Evidence map›Paper›PMID 36236443›Full record

ArticleSensors (Basel, Switzerland)2022

Smartphone-Based Social Distance Detection Technology with Near-Ultrasonic Signal.

Naizheng Jia, Haoran Shu, Xinheng Wang, Bowen Xu, Yuzhang Xi, Can Xue, Youming Liu, Zhi Wang

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.9field-weighted citation impact, top 27% of its field
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

0 citing papers in PubMed, 10 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 2 institutions in 1 country.

Naizheng JiaCollege of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China.
Haoran ShuCollege of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China.
Xinheng WangSchool of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.ORCID 0000-0001-8771-8901
Bowen XuCollege of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China.
Yuzhang XiCollege of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China.
Can XueCollege of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China.
Youming LiuCollege of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China.
Zhi WangCollege of Control Science and Engineering, Zhejiang University, Hangzhou 310000, China.
Zhejiang University · CNXi’an Jiaotong-Liverpool University · CN

Funding

National Key Research and Development Program of China 2021YFB3900800
6 · The paper itself

Abstract

With the emergence of COVID-19, social distancing detection is a crucial technique for epidemic prevention and control. However, the current mainstream detection technology cannot obtain accurate social distance in real-time. To address this problem, this paper presents a first study on smartphone-based social distance detection technology based on near-ultrasonic signals. Firstly, according to auditory characteristics of the human ear and smartphone frequency response characteristics, a group of 18 kHz-23 kHz inaudible Chirp signals accompanied with single frequency signals are designed to complete ranging and ID identification in a short time. Secondly, an improved mutual ranging algorithm is proposed by combining the cubic spline interpolation and a two-stage search to obtain robust mutual ranging performance against multipath and NLoS affect. Thirdly, a hybrid channel access protocol is proposed consisting of Chirp BOK, FDMA, and CSMA/CA to increase the number of concurrencies and reduce the probability of collision. The results show that in our ranging algorithm, 95% of the mutual ranging error within 5 m is less than 10 cm and gets the best performance compared to the other traditional methods in both LoS and NLoS. The protocol can efficiently utilize the limited near-ultrasonic channel resources and achieve a high refresh rate ranging under the premise of reducing the collision probability. Our study can realize high-precision, high-refresh-rate social distance detection on smartphones and has significant application value during an epidemic.

Indexed as

COVID-19SmartphoneHumansPhysical DistancingTechnologyUltrasonicsacoustic signalmultiple access protocolmutual rangingnear-ultrasonic signalsocial distance detection

Identifiers

PMID36236443
PMCPMC9571867
OpenAlexW4297474229

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.