Evidence map›Paper›PMID 36273010›Full record

ArticleScientific data2022

A comprehensive ultra-wideband dataset for non-cooperative contextual sensing.

Mohammud J Bocus, Robert Piechocki

Abstract readDataset
In one paragraph

Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mohammud J BocusSchool of Computer Science, Electrical and Electronic Engineering, and Engineering Maths, University of Bristol, Bristol, BS8 1UB, UK. junaid.bocus@bristol.ac.uk.ORCID 0000-0001-7843-3445
Robert PiechockiSchool of Computer Science, Electrical and Electronic Engineering, and Engineering Maths, University of Bristol, Bristol, BS8 1UB, UK.

Funding

RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/R018677/1
6 · The paper itself

Abstract

Nowadays, an increasing amount of attention is being devoted towards passive and non-intrusive sensing methods. The prime example is healthcare applications, where on-body sensors are not always an option or in other applications which require the detection and tracking of unauthorized (non-cooperative) targets within a given environment. Therefore, in this paper we present a dataset consisting of measurements obtained from Radio-Frequency (RF) devices. Essentially, the dataset consists of Ultra-Wideband (UWB) data in the form of Channel Impulse Response (CIR), acquired via a Commercial Off-the-Shelf (COTS) UWB equipment. Approximately 1.6 hours of annotated measurements are provided, which are collected in a residential environment. This dataset can be used to passively track a target's location in an indoor environment. Additionally, it can also be used to advance UWB-based Human Activity Recognition (HAR) since three basic human activities were recorded, namely, sitting, standing and walking. We anticipate that such datasets may be utilized to develop novel algorithms and methodologies for healthcare, smart homes and security applications.

Indexed as

AlgorithmsRadio WavesHumansWalking

Identifiers

PMID36273010
PMCPMC9587989

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.