Dataset Information

Passive sensing around the corner using spatial coherence.

ABSTRACT: When direct vision is obstructed, detecting an object usually involves either using mirrors or actively controlling some of the properties of light used for illumination. In our paradigm, we show that a highly scattering wall can transfer certain statistical properties of light, which, in turn, can assist in detecting objects even in non-line-of-sight conditions. We experimentally demonstrate that the transformation of spatial coherence during the reflection of light from a diffusing wall can be used to retrieve geometric information about objects hidden around a corner and assess their location. This sensing approach is completely passive, assumes no control over the source of light, and relies solely on natural broadband illumination.

SUBMITTER: Batarseh M

PROVIDER: S-EPMC6128873 | biostudies-other | 2018 Sep

REPOSITORIES: biostudies-other

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Publications

Passive sensing around the corner using spatial coherence.

Batarseh M M Sukhov S S Shen Z Z Gemar H H Rezvani R R Dogariu A A

Nature communications 20180907 1

When direct vision is obstructed, detecting an object usually involves either using mirrors or actively controlling some of the properties of light used for illumination. In our paradigm, we show that a highly scattering wall can transfer certain statistical properties of light, which, in turn, can assist in detecting objects even in non-line-of-sight conditions. We experimentally demonstrate that the transformation of spatial coherence during the reflection of light from a diffusing wall can be ...[more]

PMID: 30194292

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Project description:BackgroundLoneliness and social isolation are associated with multiple health problems, including depression, functional impairment, and death. Mobile sensing using smartphones and wearable devices, such as fitness trackers or smartwatches, as well as ambient sensors, can be used to acquire data remotely on individuals and their daily routines and behaviors in real time. This has opened new possibilities for the early detection of health and social problems, including loneliness and social isolation.ObjectiveThis scoping review aimed to identify and synthesize recent scientific studies that used passive sensing techniques, such as the use of in-home ambient sensors, smartphones, and wearable device sensors, to collect data on device users' daily routines and behaviors to detect loneliness or social isolation. This review also aimed to examine various aspects of these studies, especially target populations, privacy, and validation issues.MethodsA scoping review was undertaken, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Studies on the topic under investigation were identified through 6 databases (IEEE Xplore, Scopus, ACM, PubMed, Web of Science, and Embase). The identified studies were screened for the type of passive sensing detection methods for loneliness and social isolation, targeted population, reliability of the detection systems, challenges, and limitations of these detection systems.ResultsAfter conducting the initial search, a total of 40,071 papers were identified. After screening for inclusion and exclusion criteria, 29 (0.07%) studies were included in this scoping review. Most studies (20/29, 69%) used smartphone and wearable technology to detect loneliness or social isolation, and 72% (21/29) of the studies used a validated reference standard to assess the accuracy of passively collected data for detecting loneliness or social isolation.ConclusionsDespite the growing use of passive sensing technologies for detecting loneliness and social isolation, some substantial gaps still remain in this domain. A population heterogeneity issue exists among several studies, indicating that different demographic characteristics, such as age and differences in participants' behaviors, can affect loneliness and social isolation. In addition, despite extensive personal data collection, relatively few studies have addressed privacy and ethical issues. This review provides uncertain evidence regarding the use of passive sensing to detect loneliness and social isolation. Future research is needed using robust study designs, measures, and examinations of privacy and ethical concerns.

Project description:The traditional approach in neuroscience relies on encoding models where brain responses are related to different stimuli in order to establish dependencies. In decoding tasks, on the contrary, brain responses are used to predict the stimuli, and traditionally, the signals are assumed stationary within trials, which is rarely the case for natural stimuli. We hypothesize that a decoding model assuming each experimental trial as a realization of a random process more likely reflects the statistical properties of the undergoing process compared to the assumption of stationarity. Here, we propose a Coherence-based spectro-spatial filter that allows for reconstructing stimulus features from brain signal's features. The proposed method extracts common patterns between features of the brain signals and the stimuli that produced them. These patterns, originating from different recording electrodes are combined, forming a spatial filter that produces a unified prediction of the presented stimulus. This approach takes into account frequency, phase, and spatial distribution of brain features, hence avoiding the need to predefine specific frequency bands of interest or phase relationships between stimulus and brain responses manually. Furthermore, the model does not require the tuning of hyper-parameters, reducing significantly the computational load attached to it. Using three different cognitive tasks (motor movements, speech perception, and speech production), we show that the proposed method consistently improves stimulus feature predictions in terms of correlation (group averages of 0.74 for motor movements, 0.84 for speech perception, and 0.74 for speech production) in comparison with other methods based on regularized multivariate regression, probabilistic graphical models and artificial neural networks. Furthermore, the model parameters revealed those anatomical regions and spectral components that were discriminant in the different cognitive tasks. This novel method does not only provide a useful tool to address fundamental neuroscience questions, but could also be applied to neuroprosthetics.

Dataset Information

Passive sensing around the corner using spatial coherence.

Publications

Passive sensing around the corner using spatial coherence.

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