Evidence map›Paper›PMID 29439398›Full record

ArticleSensors (Basel, Switzerland)2018

A Real-Time Wireless Sweat Rate Measurement System for Physical Activity Monitoring.

Andrew Brueck, Tashfin Iftekhar, Alicja B Stannard, Kumar Yelamarthi, Tolga Kaya

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 9% 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.

  1. Pooled it
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  10. Biomarkers and Detection Platforms for Human Health and Performance Monitoring: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022
    Review
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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

5 authors at 2 institutions in 1 country.

Andrew BrueckSchool of Engineering and Technology, Central Michigan University, Mt Pleasant, MI 48859, USA. bruec1ap@cmich.edu.
Tashfin IftekharSchool of Engineering and Technology, Central Michigan University, Mt Pleasant, MI 48859, USA. iftek1a@cmich.edu.
Alicja B StannardDepartment of Physical Therapy and Human Movement Science, Sacred Heart University, Fairfield, CT 06825, USA. stannarda@sacredheart.edu.
Kumar YelamarthiSchool of Engineering and Technology, Central Michigan University, Mt Pleasant, MI 48859, USA. yelam1k@cmich.edu.ORCID 0000-0002-0072-3909
Tolga KayaSchool of Computing, Sacred Heart University, Fairfield, CT 06825, USA. kayat@sacredheart.edu.
Central Michigan University · USSacred Heart University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There has been significant research on the physiology of sweat in the past decade, with one of the main interests being the development of a real-time hydration monitor that utilizes sweat. The contents of sweat have been known for decades; sweat provides significant information on the physiological condition of the human body. However, it is important to know the sweat rate as well, as sweat rate alters the concentration of the sweat constituents, and ultimately affects the accuracy of hydration detection. Towards this goal, a calorimetric based flow-rate detection system was built and tested to determine sweat rate in real time. The proposed sweat rate monitoring system has been validated through both controlled lab experiments (syringe pump) and human trials. An Internet of Things (IoT) platform was embedded, with the sensor using a Simblee board and Raspberry Pi. The overall prototype is capable of sending sweat rate information in real time to either a smartphone or directly to the cloud. Based on a proven theoretical concept, our overall system implementation features a pioneer device that can truly measure the rate of sweat in real time, which was tested and validated on human subjects. Our realization of the real-time sweat rate watch is capable of detecting sweat rates as low as 0.15 µL/min/cm², with an average error in accuracy of 18% compared to manual sweat rate readings.

Indexed as

SweatExerciseHumansInternetMonitoring, PhysiologicSmartphonedehydrationIoTPDMSsweat ratesweat sensor

Identifiers

PMID29439398
PMCPMC5855985
OpenAlexW2790113251

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.