign language translation
technology aim to tear down this communication barrier, the majority of
existing sign language translation systems are either intrusive or constrained
by resolution or ambient lighting conditions. Moreover, these existing systems
can only perform single-sign ASL translation rather than sentence-level
translation, making them much less useful in daily-life communication
scenarios. In this work, we fill this critical gap by presenting DeepASL, a
transformative deep learning-based sign language translation technology that
enables ubiquitous and non-intrusive American Sign Language (ASL) translation
at both word and sentence levels. DeepASL uses infrared light as its sensing
mechanism to non-intrusively capture the ASL signs. It incorporates a novel
hierarchical bidirectional deep recurrent neural network (HB-RNN) and a
probabilistic framework based on Connectionist Temporal Classification (CTC)
for word-level and sentence-level ASL translation respectively. To evaluate its
performance, we have collected 7,306 samples from 11 participants, covering 56
commonly used ASL words and 100 ASL sentences. DeepASL achieves an average
94.5% word-level translation accuracy and an average 8.2% word error rate on
translating unseen ASL sentences. Given its promising performance, we believe
DeepASL represents a significant step towards breaking the communication
barrier between deaf people and hearing majority, and thus has the significant
potential to fundamentally change deaf people's lives
In situ characterization of two wireless transmission schemes for ingestible capsules
We report the experimental in situ characterization of 30-40 MHz and 868 MHz wireless transmission schemes for ingestible capsules, in porcine carcasses. This includes a detailed study of the performance of a magnetically coupled near-field very high-frequency (VHF) transmission scheme that requires only one eighth of the volume and one quarter of the power consumption of existing 868-MHz solutions. Our in situ measurements tested the performance of four different capsules specially constructed for this study (two variants of each transmission scheme), in two scenarios. One mimicked the performance of a body-worn receiving coil, while the other allowed the characterization of the direction-dependent signal attenuation due to losses in the surrounding tissue. We found that the magnetically coupled near-field VHF telemetry scheme presents an attractive option for future, miniturized ingestible capsules for medical applications
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