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Journal articles on the topic 'Smartphone-based'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Aralikatti, Rakesh I., and Kishan S. Anegundi. "Location-Based Services in a Smartphone." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (2016): 130–33. http://dx.doi.org/10.9756/bijsesc.8259.

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2

Pituła, Emil, Marcin Koba, and Mateusz Śmietana. "Which smartphone for a smartphone-based spectrometer?" Optics & Laser Technology 140 (August 2021): 107067. http://dx.doi.org/10.1016/j.optlastec.2021.107067.

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3

Gao, Xuefei, and Nianqiang Wu. "Smartphone-Based Sensors." Electrochemical Society Interface 25, no. 4 (2016): 79–81. http://dx.doi.org/10.1149/2.f07164if.

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4

Handzel, Ophir, and Kevin Franck. "Smartphone based hearing evaluation." Operative Techniques in Otolaryngology-Head and Neck Surgery 32, no. 2 (2021): 87–91. http://dx.doi.org/10.1016/j.otot.2021.05.004.

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5

Ahmed, Yunus. "Smartphone-based analytical biosensors." Dental Poster Journal 9, no. 2 (2020): 1–2. http://dx.doi.org/10.15713/ins.dpj.056.

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6

Garabelli, Paul, Stavros Stavrakis, and Sunny Po. "Smartphone-based arrhythmia monitoring." Current Opinion in Cardiology 32, no. 1 (2017): 53–57. http://dx.doi.org/10.1097/hco.0000000000000350.

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7

Kumar, Nilesh, Bandello Francesco, and Ashish Sharma. "Smartphone-based Gonio-Imaging." Journal of Glaucoma 28, no. 9 (2019): e149-e150. http://dx.doi.org/10.1097/ijg.0000000000001306.

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8

Turk-Adawi, Karam, and Sherry L. Grace. "Smartphone-based cardiac rehabilitation." Heart 100, no. 22 (2014): 1737–38. http://dx.doi.org/10.1136/heartjnl-2014-306335.

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9

Nuñez, José Jesús Reyes. "Smartphone-Based School Atlases?" Cartographica: The International Journal for Geographic Information and Geovisualization 48, no. 2 (2013): 126–33. http://dx.doi.org/10.3138/carto.48.2.1842.

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10

Huang, Xiwei, Dandan Xu, Jin Chen, et al. "Smartphone-based analytical biosensors." Analyst 143, no. 22 (2018): 5339–51. http://dx.doi.org/10.1039/c8an01269e.

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With the rapid development, mass production, and pervasive distribution of smartphones in recent years, they have provided people with portable, cost-effective, and easy-to-operate platforms to build analytical biosensors for point-of-care (POC) applications and mobile health.

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11

HUDÁK, Marián, Martin SIVÝ, and Branislav SOBOTA. "UNIFORM SMARTPHONE CONTROLLER FOR WEB-BASED VIRTUAL REALITY PURPOSES." Acta Electrotechnica et Informatica 21, no. 1 (2021): 11–18. http://dx.doi.org/10.15546/aeei-2021-0002.

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This work introduces a uniform smartphone controller interface integrated into LIRKIS G-CVE web-based global collaborative virtual environments. In general, VR controllers provide various kinds of interaction techniques to manipulate virtual objects. Mostly, those aim focus on controlling the virtual context and the interaction with 3D GUI integrated in the virtual environment. With respect to web-based virtual reality, the progress in development of uniform interfaces is raising thanks to emerging web technologies and frameworks with cross-platform support. Although there are many manufacture

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12

Chandrakanth, Prithvi, and KS Chandrakanth. "Smartphone-based intraocular lens microscope." Indian Journal of Ophthalmology 68, no. 10 (2020): 2213. http://dx.doi.org/10.4103/ijo.ijo_2032_19.

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Di Nonno, Sarah, and Roland Ulber. "Smartphone-based optical analysis systems." Analyst 146, no. 9 (2021): 2749–68. http://dx.doi.org/10.1039/d1an00025j.

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The review describes the design, application and performance of current smartphone-based colorimeters, photo- and spectrometers and fluorimeters. Furthermore, it gives an overview of the advantages and disadvantages of such systems.

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14

Wu, Cheng Jung, Sheng Yu Wu, and Yaoh Shiang Lin. "An Innovative Smartphone-Based Rhinoendoscope." Otolaryngology–Head and Neck Surgery 151, no. 1_suppl (2014): P42—P43. http://dx.doi.org/10.1177/0194599814541627a45.

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15

CONG Jing, 丛. 婧., 俎明明 ZU Ming-ming, 李洪涛 LI Hong-tao, 崔笑宇 CUI Xiao-yu, 陈. 硕. CHEN Shuo, and 席. 鹏. XI Peng. "Smartphone-based fundus imaging system." Chinese Optics 12, no. 1 (2019): 97–103. http://dx.doi.org/10.3788/co.20191201.0097.

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16

Rama, Rama Faiz Pangestu, Niswi Mukarromah, Rauuf Anugrah Akbar, and Hafiz Alpradisa. "Smartphone-based learning information management." World Journal on Educational Technology: Current Issues 16, no. 3 (2024): 201–14. http://dx.doi.org/10.18844/wjet.v16i3.9325.

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Information management skills are essential for students. This research aimed to identify and describe the understanding, experience, awareness, and meaning of Masters & Doctoral students in managing lecture material files. This qualitative descriptive research used a phenomenological approach. The research subjects were 10 Masters & Doctoral students. This research produced the following findings: students have understood personal information management as managing information and organizing data, and the student experience in managing learning information, including downloading, skim

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17

Alexander, John C., and Girish P. Joshi. "Smartphone Application-based Medical Devices." Anesthesia & Analgesia 123, no. 4 (2016): 1046–50. http://dx.doi.org/10.1213/ane.0000000000001502.

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18

Perez de Vargas-Sansalvador, Isabel M., Miguel M. Erenas, Antonio Martínez-Olmos, Fatima Mirza-Montoro, Dermot Diamond, and Luis Fermin Capitan-Vallvey. "Smartphone based meat freshness detection." Talanta 216 (August 2020): 120985. http://dx.doi.org/10.1016/j.talanta.2020.120985.

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19

Jian, Dan, Bin Wang, Huachuan Huang, et al. "Sunlight based handheld smartphone spectrometer." Biosensors and Bioelectronics 143 (October 2019): 111632. http://dx.doi.org/10.1016/j.bios.2019.111632.

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20

Lee, Yong-Gyu, Won Sig Jeong, and Gilwon Yoon. "Smartphone-Based Mobile Health Monitoring." Telemedicine and e-Health 18, no. 8 (2012): 585–90. http://dx.doi.org/10.1089/tmj.2011.0245.

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21

Subramanian, Lakshmi, Philipp Stephanow, and Tobias Wahl. "Towards Cloud Based Smartphone Security." PARS: Parallel-Algorithmen, -Rechnerstrukturen und -Systemsoftware 28, no. 1 (2011): 244–50. http://dx.doi.org/10.1007/bf03342011.

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22

Lasini, Wickramasinghe, and B. Dissanayake Maheshi. "Smartphone Based Indoor Position Estimation." Journal of Innovation Sciences and Sustainable Technologies 2, no. 2 (2022): 99–106. https://doi.org/10.0608/JISST.2022522969.

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Positioning or location estimation systems are adopted widely in modern world, to locate objects or users. Current broadly adopted positioning sys- tems such as global positioning system (GPS) or global navigation satellite system (GNSS) are limited to outdoor applications and provide poor locating accuracy for indoor positing with short movement span. Through this study we aim to evaluate the possibility of utilizing received signal strength indicator (RSSI) value of a smartphone antenna with the com- bination of trilateration theory and feed-forward back-propagation neural network (FFBPNN) t

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23

Tian, Ke, Mamoru Endo, Mayu Urata, Katsuhiro Mouri, and Takami Yasuda. "Multi-Viewpoint Smartphone AR-Based Learning System for Astronomical Observation." International Journal of Computer Theory and Engineering 6, no. 5 (2014): 396–400. http://dx.doi.org/10.7763/ijcte.2014.v6.897.

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24

Bhatele, Priyanka, and Mangesh Bedekar. "Machine Learning Based Smartphone Screen GestureRecognition Using Smartphone Embedded Accelerometer and Gyroscope." International Journal of Computing and Digital Systems 15, no. 1 (2024): 911–24. http://dx.doi.org/10.12785/ijcds/160166.

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25

Ding, Jierui. "Well-Designed Smartphone-Based Imaging Biosensor." Highlights in Science, Engineering and Technology 14 (September 29, 2022): 296–304. http://dx.doi.org/10.54097/hset.v14i.1835.

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With the development of hardware and software for smartphones, more and more well-designed smartphone-based imaging biosensors have been created and broadly applied in point-of-care testing (POCT). Imaging biosensors can get clear images through the high pixel density of smartphones’ camera systems. And smartphones also provide a chance for imaging processing thanks to smartphones' central processing units (CPUs) and graphics processing units (GPUs). Different approaches have extensively explored smartphone-based imaging biosensors. The commonly used imaging methods are generally implemented b

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26

Bhupathiraju, Shiva Satya, Christen Wendel, and Robert L. Williams. "Towards a Smartphone based Multimode Sensing." IFAC Proceedings Volumes 46, no. 15 (2013): 118–25. http://dx.doi.org/10.3182/20130811-5-us-2037.00091.

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27

Pfeil, Juliane, Luise N. Dangelat, Marcus Frohme, and Katja Schulze. "Smartphone based mobile microscopy for diagnostics." Journal of Cellular Biotechnology 4, no. 1-2 (2019): 57–65. http://dx.doi.org/10.3233/jcb-180010.

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28

Gupta, Harsh. "Smartphone Based Cervical Spine Stress Prevention." Journal of Software Engineering and Applications 11, no. 02 (2018): 110–20. http://dx.doi.org/10.4236/jsea.2018.112006.

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29

Qian, Shiyu, Yu Cui, Zheng Cai, and Lingling Li. "Applications of smartphone-based colorimetric biosensors." Biosensors and Bioelectronics: X 11 (September 2022): 100173. http://dx.doi.org/10.1016/j.biosx.2022.100173.

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30

Dubey, R., S. Bharadwaj, V. B. Sharma, A. Bhatt, and S. Biswas. "SMARTPHONE-BASED TRAFFIC NOISE MAPPING SYSTEM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 613–20. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-613-2022.

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Abstract. Noise pollution is one of the most serious environmental threats to human health. Noise is becoming more prevalent in urban areas, and it is having a negative impact on human health. The increase in noise is due to the increase in the number of vehicles that creates chaos over the road due to honking. Smart monitoring using is smartphones is required to reduce human dependency and monitor data efficiently to reduce logistical obstacles. A smartphone-based noise monitoring solution can handle the problem of monitoring noise at various traffic crossings in a metropolis. The topographic

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31

Alobaidi, Hind, Nathan Clarke, Fudong Li, and Abdulrahman Alruban. "Real-world smartphone-based gait recognition." Computers & Security 113 (February 2022): 102557. http://dx.doi.org/10.1016/j.cose.2021.102557.

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32

Kapse, Renuka Vijay. "Smartphone based ECG Acquisition and Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 539–43. http://dx.doi.org/10.22214/ijraset.2021.35013.

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Health monitoring and technologies related to health monitoring is an appealing area of research. The electrocardiogram (ECG) has constantly being mainstream estimation plan to evaluate and analyse cardiovascular diseases. Heart health is important for everyone. Heart needs to be monitored regularly and early warning can prevent the permanent heart damage. Also heart diseases are the leading cause of death worldwide. Hence the work presents a design of a mini wearable ECG system and it’s interfacing with the Android application. This framework is created to show and analyze the ECG signal got

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33

Hina, Manolo Dulva, Hongyu Guan, Assia Soukane, and Amar Ramdane-Cherif. "CASA: An Alternative Smartphone-Based ADAS." International Journal of Information Technology & Decision Making 21, no. 01 (2021): 273–313. http://dx.doi.org/10.1142/s0219622021500541.

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Advanced driving assistance system (ADAS) is an electronic system that helps the driver navigate roads safely. A typical ADAS, however, is suited to specific brands of vehicle and, due to proprietary restrictions, has non-extendable features. Project CASA is an alternative, low-cost generic ADAS. It is an app deployable on smartphone or tablet. The real-time data needed by the app to make sense of its environment are stored in the vehicle or on the cloud, and are accessible as web services. They are used to determine the current driving context, and, if needed, decide actions to prevent an acc

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34

Aleynikov, A. F., and S. M. Shadrin. "Smartphone-based plant leaf area meter." IOP Conference Series: Earth and Environmental Science 839, no. 3 (2021): 032032. http://dx.doi.org/10.1088/1755-1315/839/3/032032.

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Abstract A portable device is described for measuring an important plant trait – plant leaf area. The principle of operation of the device is based on digital processing of the obtained images by the method of technical vision. It is implemented on the basis of a free cross-platform framework for game development and visualization – the LibGDX software project. An algorithm and a program for the automated determination of the leaf area are presented. The device is autonomous and is based on a smartphone and a gadget for it. The results of his research tests are presented. The purpose of the de

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35

Malphrus, Rebecca M., Roger J. Paxton, Blake R. Volkmer, Robert LeMoyne, Timothy Mastroianni, and Brian L. Tracy. "Smartphone-based Assessment Of Postural Sway." Medicine & Science in Sports & Exercise 46 (May 2014): 697. http://dx.doi.org/10.1249/01.mss.0000495566.86660.3a.

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36

Chen, Yen-Jen, and Jun-Yi Lo. "An Implementation of Smartphone Based VTS." Journal of ICT, Design, Engineering and Technological Science 2, no. 1 (2018): 23–29. http://dx.doi.org/10.33150/jitdets-2.1.4.

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37

He, Suining, and Kang G. Shin. "Geomagnetism for Smartphone-Based Indoor Localization." ACM Computing Surveys 50, no. 6 (2018): 1–37. http://dx.doi.org/10.1145/3139222.

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38

Boubezari, Rayana, Hoa Le Minh, Zabih Ghassemlooy, and Ahmed Bouridane. "Smartphone Camera Based Visible Light Communication." Journal of Lightwave Technology 34, no. 17 (2016): 4121–27. http://dx.doi.org/10.1109/jlt.2016.2590880.

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39

Raber, Florian Philipp, Rokas Gerbutavicius, Armin Wolf, and Karsten Kortüm. "Smartphone-Based Data Collection in Ophthalmology." Klinische Monatsblätter für Augenheilkunde 237, no. 12 (2020): 1420–28. http://dx.doi.org/10.1055/a-1232-4250.

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AbstractDue to their widespread use among the population and their wide range of functions and sensors, smartphones are suitable for data collection for medical purposes. App-supported input masks, patient diaries, and patient information systems, mobile access to the patient file as well as telemedical services will continue to find their way into our field of expertise in the future. In addition, the use of smartphone sensors (GPS and motion sensors, touch display, microphone) and coupling possibilities with biosensors (for example with Continuous Glucose Monitoring [CGM] systems), advanced

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40

narayanan, Sathiya, Sivagnanam R, Smrithisri V.K, and V. Thulasi Bai. "Smartphone Based Non-Invasive Glucose Monitoring." International Journal of Engineering Trends and Technology 67, no. 3 (2019): 119–23. http://dx.doi.org/10.14445/22315381/ijett-v67i3p223.

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41

King, Paul H. "Smartphone-Based Medical Diagnostics, 1st ed." IEEE Pulse 11, no. 5 (2020): 37–38. http://dx.doi.org/10.1109/mpuls.2020.3022144.

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42

Kim, Tae-Hoon, and Jong-In Youn. "Development of a Smartphone-based Pupillometer." Journal of the Optical Society of Korea 17, no. 3 (2013): 249–54. http://dx.doi.org/10.3807/josk.2013.17.3.249.

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43

Abbate, Stefano, Marco Avvenuti, Francesco Bonatesta, Guglielmo Cola, Paolo Corsini, and Alessio Vecchio. "A smartphone-based fall detection system." Pervasive and Mobile Computing 8, no. 6 (2012): 883–99. http://dx.doi.org/10.1016/j.pmcj.2012.08.003.

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44

Hosu, Oana, Andrea Ravalli, Giuseppe Mattia Lo Piccolo, Cecilia Cristea, Robert Sandulescu, and Giovanna Marrazza. "Smartphone-based immunosensor for CA125 detection." Talanta 166 (May 2017): 234–40. http://dx.doi.org/10.1016/j.talanta.2017.01.073.

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45

Maruyama, Takuya, Yoshihiro Sato, Kotaro Nohara, and Shotaro Imura. "Increasing Smartphone-based Travel Survey Participants." Transportation Research Procedia 11 (2015): 280–88. http://dx.doi.org/10.1016/j.trpro.2015.12.024.

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46

Doupis, John, Georgios Festas, Christos Tsilivigos, Vasiliki Efthymiou, and Alexander Kokkinos. "Smartphone-Based Technology in Diabetes Management." Diabetes Therapy 11, no. 3 (2020): 607–19. http://dx.doi.org/10.1007/s13300-020-00768-3.

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47

Zhao, Wenhao, Shulin Tian, Lei Huang, Ke Liu, Lijuan Dong, and Jinhong Guo. "A smartphone-based biomedical sensory system." Analyst 145, no. 8 (2020): 2873–91. http://dx.doi.org/10.1039/c9an02294e.

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48

Ciavarrini, Gloria, Valerio Luconi, and Alessio Vecchio. "Smartphone-based geolocation of Internet hosts." Computer Networks 116 (April 2017): 22–32. http://dx.doi.org/10.1016/j.comnet.2017.02.006.

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49

Wahlstrom, Johan, Isaac Skog, Peter Handel, and Arye Nehorai. "IMU-Based Smartphone-to-Vehicle Positioning." IEEE Transactions on Intelligent Vehicles 1, no. 2 (2016): 139–47. http://dx.doi.org/10.1109/tiv.2016.2588978.

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50

Valcourt, L., Y. D. L. Hoz, and M. Labrador. "Smartphone-based Human Fall Detection System." IEEE Latin America Transactions 14, no. 2 (2016): 1011–17. http://dx.doi.org/10.1109/tla.2016.7437252.

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