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Citation
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HERO ID
11190006
Reference Type
Journal Article
Title
Correlation of air pollutants and prediction of physical fitness index based on wireless sensor network
Author(s)
Ping, N; Sun, P; Kang, Q
Year
2021
Is Peer Reviewed?
Yes
Journal
Arabian Journal of Geosciences
ISSN:
1866-7511
Publisher
Springer Science and Business Media Deutschland GmbH
Volume
14
Issue
18
Language
English
DOI
10.1007/s12517-021-08178-w
URL
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113385359&doi=10.1007%2fs12517-021-08178-w&partnerID=40&md5=16cb659dcf73ce363e991070fbdcb602
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Relationship(s)
has retraction
12164870
Editorial Expression of Concern: Topical Collection “Environment and Low Carbon Transportation”
has retraction
12167952
Retraction Note to:
Abstract
This paper studies the correlation between different data of urban pollutants. It is pointed out that the identification way of the relationship between the environmental pollutants in the surrounding area and the urban environmental pollutants, the geography of the surrounding area, the location and the corresponding wind direction correspond to each other. In addition, it is judged and studied according to the wireless sensor network and physical indicators, so as to understand the correlation between the environmental pollution sources. The information gathered through fitness trackers assumes a significant job in improving wellbeing and the prosperity of the individuals who wear them. There is additionally a progression of direct correlations of a few wellbeing markers from various wellness trackers. In this investigation, we thought about the quantity of steps, calories consumed, and 3 miles of wellness tracker gathered in a free day to day environment over a time of 14 days. It shows our work that the quantity of steps announced by wearing distinctive specialized gadgets at the same time can vary by as much as 26%. At the same time, the same trend is based on counting steps in the mutants seen at a distance traveled. A small correlation is found between the number of calories burned and the observed change in the number of steps between multiple devices. Our findings show their health indicators as calorie burn reports and miles run and rely heavily on the manufacturerâs proprietary algorithms for the device itself and data such as calculations and inferences. © 2021, Saudi Society for Geosciences.
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