Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, , 169 - 174, 07.07.2024
https://doi.org/10.31202/ecjse.1369680

Öz

Kaynakça

  • [1] S. Chen et al., ‘‘Internet of things based smart grids supported by intelligent edge computing,’’ IEEE Access, vol. 7, pp. 74 089–74 102, 2019.
  • [2] M. Hartmann, U. S. Hashmi, and A. Imran, ‘‘Edge computing in smart health care systems: Review, challenges, and research directions,’’ Trans. Emerg. Telecommun. Technol., vol. 33, no. 3, p. e3710, 2022.
  • [3] Z. Ning et al., ‘‘Mobile edge computing enabled 5g health monitoring for internet of medical things: A decentralized game theoretic approach,’’ IEEE J. Sel. Areas Commun., vol. 39, no. 2, pp. 463–478, 2020.
  • [4] S. S. Tripathy et al., ‘‘A novel edge-computing-based framework for an intelligent smart healthcare system in smart cities,’’ Sustainability, vol. 15, no. 1, p. 735, 2022.
  • [5] G. Aloi, G. Fortino, R. Gravina, P. Pace, and G. Caliciuri, ‘‘Edge computing-enabled body area networks,’’ in 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2018, pp. 349–353.
  • [6] S.Wan, L. Qi, X. Xu, C. Tong, and Z. Gu, ‘‘Deep learning models for real-time human activity recognition with smartphones,’’ Mob. Networks Appl., vol. 25, pp. 743–755, 2020.
  • [7] P. Verma and S. Fatima, ‘‘Smart healthcare applications and real-time analytics through edge computing. internet things use cases healthcare ind,’’ A Rev. edge Comput. Healthc. internet things, vol. 275, no. 276, pp. 241–270, 2020.
  • [8] S. U. Amin and M. S. Hossain, ‘‘Edge intelligence and internet of things in healthcare: A survey,’’ IEEE Access, vol. 9, pp. 45–59, 2020.
  • [9] M. A. Rahman and M. S. Hossain, ‘‘An internet-of-medical-things-enabled edge computing framework for tackling covid-19,’’ IEEE Internet Things J., vol. 8, no. 21, pp. 15 847–15 854, 2021.
  • [10] V. Vakamullu, S. Trivedy, M. Mishra, and A. Mukherjee, ‘‘Convolutional neural network based heart sounds recognition on edge computing platform,’’ in 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2022, pp. 1–6.
  • [11] U. D. Ulusar, E. Turk, A. S. Oztas, A. E. Savli, G. Ogunc, and M. Canpolat, ‘‘Iot and edge computing as a tool for bowel activity monitoring,’’ in Edge Comput. From Hype to Real., 2019, pp. 133–144.
  • [12] A. Sacco, F. Esposito, G. Marchetto, G. Kolar, and K. Schwetye, ‘‘On edge computing for remote pathology consultations and computations,’’ IEEE J. Biomed. Heal. Informatics, vol. 24, no. 9, pp. 2523–2534, 2020.
  • [13] S. M. Lehman and C. C. Tan, ‘‘Leveraging edge computing for mobile augmented reality,’’ in Fog/Edge Comput. Secur. Privacy, Appl., 2021, pp. 327–353.
  • [14] M. Elawady and A. Sarhan, ‘‘Mixed reality applications powered by ioe and edge computing: A survey,’’ in Internet of Things—Applications and Future: Proceedings of ITAF 2019, 2020, pp. 125–138.
  • [15] C. S. M. Babou, D. Fall, S. Kashihara, I. Niang, and Y. Kadobayashi, ‘‘Home edge computing (hec): Design of a new edge computing technology for achieving ultra-low latency,’’ in Edge Computing–EDGE 2018: Second International Conference, Held as Part of the Services Conference Federation, SCF 2018, Seattle, WA, USA, June 25-30, 2018, Proceedings 2, 2018, pp. 3–17.
  • [16] S. S. Keum, Y. J. Park, and S. J. Kang, ‘‘Edge computing-based self-organized device network for awareness activities of daily living in the home,’’ Appl. Sci., vol. 10, no. 7, p. 2475, 2020.
  • [17] A. Jenifer et al., ‘‘Edge-based heart disease prediction device using internet of things,’’ in 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 2022, pp. 1500–1504.
  • [18] J. A. Miranda, M. F. Canabal, L. Gutiérrez-Martín, J. M. Lanza-Gutiérrez, and C. López-Ongil, ‘‘Edge computing design space exploration for heart rate monitoring,’’ Integration, vol. 84, pp. 171–179, 2022.
  • [19] M. Prabhu and A. Hanumanthaiah, ‘‘Edge computing-enabled healthcare framework to provide telehealth services,’’ in 2022 International Conference onWireless Communications Signal Processing and Networking (WiSPNET), 2022, pp. 349–353.
  • [20] A. A. Alli and M. M. Alam, ‘‘The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications,’’ Internet of Things, vol. 9, p. 100177, 2020.
  • [21] S. Boss and J. Krauss, Reinventing project-based learning: Your field guide to real-world projects in the digital age. International Society for Technology in Education, 2022.
  • [22] S. Han and K. Bhattacharya, ‘‘Constructionism, learning by design, and project based learning,’’ in Emerg. Perspect. Learn. teaching, Technol., 2001, pp. 127–141.
  • [23] A. Steinemann, ‘‘Implementing sustainable development through problem-based learning: Pedagogy and practice,’’ J. Prof. Issues Eng. Educ. Pract., vol. 129, no. 4, pp. 216–224, 2003.
  • [24] S. H. Hong, ‘‘Edge impulse machine learning for embedded system design,’’ J. Korea Soc. Digit. Ind. Inf. Manag., vol. 17, no. 3, pp. 9–15, 2021.
  • [25] O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, ‘‘Systematic review of research on artificial intelligence applications in higher education – where are the educators?’’ Int. J. Educ. Technol. High. Educ., vol. 16, no. 1, p. 39, 2019.
  • [26] J. Sak and M. Suchodolska, ‘‘Artificial intelligence in nutrients science research: A review,’’ Nutrients, vol. 13, no. 2, p. 322, 2021.
  • [27] G. Briganti and O. L. Moine, ‘‘Artificial intelligence in medicine: Today and tomorrow,’’ Front. Med., vol. 7, 2020.

Teaching Effective Techniques for Incorporating Edge Computing in Biomedical Applications with Biomedical Students

Yıl 2024, , 169 - 174, 07.07.2024
https://doi.org/10.31202/ecjse.1369680

Öz

This case study demonstrates the potential of integrating the Edge Impulse platform into project based learning for biomedical technology students for edge computing consept. The use of the Edge Impulse platform as a project-based teaching and learning approach for biomedical students has been shown to significantly enhance student learning performance and experiences. The platform allows students to develop practical skills and deepen their knowledge while also providing opportunities for students to apply their projects in real-world settings. This study provides valuable insights on how to effectively improve learning for biomedical students using the Edge Impulse platform. The study focuses on a course designed using the Edge Impulse platform, where students design and develop prototype devices and systems. The course begins with the learning of basic hardware and culminates in students creating a complete prototype system. The study showcases the prototype devices and systems developed by students at the end of the course, demonstrating how the use of Edge Impulse platform improves students’ practical skills and enables them to apply their projects in real-world settings. Furthermore, it highlights how this approach contributes to students’ self-development and acquisition of skills that can be used in their future careers. Thus, the use of this platform can lead to new ideas and experiences which may open new horizons for biomedical research and the development of integrated devices in the current world.

Kaynakça

  • [1] S. Chen et al., ‘‘Internet of things based smart grids supported by intelligent edge computing,’’ IEEE Access, vol. 7, pp. 74 089–74 102, 2019.
  • [2] M. Hartmann, U. S. Hashmi, and A. Imran, ‘‘Edge computing in smart health care systems: Review, challenges, and research directions,’’ Trans. Emerg. Telecommun. Technol., vol. 33, no. 3, p. e3710, 2022.
  • [3] Z. Ning et al., ‘‘Mobile edge computing enabled 5g health monitoring for internet of medical things: A decentralized game theoretic approach,’’ IEEE J. Sel. Areas Commun., vol. 39, no. 2, pp. 463–478, 2020.
  • [4] S. S. Tripathy et al., ‘‘A novel edge-computing-based framework for an intelligent smart healthcare system in smart cities,’’ Sustainability, vol. 15, no. 1, p. 735, 2022.
  • [5] G. Aloi, G. Fortino, R. Gravina, P. Pace, and G. Caliciuri, ‘‘Edge computing-enabled body area networks,’’ in 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2018, pp. 349–353.
  • [6] S.Wan, L. Qi, X. Xu, C. Tong, and Z. Gu, ‘‘Deep learning models for real-time human activity recognition with smartphones,’’ Mob. Networks Appl., vol. 25, pp. 743–755, 2020.
  • [7] P. Verma and S. Fatima, ‘‘Smart healthcare applications and real-time analytics through edge computing. internet things use cases healthcare ind,’’ A Rev. edge Comput. Healthc. internet things, vol. 275, no. 276, pp. 241–270, 2020.
  • [8] S. U. Amin and M. S. Hossain, ‘‘Edge intelligence and internet of things in healthcare: A survey,’’ IEEE Access, vol. 9, pp. 45–59, 2020.
  • [9] M. A. Rahman and M. S. Hossain, ‘‘An internet-of-medical-things-enabled edge computing framework for tackling covid-19,’’ IEEE Internet Things J., vol. 8, no. 21, pp. 15 847–15 854, 2021.
  • [10] V. Vakamullu, S. Trivedy, M. Mishra, and A. Mukherjee, ‘‘Convolutional neural network based heart sounds recognition on edge computing platform,’’ in 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2022, pp. 1–6.
  • [11] U. D. Ulusar, E. Turk, A. S. Oztas, A. E. Savli, G. Ogunc, and M. Canpolat, ‘‘Iot and edge computing as a tool for bowel activity monitoring,’’ in Edge Comput. From Hype to Real., 2019, pp. 133–144.
  • [12] A. Sacco, F. Esposito, G. Marchetto, G. Kolar, and K. Schwetye, ‘‘On edge computing for remote pathology consultations and computations,’’ IEEE J. Biomed. Heal. Informatics, vol. 24, no. 9, pp. 2523–2534, 2020.
  • [13] S. M. Lehman and C. C. Tan, ‘‘Leveraging edge computing for mobile augmented reality,’’ in Fog/Edge Comput. Secur. Privacy, Appl., 2021, pp. 327–353.
  • [14] M. Elawady and A. Sarhan, ‘‘Mixed reality applications powered by ioe and edge computing: A survey,’’ in Internet of Things—Applications and Future: Proceedings of ITAF 2019, 2020, pp. 125–138.
  • [15] C. S. M. Babou, D. Fall, S. Kashihara, I. Niang, and Y. Kadobayashi, ‘‘Home edge computing (hec): Design of a new edge computing technology for achieving ultra-low latency,’’ in Edge Computing–EDGE 2018: Second International Conference, Held as Part of the Services Conference Federation, SCF 2018, Seattle, WA, USA, June 25-30, 2018, Proceedings 2, 2018, pp. 3–17.
  • [16] S. S. Keum, Y. J. Park, and S. J. Kang, ‘‘Edge computing-based self-organized device network for awareness activities of daily living in the home,’’ Appl. Sci., vol. 10, no. 7, p. 2475, 2020.
  • [17] A. Jenifer et al., ‘‘Edge-based heart disease prediction device using internet of things,’’ in 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 2022, pp. 1500–1504.
  • [18] J. A. Miranda, M. F. Canabal, L. Gutiérrez-Martín, J. M. Lanza-Gutiérrez, and C. López-Ongil, ‘‘Edge computing design space exploration for heart rate monitoring,’’ Integration, vol. 84, pp. 171–179, 2022.
  • [19] M. Prabhu and A. Hanumanthaiah, ‘‘Edge computing-enabled healthcare framework to provide telehealth services,’’ in 2022 International Conference onWireless Communications Signal Processing and Networking (WiSPNET), 2022, pp. 349–353.
  • [20] A. A. Alli and M. M. Alam, ‘‘The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications,’’ Internet of Things, vol. 9, p. 100177, 2020.
  • [21] S. Boss and J. Krauss, Reinventing project-based learning: Your field guide to real-world projects in the digital age. International Society for Technology in Education, 2022.
  • [22] S. Han and K. Bhattacharya, ‘‘Constructionism, learning by design, and project based learning,’’ in Emerg. Perspect. Learn. teaching, Technol., 2001, pp. 127–141.
  • [23] A. Steinemann, ‘‘Implementing sustainable development through problem-based learning: Pedagogy and practice,’’ J. Prof. Issues Eng. Educ. Pract., vol. 129, no. 4, pp. 216–224, 2003.
  • [24] S. H. Hong, ‘‘Edge impulse machine learning for embedded system design,’’ J. Korea Soc. Digit. Ind. Inf. Manag., vol. 17, no. 3, pp. 9–15, 2021.
  • [25] O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, ‘‘Systematic review of research on artificial intelligence applications in higher education – where are the educators?’’ Int. J. Educ. Technol. High. Educ., vol. 16, no. 1, p. 39, 2019.
  • [26] J. Sak and M. Suchodolska, ‘‘Artificial intelligence in nutrients science research: A review,’’ Nutrients, vol. 13, no. 2, p. 322, 2021.
  • [27] G. Briganti and O. L. Moine, ‘‘Artificial intelligence in medicine: Today and tomorrow,’’ Front. Med., vol. 7, 2020.
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik Eğitimi, Mühendislik Uygulaması, Mühendislik Uygulaması ve Eğitim (Diğer)
Bölüm Research Articles
Yazarlar

Mahmut Durgun 0000-0002-5010-687X

Yeliz Durgun 0000-0003-3834-5533

Yayımlanma Tarihi 7 Temmuz 2024
Gönderilme Tarihi 1 Ekim 2023
Kabul Tarihi 21 Mayıs 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

IEEE M. Durgun ve Y. Durgun, “Teaching Effective Techniques for Incorporating Edge Computing in Biomedical Applications with Biomedical Students”, ECJSE, c. 11, sy. 2, ss. 169–174, 2024, doi: 10.31202/ecjse.1369680.