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Yeni Nesil Akıllı Kişisel Koruyucu Donanımların Sektörel Kullanımına Yönelik Niteliksel Bir İnceleme

Yıl 2025, Cilt: 37 Sayı: 2, 109 - 123

Ahmet Çabuk , Hızırcan Kartal , Enes Said Karataş , Eray Şeker , Sıla Özkaya , Maral Engin , Zeynep Nur Salkın , Saadet Karakuş , Serap Tepe

https://doi.org/10.7240/jeps.1598360

Öz

Akıllı teknolojilerle donatılan yeni nesil kişisel koruyucu donanımlar (KKD), çalışanların güvenliği ve iş verimliliğini artırmak için geliştirilen bir çözümdür. Akıllı KKD'ler, ortam koşullarını ve çalışan hareketlerini izleme, potansiyel tehlikeleri önceden tespit etme ve kazaları engelleme potansiyeline sahiptir. Bununla birlikte, yüksek maliyetler, eğitim eksiklikleri, yasal düzenlemelerin yetersizliği ve kullanıcı mahremiyeti gibi engeller bu teknolojilerin geniş ölçekli uygulamalarını kısıtlamaktadır. Bu çalışma, KKD’lerin iş güvenliği alanındaki kullanımını incelemektedir. Çalışmada, iş kazalarını önlemek, çalışma ortamını izlemek ve çalışan sağlığını korumak için akıllı sensörler, nesnelerin interneti (IoT), veri analitiği ve yapay zekâ gibi teknolojilerin KKD’lere entegre edilmesi ele alınmaktadır. Çalışma kapsamında, farklı sektörlerdeki iş güvenliği uzmanlarıyla yapılan görüşmeler sonucunda, akıllı KKD'lerin avantajları ve sınırlılıkları tartışılmakta, Türkiye'deki uygulamalara dair sektör temsilcilerinin görüşleri analiz edilmektedir. Katılımcılar, yüksek risk içeren işlerde akıllı KKD’lerin zorunlu hale getirilmesini savunurken, veri gizliliği ve psikolojik baskı gibi sorunlara dikkat çekmiştir. Çalışmada akıllı KKD'lerin iş güvenliği kültürüne katkısı vurgulanırken, maliyet-fayda analizlerinin yapılması ve teknolojinin etkinliğini artıracak yasal düzenlemelerin geliştirilmesi önerilmektedir.

Anahtar Kelimeler

Akıllı kişisel koruyucu donanımlar iş güvenliği akıllı teknolojiler veri gizliliği

Etik Beyan

Kurulumuza değerlendirilmek üzere sunulan Doç. Dr. Serap Tepe'nin sorumlu araştırmacı olduğu 24/697 kayıt numaralı "Yeni Nesil Akıllı Kişisel Koruyucu Donanımların Sektörel Kullanımına Yönelik Niteliksel Bir İnceleme" başlıklı proje önerisi kurulumuzun 28.11.2024 tarihli toplantısında değerlendirilmiş ve etik açıdan uygun bulunmuştur.

Kaynakça

  • Bhattacharjee, S., Joshi, R., Chughtai, A. A., & Macintyre, C. R. (2019). Graphene Modified Multifunctional Personal Protective Clothing. Advanced Materials Interfaces, 6(21). https://doi.org/10.1002/admi.201900622
  • Satapathy, S., Mishra, D., & Realyvásquez Vargas, A. (2022). Personal Protective Equipment for Farmers (pp. 69–78). https://doi.org/10.1007/978-3-030-88828-2_5
  • Del Giudice, A., Dellutri, M., Di Francia, G., Formisano, F., & Loffredo, G. (2023). S. A. L. V. O.: Towards a Smart Personal Protective Equipment (pp. 282–288). https://doi.org/10.1007/978-3-031-08136-1_44
  • Saidi, A., Gauvin, C., Ladhari, S., & Nguyen-Tri, P. (2021). Advanced functional materials for intelligent thermoregulation in personal protective equipment. Polymers, 13(21), 3711. https://doi.org/10.3390/polym13213711
  • Tripathi, G. K., Soni, A., Singh, P., Bundela, P., Khiriya, P., Khare, P. S., Dixit, P., & Sundaramurthy, S. (2024). “Advanced Conversion Technologies for PPEs and Their Recent Research Trends” (pp. 53–71). https://doi.org/10.1007/978-981-97-4692-7_3
  • Rossin, A. R. S., Spessato, L., Cardoso, F. da S. L., Caetano, J., Caetano, W., Radovanovic, E., & Dragunski, D. C. (2024). Electrospinning in personal protective equipment for healthcare work. Polymer Bulletin, 81(3), 1957–1980. https://doi.org/10.1007/s00289-023-04814-5
  • Shi, J., Li, H., Xu, F., & Tao, X. (2021). Materials in advanced design of personal protective equipment: a review. Materials Today Advances, 12, 100171. https://doi.org/10.1016/j.mtadv.2021.100171
  • Reaño, C., Riera, J. V., Romero, V., Morillo, P., & Casas-Yrurzum, S. (2024). A cloud-edge computing architecture for monitoring protective equipment. Journal of Cloud Computing, 13(1), 82. https://doi.org/10.1186/s13677-024-00649-1
  • Kim, I.-D. (2024). ACS Nano Strengthening Global Ties in South Korea. ACS Nano, 18(38), 25907–25909. https://doi.org/10.1021/acsnano.4c11864
  • Shen, J., Xiong, X., Li, Y., He, W., Li, P., & Zheng, X. (2021). Detecting safety helmet wearing on construction sites with bounding‐box regression and deep transfer learning. Computer-Aided Civil and Infrastructure Engineering, 36(2), 180–196. https://doi.org/10.1111/mice.12579
  • Yang, X., Yu, Y., Shirowzhan, S., sepasgozar, S., & Li, H. (2020). Automated PPE-Tool pair check system for construction safety using smart IoT. Journal of Building Engineering, 32, 101721. https://doi.org/10.1016/j.jobe.2020.101721
  • Ding, L., Jiang, W., & Zhou, C. (2022). IoT sensor-based BIM system for smart safety barriers of hazardous energy in petrochemical construction. Frontiers of Engineering Management, 9(1), 1–15. https://doi.org/10.1007/s42524-021-0160-6
  • Carmona, A. M., Chaparro, A. I., Velásquez, R., Botero-Valencia, J., Castano-Londono, L., Marquez-Viloria, D., & Mesa, A. M. (2019). Instrumentation and Data Collection Methodology to Enhance Productivity in Construction Sites Using Embedded Systems and IoT Technologies. In Advances in Informatics and Computing in Civil and Construction Engineering (pp. 637–644). Springer International Publishing. https://doi.org/10.1007/978-3-030-00220-6_76
  • Jiang, Y., & He, X. (2020). Overview of Applications of the Sensor Technologies for Construction Machinery. IEEE Access, 8, 110324–110335. https://doi.org/10.1109/ACCESS.2020.3001968
  • Kanan, R., Elhassan, O., & Bensalem, R. (2018). An IoT-based autonomous system for workers’ safety in construction sites with real-time alarming, monitoring, and positioning strategies. Automation in Construction, 88, 73–86. https://doi.org/10.1016/j.autcon.2017.12.033
  • Prabha, D., B, D., A, D. M., & K, S. (2021). IoT application for Safety and Health Monitoring System for Construction Workers. 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), 453–457. https://doi.org/10.1109/ICOEI51242.2021.9452911
  • Xu, Z., & Zheng, N. (2020). Incorporating Virtual Reality Technology in Safety Training Solution for Construction Site of Urban Cities. Sustainability, 13(1), 243. https://doi.org/10.3390/su13010243
  • Yong, C., Xudong, H., Guojun, L., & Ping, W. (2021). Research on safety risk early warning of tunnel construction based on BIM and RFID Technology. E3S Web of Conferences, 293, 02048. https://doi.org/10.1051/e3sconf/202129302048
  • Ahn, C. R., Lee, S., Sun, C., Jebelli, H., Yang, K., & Choi, B. (2019). Wearable Sensing Technology Applications in Construction Safety and Health. Journal of Construction Engineering and Management, 145(11). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001708
  • E. Angelia, R., S. Pangantihon Jr, R., & F. Villaverde, J. (2021). Wireless Sensor Network for Safety Tracking of Construction Workers through Hard Hat. 2021 7th International Conference on Computing and Artificial Intelligence, 412–417. https://doi.org/10.1145/3467707.3467769
  • Guo, H., Yu, Y., Xiang, T., Li, H., & Zhang, D. (2017). The availability of wearable-device-based physical data for the measurement of construction workers’ psychological status on site: From the perspective of safety management. Automation in Construction, 82, 207–217. https://doi.org/10.1016/j.autcon.2017.06.001
  • Jebelli, H., Hwang, S., & Lee, S. (2018). EEG-based workers’ stress recognition at construction sites. Automation in Construction, 93, 315–324. https://doi.org/10.1016/j.autcon.2018.05.027
  • Goar, V., Sharma, A., Yadav, N. S., Chowdhury, S., & Hu, Y.-C. (2023). IoT-Based Smart Mask Protection against the Waves of COVID-19. Journal of Ambient Intelligence and Humanized Computing, 14(8), 11153–11164. https://doi.org/10.1007/s12652-022-04395-7
  • Kim, H., Tae, S., Zheng, P., Kang, G., & Lee, H. (2021). Development of IoT-Based Particulate Matter Monitoring System for Construction Sites. International Journal of Environmental Research and Public Health, 18(21), 11510. https://doi.org/10.3390/ijerph182111510
  • Mayton, B., Dublon, G., Palacios, S., & Paradiso, J. A. (2012). TRUSS: Tracking Risk with Ubiquitous Smart Sensing. 2012 IEEE Sensors, 1–4. https://doi.org/10.1109/ICSENS.2012.6411393
  • Fugini, M., Conti, G. M., Rizzo, F., Raibulet, C., & Ubezio, L. (2009). Wearable Services in Risk Management. 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 563–566. https://doi.org/10.1109/WI-IAT.2009.350
  • Cheng, T., & Teizer, J. (2013). Real-time resource location data collection and visualization technology for construction safety and activity monitoring applications. Automation in Construction, 34, 3–15. https://doi.org/10.1016/j.autcon.2012.10.017
  • Liu, H., Song, J., & Wang, G. (2021). A Scientometric Review of Smart Construction Site in Construction Engineering and Management: Analysis and Visualization. Sustainability, 13(16), 8860. https://doi.org/10.3390/su13168860
  • Marks, E., & Teizer, J. (2012). Proximity Sensing and Warning Technology for Heavy Construction Equipment Operation. Construction Research Congress 2012, 981–990. https://doi.org/10.1061/9780784412329.099
  • Guo, S. Y., Ding, L. Y., Luo, H. B., & Jiang, X. Y. (2016). A Big-Data-based platform of workers’ behavior: Observations from the field. Accident Analysis & Prevention, 93, 299–309. https://doi.org/10.1016/j.aap.2015.09.024
  • Rey-Merchán, M. del C., Gómez-de-Gabriel, J. M., López-Arquillos, A., & Fernández-Madrigal, J. A. (2021). Virtual Fence System Based on IoT Paradigm to Prevent Occupational Accidents in the Construction Sector. International Journal of Environmental Research and Public Health, 18(13), 6839. https://doi.org/10.3390/ijerph18136839
  • Huang, L., Fu, Q., He, M., Jiang, D., & Hao, Z. (2021). Detection algorithm of safety helmet wearing based on deep learning. Concurrency and Computation: Practice and Experience, 33(13). https://doi.org/10.1002/cpe.6234
  • Abbasianjahromi, H., & Sohrab Ghazvini, E. (2022). Developing a Wearable Device Based on IoT to Monitor the Use of Personal Protective Equipment in Construction Projects. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46(3), 2561–2573. https://doi.org/10.1007/s40996-021-00716-6
  • Adjiski, V., Despodov, Z., Mirakovski, D., & Serafimovski, D. (2019). System Archıtecture To Brıng Smart Personal Protectıve Equıpment Wearables And Sensors To Transform Safety At Work In The Underground Mınıng Industry. Rudarsko-Geološko-Naftni Zbornik, 34(1), 37–44. https://doi.org/10.17794/rgn.2019.1.4
  • Arcayena, R. D., Ballarta, A. D., Claros, K. N., & Pangantihon, R. S. (2019). Development of Arduino Microcontroller-based Safety Monitoring Prototype in the Hard Hat. Proceedings of the 2019 6th International Conference on Bioinformatics Research and Applications, 119–124. https://doi.org/10.1145/3383783.3383790
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A Qualitative Examination of the Sectoral Applications of Next-Generation Smart Personal Protective Equipment

Yıl 2025, Cilt: 37 Sayı: 2, 109 - 123

Ahmet Çabuk , Hızırcan Kartal , Enes Said Karataş , Eray Şeker , Sıla Özkaya , Maral Engin , Zeynep Nur Salkın , Saadet Karakuş , Serap Tepe

https://doi.org/10.7240/jeps.1598360

Öz

The next generation of personal protective equipment (PPE) equipped with smart technologies represents an innovative solution designed to enhance worker safety and operational efficiency. Smart PPE possesses the capability to monitor environmental conditions and employee movements, allowing for the proactive identification of potential hazards and the prevention of accidents. However, several barriers, including high costs, deficiencies in training, inadequate regulatory frameworks, and concerns regarding user privacy, limit the widespread adoption of these technologies. This paper examines the application of PPE in the field of occupational safety. It discusses the integration of technologies such as smart sensors, the Internet of Things (IoT), data analytics, and artificial intelligence into PPE to prevent workplace accidents, monitor work environments, and safeguard employee health. Through interviews conducted with occupational safety experts across various sectors, the paper explores the advantages and limitations of smart PPE and analyzes the perspectives of industry representatives regarding its implementation in Turkey. Participants advocate for the mandatory adoption of smart PPE in high-risk occupations while highlighting issues such as data privacy and psychological pressure. The paper emphasizes the contribution of smart PPE to the culture of occupational safety, recommending that cost-benefit analyses be conducted and that legal regulations be developed to enhance the effectiveness of the technology.

Anahtar Kelimeler

Smart personal protective equipment occupational safety smart technologies data privacy

Kaynakça

  • Bhattacharjee, S., Joshi, R., Chughtai, A. A., & Macintyre, C. R. (2019). Graphene Modified Multifunctional Personal Protective Clothing. Advanced Materials Interfaces, 6(21). https://doi.org/10.1002/admi.201900622
  • Satapathy, S., Mishra, D., & Realyvásquez Vargas, A. (2022). Personal Protective Equipment for Farmers (pp. 69–78). https://doi.org/10.1007/978-3-030-88828-2_5
  • Del Giudice, A., Dellutri, M., Di Francia, G., Formisano, F., & Loffredo, G. (2023). S. A. L. V. O.: Towards a Smart Personal Protective Equipment (pp. 282–288). https://doi.org/10.1007/978-3-031-08136-1_44
  • Saidi, A., Gauvin, C., Ladhari, S., & Nguyen-Tri, P. (2021). Advanced functional materials for intelligent thermoregulation in personal protective equipment. Polymers, 13(21), 3711. https://doi.org/10.3390/polym13213711
  • Tripathi, G. K., Soni, A., Singh, P., Bundela, P., Khiriya, P., Khare, P. S., Dixit, P., & Sundaramurthy, S. (2024). “Advanced Conversion Technologies for PPEs and Their Recent Research Trends” (pp. 53–71). https://doi.org/10.1007/978-981-97-4692-7_3
  • Rossin, A. R. S., Spessato, L., Cardoso, F. da S. L., Caetano, J., Caetano, W., Radovanovic, E., & Dragunski, D. C. (2024). Electrospinning in personal protective equipment for healthcare work. Polymer Bulletin, 81(3), 1957–1980. https://doi.org/10.1007/s00289-023-04814-5
  • Shi, J., Li, H., Xu, F., & Tao, X. (2021). Materials in advanced design of personal protective equipment: a review. Materials Today Advances, 12, 100171. https://doi.org/10.1016/j.mtadv.2021.100171
  • Reaño, C., Riera, J. V., Romero, V., Morillo, P., & Casas-Yrurzum, S. (2024). A cloud-edge computing architecture for monitoring protective equipment. Journal of Cloud Computing, 13(1), 82. https://doi.org/10.1186/s13677-024-00649-1
  • Kim, I.-D. (2024). ACS Nano Strengthening Global Ties in South Korea. ACS Nano, 18(38), 25907–25909. https://doi.org/10.1021/acsnano.4c11864
  • Shen, J., Xiong, X., Li, Y., He, W., Li, P., & Zheng, X. (2021). Detecting safety helmet wearing on construction sites with bounding‐box regression and deep transfer learning. Computer-Aided Civil and Infrastructure Engineering, 36(2), 180–196. https://doi.org/10.1111/mice.12579
  • Yang, X., Yu, Y., Shirowzhan, S., sepasgozar, S., & Li, H. (2020). Automated PPE-Tool pair check system for construction safety using smart IoT. Journal of Building Engineering, 32, 101721. https://doi.org/10.1016/j.jobe.2020.101721
  • Ding, L., Jiang, W., & Zhou, C. (2022). IoT sensor-based BIM system for smart safety barriers of hazardous energy in petrochemical construction. Frontiers of Engineering Management, 9(1), 1–15. https://doi.org/10.1007/s42524-021-0160-6
  • Carmona, A. M., Chaparro, A. I., Velásquez, R., Botero-Valencia, J., Castano-Londono, L., Marquez-Viloria, D., & Mesa, A. M. (2019). Instrumentation and Data Collection Methodology to Enhance Productivity in Construction Sites Using Embedded Systems and IoT Technologies. In Advances in Informatics and Computing in Civil and Construction Engineering (pp. 637–644). Springer International Publishing. https://doi.org/10.1007/978-3-030-00220-6_76
  • Jiang, Y., & He, X. (2020). Overview of Applications of the Sensor Technologies for Construction Machinery. IEEE Access, 8, 110324–110335. https://doi.org/10.1109/ACCESS.2020.3001968
  • Kanan, R., Elhassan, O., & Bensalem, R. (2018). An IoT-based autonomous system for workers’ safety in construction sites with real-time alarming, monitoring, and positioning strategies. Automation in Construction, 88, 73–86. https://doi.org/10.1016/j.autcon.2017.12.033
  • Prabha, D., B, D., A, D. M., & K, S. (2021). IoT application for Safety and Health Monitoring System for Construction Workers. 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), 453–457. https://doi.org/10.1109/ICOEI51242.2021.9452911
  • Xu, Z., & Zheng, N. (2020). Incorporating Virtual Reality Technology in Safety Training Solution for Construction Site of Urban Cities. Sustainability, 13(1), 243. https://doi.org/10.3390/su13010243
  • Yong, C., Xudong, H., Guojun, L., & Ping, W. (2021). Research on safety risk early warning of tunnel construction based on BIM and RFID Technology. E3S Web of Conferences, 293, 02048. https://doi.org/10.1051/e3sconf/202129302048
  • Ahn, C. R., Lee, S., Sun, C., Jebelli, H., Yang, K., & Choi, B. (2019). Wearable Sensing Technology Applications in Construction Safety and Health. Journal of Construction Engineering and Management, 145(11). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001708
  • E. Angelia, R., S. Pangantihon Jr, R., & F. Villaverde, J. (2021). Wireless Sensor Network for Safety Tracking of Construction Workers through Hard Hat. 2021 7th International Conference on Computing and Artificial Intelligence, 412–417. https://doi.org/10.1145/3467707.3467769
  • Guo, H., Yu, Y., Xiang, T., Li, H., & Zhang, D. (2017). The availability of wearable-device-based physical data for the measurement of construction workers’ psychological status on site: From the perspective of safety management. Automation in Construction, 82, 207–217. https://doi.org/10.1016/j.autcon.2017.06.001
  • Jebelli, H., Hwang, S., & Lee, S. (2018). EEG-based workers’ stress recognition at construction sites. Automation in Construction, 93, 315–324. https://doi.org/10.1016/j.autcon.2018.05.027
  • Goar, V., Sharma, A., Yadav, N. S., Chowdhury, S., & Hu, Y.-C. (2023). IoT-Based Smart Mask Protection against the Waves of COVID-19. Journal of Ambient Intelligence and Humanized Computing, 14(8), 11153–11164. https://doi.org/10.1007/s12652-022-04395-7
  • Kim, H., Tae, S., Zheng, P., Kang, G., & Lee, H. (2021). Development of IoT-Based Particulate Matter Monitoring System for Construction Sites. International Journal of Environmental Research and Public Health, 18(21), 11510. https://doi.org/10.3390/ijerph182111510
  • Mayton, B., Dublon, G., Palacios, S., & Paradiso, J. A. (2012). TRUSS: Tracking Risk with Ubiquitous Smart Sensing. 2012 IEEE Sensors, 1–4. https://doi.org/10.1109/ICSENS.2012.6411393
  • Fugini, M., Conti, G. M., Rizzo, F., Raibulet, C., & Ubezio, L. (2009). Wearable Services in Risk Management. 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 563–566. https://doi.org/10.1109/WI-IAT.2009.350
  • Cheng, T., & Teizer, J. (2013). Real-time resource location data collection and visualization technology for construction safety and activity monitoring applications. Automation in Construction, 34, 3–15. https://doi.org/10.1016/j.autcon.2012.10.017
  • Liu, H., Song, J., & Wang, G. (2021). A Scientometric Review of Smart Construction Site in Construction Engineering and Management: Analysis and Visualization. Sustainability, 13(16), 8860. https://doi.org/10.3390/su13168860
  • Marks, E., & Teizer, J. (2012). Proximity Sensing and Warning Technology for Heavy Construction Equipment Operation. Construction Research Congress 2012, 981–990. https://doi.org/10.1061/9780784412329.099
  • Guo, S. Y., Ding, L. Y., Luo, H. B., & Jiang, X. Y. (2016). A Big-Data-based platform of workers’ behavior: Observations from the field. Accident Analysis & Prevention, 93, 299–309. https://doi.org/10.1016/j.aap.2015.09.024
  • Rey-Merchán, M. del C., Gómez-de-Gabriel, J. M., López-Arquillos, A., & Fernández-Madrigal, J. A. (2021). Virtual Fence System Based on IoT Paradigm to Prevent Occupational Accidents in the Construction Sector. International Journal of Environmental Research and Public Health, 18(13), 6839. https://doi.org/10.3390/ijerph18136839
  • Huang, L., Fu, Q., He, M., Jiang, D., & Hao, Z. (2021). Detection algorithm of safety helmet wearing based on deep learning. Concurrency and Computation: Practice and Experience, 33(13). https://doi.org/10.1002/cpe.6234
  • Abbasianjahromi, H., & Sohrab Ghazvini, E. (2022). Developing a Wearable Device Based on IoT to Monitor the Use of Personal Protective Equipment in Construction Projects. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46(3), 2561–2573. https://doi.org/10.1007/s40996-021-00716-6
  • Adjiski, V., Despodov, Z., Mirakovski, D., & Serafimovski, D. (2019). System Archıtecture To Brıng Smart Personal Protectıve Equıpment Wearables And Sensors To Transform Safety At Work In The Underground Mınıng Industry. Rudarsko-Geološko-Naftni Zbornik, 34(1), 37–44. https://doi.org/10.17794/rgn.2019.1.4
  • Arcayena, R. D., Ballarta, A. D., Claros, K. N., & Pangantihon, R. S. (2019). Development of Arduino Microcontroller-based Safety Monitoring Prototype in the Hard Hat. Proceedings of the 2019 6th International Conference on Bioinformatics Research and Applications, 119–124. https://doi.org/10.1145/3383783.3383790
  • Balakreshnan, B., Richards, G., Nanda, G., Mao, H., Athinarayanan, R., & Zaccaria, J. (2020). PPE Compliance Detection using Artificial Intelligence in Learning Factories. Procedia Manufacturing, 45, 277–282. https://doi.org/10.1016/j.promfg.2020.04.017
  • Harito, C., Utari, L., Putra, B. R., Yuliarto, B., Purwanto, S., Zaidi, S. Z. J., Bavykin, D. V., Marken, F., & Walsh, F. C. (2020). Review—The Development of Wearable Polymer-Based Sensors: Perspectives. Journal of The Electrochemical Society, 167(3), 037566. https://doi.org/10.1149/1945-7111/ab697c
  • Ji, X., Gong, F., Yuan, X., & Wang, N. (2023). A high-performance framework for personal protective equipment detection on the offshore drilling platform. Complex & Intelligent Systems, 9(5), 5637–5652. https://doi.org/10.1007/s40747-023-01028-0
  • Li, Y., Wei, H., Han, Z., Huang, J., & Wang, W. (2020). Deep Learning‐Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks. Advances in Civil Engineering, 2020(1). https://doi.org/10.1155/2020/9703560
  • Slade Shantz, J. A., & Veillette, C. J. H. (2014). The Application of Wearable Technology in Surgery: Ensuring the Positive Impact of the Wearable Revolution on Surgical Patients. Frontiers in Surgery, 1. https://doi.org/10.3389/fsurg.2014.00039
  • Ghosh, S., Dave, V., Sharma, P., Patel, A., & Kuila, A. (2023). Protective face mask: an effective weapon against SARS-CoV-2 with controlled environmental pollution. Environmental Science and Pollution Research, 31(29), 41656–41682. https://doi.org/10.1007/s11356-023-30460-5
  • Rao, P. M., & Deebak, B. D. (2023). Security and privacy issues in smart cities/industries: technologies, applications, and challenges. Journal of Ambient Intelligence and Humanized Computing, 14(8), 10517–10553. https://doi.org/10.1007/s12652-022-03707-1
  • Sung, C.-H., & Lu, M.-C. (2023). Protection of personal privacy under the development of the Internet of Things. Wireless Networks. https://doi.org/10.1007/s11276-023-03569-1
  • Han, T.-S., Kim, D., Kwon, O.-Y., & Choa, S.-H. (2016). Study of Standardization and Test Certification for Wearable Smart Devices. Journal of the Microelectronics and Packaging Society, 23(4), 11–18. https://doi.org/10.6117/kmeps.2016.23.4.011
  • Xu, Q., Chong, H.-Y., & Liao, P.-C. (2019). Collaborative information integration for construction safety monitoring. Automation in Construction, 102, 120–134. https://doi.org/10.1016/j.autcon.2019.02.004
  • Kanun, 6331 Sayılı İş Sağlığı ve Güvenliği Kanunu, 30.06.2012 tarihli ve 28339 sayılı Resmî Gazete
  • Yönetmelik, Kişisel Koruyucu Donanım Yönetmeliği, 01.05.2019 tarihli ve 30761 sayılı Resmî Gazete
  • Yönetmelik, Kişisel Koruyucu Donanımların İşyerlerinde Kullanılması Hakkında Yönetmelik, 02.07.2013 tarihli ve 28695 sayılı Resmî Gazete
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Veri ve Bilgi Gizliliği, Akıllı Robotik
Bölüm Araştırma Makaleleri
Yazarlar

Ahmet Çabuk 0000-0002-5302-1847

Hızırcan Kartal 0009-0002-5181-4859

Enes Said Karataş 0009-0008-6795-3291

Eray Şeker 0009-0007-8755-4135

Sıla Özkaya 0009-0007-1748-9219

Maral Engin 0009-0007-3817-5112

Zeynep Nur Salkın 0009-0007-8129-2760

Saadet Karakuş 0000-0002-6326-5941

Serap Tepe 0000-0002-9723-6049

Erken Görünüm Tarihi 16 Haziran 2025
Yayımlanma Tarihi
Gönderilme Tarihi 10 Aralık 2024
Kabul Tarihi 18 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 37 Sayı: 2

Kaynak Göster

APA Çabuk, A., Kartal, H., Karataş, E. S., Şeker, E., vd. (2025). Yeni Nesil Akıllı Kişisel Koruyucu Donanımların Sektörel Kullanımına Yönelik Niteliksel Bir İnceleme. International Journal of Advances in Engineering and Pure Sciences, 37(2), 109-123. https://doi.org/10.7240/jeps.1598360
AMA Çabuk A, Kartal H, Karataş ES, Şeker E, Özkaya S, Engin M, Salkın ZN, Karakuş S, Tepe S. Yeni Nesil Akıllı Kişisel Koruyucu Donanımların Sektörel Kullanımına Yönelik Niteliksel Bir İnceleme. JEPS. Haziran 2025;37(2):109-123. doi:10.7240/jeps.1598360
Chicago Çabuk, Ahmet, Hızırcan Kartal, Enes Said Karataş, Eray Şeker, Sıla Özkaya, Maral Engin, Zeynep Nur Salkın, Saadet Karakuş, ve Serap Tepe. “Yeni Nesil Akıllı Kişisel Koruyucu Donanımların Sektörel Kullanımına Yönelik Niteliksel Bir İnceleme”. International Journal of Advances in Engineering and Pure Sciences 37, sy. 2 (Haziran 2025): 109-23. https://doi.org/10.7240/jeps.1598360.
EndNote Çabuk A, Kartal H, Karataş ES, Şeker E, Özkaya S, Engin M, Salkın ZN, Karakuş S, Tepe S (01 Haziran 2025) Yeni Nesil Akıllı Kişisel Koruyucu Donanımların Sektörel Kullanımına Yönelik Niteliksel Bir İnceleme. International Journal of Advances in Engineering and Pure Sciences 37 2 109–123.
IEEE A. Çabuk, H. Kartal, E. S. Karataş, E. Şeker, S. Özkaya, M. Engin, Z. N. Salkın, S. Karakuş, ve S. Tepe, “Yeni Nesil Akıllı Kişisel Koruyucu Donanımların Sektörel Kullanımına Yönelik Niteliksel Bir İnceleme”, JEPS, c. 37, sy. 2, ss. 109–123, 2025, doi: 10.7240/jeps.1598360.
ISNAD Çabuk, Ahmet vd. “Yeni Nesil Akıllı Kişisel Koruyucu Donanımların Sektörel Kullanımına Yönelik Niteliksel Bir İnceleme”. International Journal of Advances in Engineering and Pure Sciences 37/2 (Haziran 2025), 109-123. https://doi.org/10.7240/jeps.1598360.
JAMA Çabuk A, Kartal H, Karataş ES, Şeker E, Özkaya S, Engin M, Salkın ZN, Karakuş S, Tepe S. Yeni Nesil Akıllı Kişisel Koruyucu Donanımların Sektörel Kullanımına Yönelik Niteliksel Bir İnceleme. JEPS. 2025;37:109–123.
MLA Çabuk, Ahmet vd. “Yeni Nesil Akıllı Kişisel Koruyucu Donanımların Sektörel Kullanımına Yönelik Niteliksel Bir İnceleme”. International Journal of Advances in Engineering and Pure Sciences, c. 37, sy. 2, 2025, ss. 109-23, doi:10.7240/jeps.1598360.
Vancouver Çabuk A, Kartal H, Karataş ES, Şeker E, Özkaya S, Engin M, Salkın ZN, Karakuş S, Tepe S. Yeni Nesil Akıllı Kişisel Koruyucu Donanımların Sektörel Kullanımına Yönelik Niteliksel Bir İnceleme. JEPS. 2025;37(2):109-23.
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