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Journal articles on the topic 'Smartphone-based'

To see the other types of publications on this topic, follow the link: Smartphone-based.

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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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 (October 31, 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 (June 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 (January 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 (September 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 (August 27, 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 (June 2013): 126–33. http://dx.doi.org/10.3138/carto.48.2.1842.

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10

Huang, Xiwei, Dandan Xu, Jin Chen, Jixuan Liu, Yangbo Li, Jing Song, Xing Ma, and Jinhong Guo. "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 (June 9, 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 manufacturers of VR controllers, their usage is often limited only for specified display device. Our intention is to cover multiple devices through only one simple controller interface, that is capable to provide a variety of interactions for web-based VR. In this study we proposed Enhanced Smart Client Interface designed for providing fully immersive interaction through smartphones. We performed several experiments focused on user experience and usability under two cloud platforms. Results obtained from experiments performed in our study confirm that utilization of our interface is mostly affected by the server response time. Based on the results this solution is suitable for further development and improvements.

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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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13

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 (September 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

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

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17

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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18

Jian, Dan, Bin Wang, Huachuan Huang, Xin Meng, Cheng Liu, Liang Xue, Fei Liu, and Shouyu Wang. "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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19

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

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20

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

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21

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 (October 2014): 396–400. http://dx.doi.org/10.7763/ijcte.2014.v6.897.

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22

Karimi, Khaoula. "Secure Smart Door Lock System based on Arduino and Smartphone App." Journal of Advanced Research in Dynamical and Control Systems 12, no. 01-Special Issue (February 13, 2020): 407–14. http://dx.doi.org/10.5373/jardcs/v12sp1/20201088.

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23

Gazder, Uneb. "Environmental Awareness and Inclination towards Walking: A Smartphone Application based study." International Journal of Traffic and Transportation Management 02, no. 01 (November 11, 2020): 15–21. http://dx.doi.org/10.5383/jttm.02.01.003.

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Walking is considered to be one of the sustainable modes of transportation which reduces traffic demand leading to lower levels of pollution and congestion. It has resulted in improved physical and psychological health of individuals. This study focuses on studying walking behavior of travelers in Bahrain. Walking data was collected through Moves mobile application while a questionnaire survey was conducted to find the characteristics of the participants. Walking distance per day was found to be approximately 400m on average which is insufficient to have any significant impact on health. There was no significant change in walking behavior between day and night timings. Walking behavior was found to be related to familiarity with location, financial status and family structure of the participants. Personal characteristics (age, gender, etc.) and perception of the participant did not have any significant impact on walking behavior. It is recommended to increase awareness related to walking and improve pedestrian facilities in all areas of Bahrain to promote walking.

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24

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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25

Pfeil, Juliane, Luise N. Dangelat, Marcus Frohme, and Katja Schulze. "Smartphone based mobile microscopy for diagnostics." Journal of Cellular Biotechnology 4, no. 1-2 (January 16, 2019): 57–65. http://dx.doi.org/10.3233/jcb-180010.

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26

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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27

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 by the bright field with the light source or by fluorescence with a fluorescence microscope. Smartphones have enabled the widespread application of imaging-based methods in clinical chemistry, environmental monitoring, flow cytometry, food analysis, drug screening, and medical diagnostics. In detail, this article discusses various imaging biosensors and specific applications of smartphone-based imaging biosensors for bright-field imaging and fluorescence bioimaging. Meanwhile, the opportunities and challenges of smartphone-based imaging biosensors are also analyzed here.

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28

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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29

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 topographical data, noise data, and noise prediction models are required for forecasting noise levels and showing them as maps. In the Indian city of Lucknow, the entire procedure is being performed by providing a map of 2D and 3D forms. The smartphone-based software tracks noise levels at three road crossings at three different times each day. The collected noise levels were calibrated against a standard noise metre to achieve correct noise levels for these sites. Following that, three noise environment types are chosen and mapped using open-source satellite images and conventional noise models through the web on the GIS platform. The anticipated noise levels on the maps were compared to recorded noise data from identical locations using a conventional noise metre for these three crossings and were found to be within 5.5 dB of accuracy. For 3D mapping, shadow height provides the Z value for point cloud DEM generation for 3D model for noise data of city of Lucknow.

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30

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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31

Kapse, Renuka Vijay. "Smartphone based ECG Acquisition and Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 10, 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 from the ECG wearable system. The ECG signals will be shipped off an android application via Bluetooth device. This system will automatically alert the user through SMS.

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32

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 (September 30, 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 accident or keep road navigation safe. Project CASA is an undertaking of a consortium of industrial and academic partners. A use case scenario is tested in the laboratory (virtual) and on the road (actual) to validate the appropriateness of CASA. It is a contribution to safe driving. CASA’s contribution also lies in its approach in the semantic modeling of the context of the environment, the vehicle and the driver, and on the modeling of rules for fusion of data and fission process yielding an action to be implemented. In addition, CASA proposes a secured means of transmitting data using light, via light fidelity (LiFi), itself an alternative means of wireless vehicle–smartphone communication.

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33

Aleynikov, A. F., and S. M. Shadrin. "Smartphone-based plant leaf area meter." IOP Conference Series: Earth and Environmental Science 839, no. 3 (September 1, 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 device is to study the influence of the environment on ecological systems in the field.

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34

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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35

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

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36

He, Suining, and Kang G. Shin. "Geomagnetism for Smartphone-Based Indoor Localization." ACM Computing Surveys 50, no. 6 (January 12, 2018): 1–37. http://dx.doi.org/10.1145/3139222.

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37

Boubezari, Rayana, Hoa Le Minh, Zabih Ghassemlooy, and Ahmed Bouridane. "Smartphone Camera Based Visible Light Communication." Journal of Lightwave Technology 34, no. 17 (September 1, 2016): 4121–27. http://dx.doi.org/10.1109/jlt.2016.2590880.

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38

Raber, Florian Philipp, Rokas Gerbutavicius, Armin Wolf, and Karsten Kortüm. "Smartphone-Based Data Collection in Ophthalmology." Klinische Monatsblätter für Augenheilkunde 237, no. 12 (December 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 camera technology, the possibility of regular and appointment independent checking of the visual system (visual acuity/contrast vision) as well as real-time data transfer offer interesting possibilities for patient treatment and clinical research. The present review deals with the current status and future perspectives of smartphone-based data collection and possible applications in ophthalmology.

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39

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 (March 25, 2019): 119–23. http://dx.doi.org/10.14445/22315381/ijett-v67i3p223.

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40

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

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41

Kim, Tae-Hoon, and Jong-In Youn. "Development of a Smartphone-based Pupillometer." Journal of the Optical Society of Korea 17, no. 3 (June 25, 2013): 249–54. http://dx.doi.org/10.3807/josk.2013.17.3.249.

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42

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 (December 2012): 883–99. http://dx.doi.org/10.1016/j.pmcj.2012.08.003.

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43

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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44

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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45

Doupis, John, Georgios Festas, Christos Tsilivigos, Vasiliki Efthymiou, and Alexander Kokkinos. "Smartphone-Based Technology in Diabetes Management." Diabetes Therapy 11, no. 3 (January 25, 2020): 607–19. http://dx.doi.org/10.1007/s13300-020-00768-3.

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46

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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47

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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48

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

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49

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

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50

Pradhan, Swadhin, Ghufran Baig, Wenguang Mao, Lili Qiu, Guohai Chen, and Bo Yang. "Smartphone-based Acoustic Indoor Space Mapping." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, no. 2 (July 5, 2018): 1–26. http://dx.doi.org/10.1145/3214278.

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