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Advanced Applications in Smartphone-Based Analysis

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 19226

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Special Issue Editors

Dr. Miguel María Erenas

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Guest Editor

Faculty of Sciences, Department of Analytical Chemistry, University of Granada, 18071 Granada, Spain
Interests: analytical chemistry; sensors and biosensors; optical sensors; electrochemical sensors; intelligent food packaging; thread-based microfluidic devices; cloth-based microfluidic devices

Dr. Isabel M Pérez de Vargas-Sansalvador

E-Mail Website1 Website2
Guest Editor

Faculty of Sciences, Department of Analytical Chemistry, University of Granada, 18071 Granada, Spain
Interests: analytical chemistry; sensors and biosensors; optical sensors; gas sensors; smart packaging sensors and indicators; microfluidic devices

Dr. Ignacio de Orbe-Payá

E-Mail Website1 Website2
Guest Editor

Faculty of Sciences, Department of Analytical Chemistry, University of Granada, 18071 Granada, Spain
Interests: analytical chemistry, sensors and biosensors; optical sensors; microfluidic devices; multivariate calibration; portable instrumentation development

Special Issue Information

Dear Colleagues,

The use of smartphones as analytical platforms, either by themselves or in combination with any kind of external device or sensor, is a recent trend that combines an immediate response and simplicity in the analysis with the possibility of real-time communication to obtain information in situ. Furthermore, thanks to the advances in electronics and the miniaturization of the different electronic components, smartphones possess a great variety of sensors, wireless communication protocols permitting data transmission, real-time geolocation, and energy harvesting between devices and different ports (USB, jack) that permits the registration of different kinds of signals, optical or electrical, together with a high processing capability.

This Special Issue aims at bringing together academia and industrial researchers to explore the opportunities of the use of smartphones as analytical platforms. We solicit papers covering various topics of interest, such as the use of smartphones to obtain an analytical signal (optical, colorimetric or electrochemical) directly from the sample or combined with a chemical and/or biochemical sensor to produce it, used in different fields as medicine, environment, food, process industries, security, and defense.

Dr. Miguel María Erenas
Dr. Isabel M Pérez de Vargas-Sansalvador
Dr. Ignacio de Orbe-Payá
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Smartphone
Optical techniques
Vision-based computer
Electrochemical techniques
Point-of-care
Point-of-need
Sensing
Application
Portable instrumentation

Published Papers (4 papers)

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Research

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11 pages, 2650 KiB

Open AccessArticle

Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis

by Seon-Chil Kim and Young-Sik Cho

Sensors 2022, 22(14), 5445; https://doi.org/10.3390/s22145445 - 21 Jul 2022

Cited by 8 | Viewed by 5603

Abstract

Urinalysis, an elementary chemical reaction-based method for analyzing color conversion factors, facilitates examination of pathological conditions in the human body. Recently, considerable urinalysis-centered research has been conducted on the analysis of urine dipstick colors using smartphone cameras; however, such methods have a drawback: the problem of reproducibility of accuracy through quantitative analysis. In this study, to solve this problem, the function values for each concentration of a range of analysis factors were implemented in an algorithm through urine dipstick RGB semi-quantitative color analysis to enable real-time results. Herein, pH, glucose, ketones, hemoglobin, bilirubin, protein (albumin), and nitrites were selected as analysis factors, and the accuracy levels of the existing equipment and the test application were compared and evaluated using artificial urine. In the semi-quantitative analysis, the red (R), green (G), and blue (B) characteristic values were analyzed by extracting the RGB characteristic values of the analysis factors for each concentration of artificial urine and obtaining linear function values. In addition, to improve the reproducibility of detection accuracy, the measurement value of the existing test equipment was set to an absolute value; using a machine-learning technique, the confusion matrix, we attempted to stabilize test results that vary with environment. Full article

(This article belongs to the Special Issue Advanced Applications in Smartphone-Based Analysis)

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13 pages, 6072 KiB

Open AccessArticle

Carrying Position-Independent Ensemble Machine Learning Step-Counting Algorithm for Smartphones

by Zihan Song, Hye-Jin Park, Ngeemasara Thapa, Ja-Gyeong Yang, Kenji Harada, Sangyoon Lee, Hiroyuki Shimada, Hyuntae Park and Byung-Kwon Park

Sensors 2022, 22(10), 3736; https://doi.org/10.3390/s22103736 - 13 May 2022

Cited by 6 | Viewed by 2252

Abstract

Current step-count estimation techniques use either an accelerometer or gyroscope sensors to calculate the number of steps. However, because of smartphones unfixed placement and direction, their accuracy is insufficient. It is necessary to consider the impact of the carrying position on the accuracy of the pedometer algorithm, because of people carry their smartphones in various positions. Therefore, this study proposes a carrying-position independent ensemble step-counting algorithm suitable for unconstrained smartphones in different carrying positions. The proposed ensemble algorithm comprises a classification algorithm that identifies the carrying position of the smartphone, and a regression algorithm that considers the identified carrying position and calculates the number of steps. Furthermore, a data acquisition system that collects (i) label data in the form of the number of steps estimated from the Force Sensitive Resistor (FSR) sensors, and (ii) input data in the form of the three-axis acceleration data obtained from the smartphones is also proposed. The obtained data were used to allow the machine learning algorithms to fit the signal features of the different carrying positions. The reliability of the proposed ensemble algorithms, comprising a random forest classifier and a regression model, was comparatively evaluated with a commercial pedometer application. The results indicated that the proposed ensemble algorithm provides higher accuracy, ranging from 98.1% to 98.8%, at self-paced walking speed than the commercial pedometer application, and the machine learning-based ensemble algorithms can effectively and accurately predict step counts under different smart phone carrying positions. Full article

(This article belongs to the Special Issue Advanced Applications in Smartphone-Based Analysis)

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23 pages, 3512 KiB

Open AccessEditor’s ChoiceArticle

Measurement of Rock Joint Surfaces by Using Smartphone Structure from Motion (SfM) Photogrammetry

by Pengju An, Kun Fang, Qiangqiang Jiang, Haihua Zhang and Yi Zhang

Sensors 2021, 21(3), 922; https://doi.org/10.3390/s21030922 - 30 Jan 2021

Cited by 36 | Viewed by 4636

Abstract

The measurement of rock joint surfaces is essential for the estimation of the shear strength of the rock discontinuities in rock engineering. Commonly used techniques for the acquisition of the morphology of the surfaces, such as profilometers and laser scanners, either have low accuracy or high cost. Therefore, a high-speed, low-cost, and high-accuracy method for obtaining the topography of the joint surfaces is necessary. In this paper, a smartphone structure from motion (SfM) photogrammetric solution for measuring rock joint surfaces is presented and evaluated. Image datasets of two rock joint specimens were taken under two different modes by using an iPhone 6s, a Pixel 2, and a T329t and subsequently processed through SfM-based software to obtain 3D models. The technique for measuring rock joint surfaces was evaluated using the root mean square error (RMSE) of the cloud-to-cloud distance and the mean error of the joint roughness coefficient (JRC). The results show that the RMSEs by using the iPhone 6s and Pixel 2 are both less than 0.08 mm. The mean errors of the JRC are −7.54 and −5.27% with point intervals of 0.25 and 1.0 mm, respectively. The smartphone SfM photogrammetric method has comparable accuracy to a 3D laser scanner approach for reconstructing laboratory-sized rock joint surfaces, and it has the potential to become a popular method for measuring rock joint surfaces. Full article

(This article belongs to the Special Issue Advanced Applications in Smartphone-Based Analysis)

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Review

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25 pages, 4457 KiB

Open AccessReview

Smartphone-Based Electrochemical Systems for Glucose Monitoring in Biofluids: A Review

by Jie Xu, Zupeng Yan and Qingjun Liu

Sensors 2022, 22(15), 5670; https://doi.org/10.3390/s22155670 - 28 Jul 2022

Cited by 13 | Viewed by 5650

Abstract

As a vital biomarker, glucose plays an important role in multiple physiological and pathological processes. Thus, glucose detection has become an important direction in the electrochemical analysis field. In order to realize more convenient, real-time, comfortable and accurate monitoring, smartphone-based portable, wearable and implantable electrochemical glucose monitoring is progressing rapidly. In this review, we firstly introduce technologies integrated in smartphones and the advantages of these technologies in electrochemical glucose detection. Subsequently, this overview illustrates the advances of smartphone-based portable, wearable and implantable electrochemical glucose monitoring systems in diverse biofluids over the last ten years (2012–2022). Specifically, some interesting and innovative technologies are highlighted. In the last section, after discussing the challenges in this field, we offer some future directions, such as application of advanced nanomaterials, novel power sources, simultaneous detection of multiple markers and a closed-loop system. Full article

(This article belongs to the Special Issue Advanced Applications in Smartphone-Based Analysis)

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