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Wearable Technologies and Internet of Things for Real-Time Hazard Monitoring in Occupational Health and Safety: A Systematic Content Analysis

Yıl 2025, Cilt: 1 Sayı: 2, 45 - 54, 29.09.2025

Öz

The rapid digitalisation of work under Industry 4.0 has introduced both opportunities and challenges for occupational health and safety (OHS). Wearable and Internet of Things (IoT) technologies are increasingly deployed to enable real-time monitoring, predictive analytics, and proactive risk management, advancing the concept of “Safety 4.0.” This study conducts a systematic content analysis of international literature published between 2010 and June 2025, synthesising evidence across device types, industrial sectors, predictive safety integration, and ethical concerns. Findings show that monitoring devices such as smart helmets, wristbands, and connected vests dominate the literature, with strong uptake in high-risk sectors like construction, mining, and manufacturing, while healthcare and logistics are emerging areas of adoption. Thematic analysis highlights the evolution of personal protective equipment into active sensing platforms, the integration of AI for predictive safety models, and case studies demonstrating measurable reductions in accidents and compensation costs. However, the review also reveals fragmented ethical analyses, with privacy, surveillance, consent, and data governance frequently cited as barriers to adoption. Regional disparities and sector-specific challenges persist, underscoring the need for interdisciplinary frameworks that balance technical performance with worker autonomy, fairness, and regulatory compliance. The study concludes by identifying gaps in longitudinal evidence, cross-sectoral comparison, and global coverage, and calls for future research to integrate engineering, ethics, data science, and policy to ensure that Safety 4.0 advances both productivity and worker well-being.

Kaynakça

  • [1] I. Awolusi, E. Marks, and M. Hallowell, “Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices,” Autom. Constr., vol. 85, pp. 96–106, Jan. 2018, doi: 10.1016/j.autcon.2017.10.018.
  • [2] A. Briceño-Ruiz, W. O. Lopez, J. Riofrío-Vera, S. Paredes-Medina, L. Mejía-Ibarra, and J. E. Naranjo, “Machine learning algorithm selection for predictive maintenance in the oil industry,” in Proc. Int. Conf. Comput. Sci., Electron. Ind. Eng. (CSEI 2023), Ambato, Ecuador, Nov. 6–10, 2023, pp. 91–109. Cham, Switzerland: Springer, 2024.
  • [3] M. Buzinkay, “Miner technology: Wearables,” Identec Solutions, Aug. 18, 2022. [Online]. Available: https://www.identecsolutions.com/news/miner-technology-wearables#:~:text=In%20recent%20years%2C%20wearable%20devices,use%20in%20the%20mining%20industry
  • [4] E. A. Caburao, “Embracing safety 4.0: The future of safety management,” SafetyCulture, May 9, 2025. [Online]. Available: https://safetyculture.com/topics/safety-innovation/safety-4-0/
  • [5] B. Choi, S. Hwang, and S. Lee, “What drives construction workers’ acceptance of wearable technologies in the workplace? Indoor localization and wearable health devices for occupational safety and health,” Autom. Constr., vol. 84, pp. 31–41, Dec. 2017, doi: 10.1016/j.autcon.2017.08.005.
  • [6] M. Çalık and M. Sözbilir, “Parameters of content analysis,” Educ. Sci., vol. 39, no. 174, pp. 33–45, 2014.
  • [7] M. El Bouchikhi, S. Weerts, and C. Clavien, “Behind the good of digital tools for occupational safety and health: A scoping review of ethical issues surrounding the use of the internet of things,” Front. Public Health, vol. 12, Art. no. 1468646, Jan. 2024, doi: 10.3389/fpubh.2024.1468646.
  • [8] M. El-Helaly, “Artificial intelligence and occupational health and safety, benefits and drawbacks,” Med. Lavoro, vol. 115, no. 2, e2024014, Feb. 2024, doi: 10.23749/mdl.v115i2.15835.
  • [9] J. Fiegler-Rudol, K. Lau, A. Mroczek, and J. Kasperczyk, “Exploring human-AI dynamics in enhancing workplace health and safety: A narrative review,” Int. J. Environ. Res. Public Health, vol. 22, no. 2, Art. no. 199, Jan. 2025, doi: 10.3390/ijerph22020199.
  • [10] E. Fisher, M. A. Flynn, P. Pratap, and J. A. Vietas, “Occupational safety and health equity impacts of artificial intelligence: A scoping review,” Int. J. Environ. Res. Public Health, vol. 20, no. 13, Art. no. 6221, Jul. 2023, doi: 10.3390/ijerph20136221.
  • [11] M. Gusenbauer, “Search where you will find most: Comparing the disciplinary coverage of 56 bibliographic databases,” Scientometrics, vol. 127, pp. 2683–2745, Oct. 2022, doi: 10.1007/s11192-022-04289-7.
  • [12] M. Hennessy, A. Bleakley, and M. E. Ellithorpe, “Evaluating and tracking qualitative content coder performance using item response theory,” Qual. Quant., vol. 57, no. 2, pp. 1231–1245, Feb. 2023, doi: 10.1007/s11135-022-01397-7.
  • [13] M. Jarota, “Artificial intelligence in the work process: A reflection on the proposed European Union regulations on artificial intelligence from an occupational health and safety perspective,” Comput. Law Secur. Rev., vol. 49, Art. no. 105825, Jan. 2023, doi: 10.1016/j.clsr.2023.105825.
  • [14] T. Jowsey, C. Deng, and J. Weller, “General-purpose thematic analysis: A useful qualitative method for anaesthesia research,” BJA Educ., vol. 21, no. 12, pp. 472–478, Dec. 2021, doi: 10.1016/j.bjae.2021.07.006.
  • [15] A. J. Kleinheksel, N. Rockich-Winston, H. Tawfik, and T. R. Wyatt, “Demystifying content analysis,” Am. J. Pharm. Educ., vol. 84, no. 1, Art. no. 7113, Jan. 2020, doi: 10.5688/ajpe7113.
  • [16] S. Ling, Y. Yuan, D. Yan, Y. Leng, Y. Rong, and G. Q. Huang, “RHYTHMS: Real-time data-driven human-machine synchronization for proactive ergonomic risk mitigation in the context of Industry 4.0 and beyond,” Robot. Comput.-Integr. Manuf., vol. 87, Art. no. 102709, Jan. 2024, doi: 10.1016/j.rcim.2023.102709.
  • [17] MākuSafe, “FleetPride case study: Wearable tech drives record safety performance,” Case study, 2024. [Online]. Available: https://makusafe.com/downloads/
  • [18] E. Mayo-Wilson, T. Li, N. Fusco, K. Dickersin, and MUDS investigators, “Practical guidance for using multiple data sources in systematic reviews and meta-analyses (with examples from the MUDS study),” Res. Synth. Methods, vol. 9, no. 1, pp. 2–12, Jan. 2018, doi: 10.1002/jrsm.1277.
  • [19] Minew, “5 IoT solutions for industrial safety,” IoT For All, Dec. 3, 2024. [Online]. Available: https://www.iotforall.com/5-iot-solutions-for-industrial-safety
  • [20] L. Montesinos, R. Castaldo, and L. Pecchia, “Wearable inertial sensors for fall risk assessment and prediction in older adults: A systematic review and meta-analysis,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 26, no. 3, pp. 573–582, Mar. 2018, doi: 10.1109/TNSRE.2017.2771383.
  • [21] A. Morley, G. DeBord, and M. D. Hoover, “Wearable sensors: An ethical framework for decision-making,” NIOSH Sci. Blog, Jan. 20, 2017. [Online]. Available: https://blogs.cdc.gov/niosh-science-blog/2017/01/20/wearable-sensors-ethics/
  • [22] J. E. Naranjo, C. A. Mora, D. F. Bustamante Villagómez, M. G. Mancheno Falconi, and M. V. Garcia, “Wearable sensors in industrial ergonomics: Enhancing safety and productivity in Industry 4.0,” Sensors (Basel), vol. 25, no. 5, Art. no. 1526, Mar. 2025, doi: 10.3390/s25051526.
  • [23] N. Nath, R. Akhavian, and A. Behzadan, “Ergonomic analysis of construction worker’s body postures using wearable mobile sensors,” Appl. Ergon., vol. 62, pp. 107–117, Jul. 2017, doi: 10.1016/j.apergo.2017.02.015.
  • [24] NIOSH Sci. Blog, “Wearable exoskeletons to reduce physical load at work,” Centers for Disease Control and Prevention, Mar. 4, 2016. [Online]. Available: https://blogs.cdc.gov/niosh-science-blog/2016/03/04/exoskeletons/
  • [25] NIOSH Sci. Blog, “Wearable sensors: An ethical framework for decision making,” Centers for Disease Control and Prevention, Jan. 20, 2017. [Online]. Available: https://blogs.cdc.gov/niosh-science-blog/2017/01/20/wearable-sensors-ethics/
  • [26] A. Nioata, A. Țăpirdea, O. R. Chivu, A. Feier, I. C. Enache, M. Gheorghe, and C. Borda, “Workplace safety in Industry 4.0 and beyond: A case study on risk reduction through smart manufacturing systems in the automotive sector,” Safety, vol. 11, no. 2, Art. no. 50, Feb. 2025, doi: 10.3390/safety11020050.
  • [27] Optalert, “Optalert Eagle Industrial,” Optalert, May 27, 2021. [Online]. Available: https://www.optalert.com/explore-products/eagle-industrial
  • [28] M. J. Page et al., “PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews,” BMJ, vol. 372, Art. no. n160, Mar. 2021, doi: 10.1136/bmj.n160.
  • [29] J. Pavelko, “Integrating technology in workplace safety: The role of AI and IoT,” Occup. Health Saf., Jul. 17, 2024. [Online]. Available: https://ohsonline.com/articles/2024/07/17/integrating-technology-in-workplace- safety-the-role-of-ai-and-iot.aspx
  • [30] G. Peterson, “Enhancing workplace safety through wearable technology: A modern approach for workers’ compensation professionals,” WCI360, 2025. [Online]. Available: https://wci360.com/enhancing-workplace-safety-through-wearable-technology/
  • [31] PwC, “How wearable technology could promote trust and wellness at work,” Dec. 2, 2021. [Online]. Available: https://www.pwc.com/gx/en/services/workforce/leveraging-wearable-technology.html
  • [32] J. Saldaña, The Coding Manual for Qualitative Researchers, 1st ed. London, U.K.: SAGE Publ., 2009.
  • [33] M. Salehijam, “The value of systematic content analysis in legal research,” Tilburg Law Rev., vol. 23, no. 1–2, pp. 55–70, 2018.
  • [34] M. C. Schall, Jr., R. F. Sesek, and L. A. Cavuoto, “Barriers to the adoption of wearable sensors in the workplace: A survey of occupational safety and health professionals,” Hum. Factors, vol. 60, no. 3, pp. 351–362, May 2018, doi: 10.1177/0018720817753907.
  • [35] I. A. Shah and S. Mishra, “Artificial intelligence in advancing occupational health and safety: An encapsulation of developments,” J. Occup. Health, vol. 66, pp. 12–29, Jan. 2024, doi: 10.1093/joccuh/uiad017.
  • [36] E. Svertoka, S. Saafi, A. Rusu-Casandra, R. Burget, I. Marghescu, J. Hosek, and A. Ometov, “Wearables for industrial work safety: A survey,” Sensors (Basel), vol. 21, no. 11, Art. no. 3844, Jun. 2021, doi: 10.3390/s21113844.
  • [37] U.S. Government Accountability Office, Wearable Technologies in the Workplace (GAO-24-107303), Washington, DC, USA, Mar. 2024. [Online]. Available: https://www.gao.gov/assets/d24107303.pdf
  • [38] D. Wang, J. Chen, D. Zhao, F. Dai, C. Zheng, and X. Wu, “Monitoring workers’ attention and vigilance in construction activities through a wireless and wearable electroencephalography system,” Autom. Constr., vol. 82, pp. 122–137, Sep. 2017, doi: 10.1016/j.autcon.2017.03.001.
  • [39] Z. Wang, J. P. Brito, A. Tsapas, M. L. Griebeler, F. Alahdab, and M. H. Murad, “Systematic reviews with language restrictions and no author contact have lower overall credibility: A methodology study,” Clin. Epidemiol., vol. 7, pp. 243–247, Apr. 2015, doi: 10.2147/CLEP.S78879.
  • [40] D. J. Warrington, E. J. Shortis, and P. J. Whittaker, “Are wearable devices effective for preventing and detecting falls: An umbrella review (a review of systematic reviews),” BMC Public Health, vol. 21, no. 1, Art. no. 2091, Dec. 2021, doi: 10.1186/s12889-021-12169-7.
  • [41] A. Puranik, M. Kanthi, and A. V. Nayak, “Wearable device for yogic breathing with real-time heart rate and posture monitoring,” J. Med. Signals Sens., vol. 11, no. 4, pp. 253–261, 2021.
  • [42] M. C. Schall Jr, H. Chen, and L. Cavuoto, “Wearable inertial sensors for objective kinematic assessments: A brief overview,” J. Occup. Environ. Hyg., vol. 19, no. 9, pp. 501–508, 2022.

İş Sağlığı ve Güvenliğinde Gerçek Zamanlı Tehlike İzleme için Giyilebilir Teknolojiler ve Nesnelerin İnterneti: Sistematik İçerik Analizi

Yıl 2025, Cilt: 1 Sayı: 2, 45 - 54, 29.09.2025

Öz

Endüstri 4.0 kapsamında işlemlerin hızlı dijitalleşmesi, iş sağlığı ve güvenliği (OHS) için hem fırsatlar hem de zorluklar getirmiştir. Giyilebilir teknolojiler ve Nesnelerin İnterneti (IoT) teknolojileri, gerçek zamanlı izleme, tahmine dayalı analitik ve proaktif risk yönetimi sağlamak için giderek daha fazla kullanılmaktadır, ve “Güvenlik 4.0” kavramını geliştirmektedir. Bu çalışma, 2010 ile Haziran 2025 arasında yayınlanan uluslararası literatürü sistematik bir içerik analizi ile inceleyerek, cihaz türleri, endüstriyel sektörler, öngörücü güvenlik entegrasyonu ve etik kaygılar arasındaki kanıtları sentezlemektedir. Bulgular, akıllı kasklar, bileklikler ve bağlantılı yelekler gibi izleme cihazlarının literatürde hakim olduğunu, inşaat, madencilik ve imalat gibi yüksek riskli sektörlerde güçlü bir şekilde benimsenirken, sağlık ve lojistik alanlarının da bu cihazları benimseme konusunda yeni gelişen alanlar olduğunu göstermektedir. Tematik analiz, kişisel koruyucu ekipmanların aktif algılama platformlarına dönüşümünü, öngörücü güvenlik modelleri için yapay zekanın entegrasyonunu ve kazalarda ve tazminat maliyetlerinde ölçülebilir azalmalar gösteren vaka çalışmalarını vurgulamaktadır. Ancak inceleme, gizlilik, gözetim, rıza ve veri yönetimi gibi konuların benimsenmenin önündeki engeller olarak sıklıkla belirtildiği, parçalı etik analizleri de ortaya koymaktadır. Bölgesel eşitsizlikler ve sektöre özgü zorluklar devam etmekte olup, teknik performans ile işçi özerkliği, adalet ve mevzuata uygunluk arasında denge kuran disiplinler arası çerçevelere olan ihtiyacı vurgulamaktadır. Çalışma, uzun vadeli kanıtlar, sektörler arası karşılaştırma ve küresel kapsama alanındaki boşlukları belirleyerek son bulmakta ve Safety 4.0'ın hem üretkenliği hem de işçi refahını artırmasını sağlamak için mühendislik, etik, veri bilimi ve politikayı entegre edecek gelecekteki araştırmalara çağrı yapmaktadır.

Kaynakça

  • [1] I. Awolusi, E. Marks, and M. Hallowell, “Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices,” Autom. Constr., vol. 85, pp. 96–106, Jan. 2018, doi: 10.1016/j.autcon.2017.10.018.
  • [2] A. Briceño-Ruiz, W. O. Lopez, J. Riofrío-Vera, S. Paredes-Medina, L. Mejía-Ibarra, and J. E. Naranjo, “Machine learning algorithm selection for predictive maintenance in the oil industry,” in Proc. Int. Conf. Comput. Sci., Electron. Ind. Eng. (CSEI 2023), Ambato, Ecuador, Nov. 6–10, 2023, pp. 91–109. Cham, Switzerland: Springer, 2024.
  • [3] M. Buzinkay, “Miner technology: Wearables,” Identec Solutions, Aug. 18, 2022. [Online]. Available: https://www.identecsolutions.com/news/miner-technology-wearables#:~:text=In%20recent%20years%2C%20wearable%20devices,use%20in%20the%20mining%20industry
  • [4] E. A. Caburao, “Embracing safety 4.0: The future of safety management,” SafetyCulture, May 9, 2025. [Online]. Available: https://safetyculture.com/topics/safety-innovation/safety-4-0/
  • [5] B. Choi, S. Hwang, and S. Lee, “What drives construction workers’ acceptance of wearable technologies in the workplace? Indoor localization and wearable health devices for occupational safety and health,” Autom. Constr., vol. 84, pp. 31–41, Dec. 2017, doi: 10.1016/j.autcon.2017.08.005.
  • [6] M. Çalık and M. Sözbilir, “Parameters of content analysis,” Educ. Sci., vol. 39, no. 174, pp. 33–45, 2014.
  • [7] M. El Bouchikhi, S. Weerts, and C. Clavien, “Behind the good of digital tools for occupational safety and health: A scoping review of ethical issues surrounding the use of the internet of things,” Front. Public Health, vol. 12, Art. no. 1468646, Jan. 2024, doi: 10.3389/fpubh.2024.1468646.
  • [8] M. El-Helaly, “Artificial intelligence and occupational health and safety, benefits and drawbacks,” Med. Lavoro, vol. 115, no. 2, e2024014, Feb. 2024, doi: 10.23749/mdl.v115i2.15835.
  • [9] J. Fiegler-Rudol, K. Lau, A. Mroczek, and J. Kasperczyk, “Exploring human-AI dynamics in enhancing workplace health and safety: A narrative review,” Int. J. Environ. Res. Public Health, vol. 22, no. 2, Art. no. 199, Jan. 2025, doi: 10.3390/ijerph22020199.
  • [10] E. Fisher, M. A. Flynn, P. Pratap, and J. A. Vietas, “Occupational safety and health equity impacts of artificial intelligence: A scoping review,” Int. J. Environ. Res. Public Health, vol. 20, no. 13, Art. no. 6221, Jul. 2023, doi: 10.3390/ijerph20136221.
  • [11] M. Gusenbauer, “Search where you will find most: Comparing the disciplinary coverage of 56 bibliographic databases,” Scientometrics, vol. 127, pp. 2683–2745, Oct. 2022, doi: 10.1007/s11192-022-04289-7.
  • [12] M. Hennessy, A. Bleakley, and M. E. Ellithorpe, “Evaluating and tracking qualitative content coder performance using item response theory,” Qual. Quant., vol. 57, no. 2, pp. 1231–1245, Feb. 2023, doi: 10.1007/s11135-022-01397-7.
  • [13] M. Jarota, “Artificial intelligence in the work process: A reflection on the proposed European Union regulations on artificial intelligence from an occupational health and safety perspective,” Comput. Law Secur. Rev., vol. 49, Art. no. 105825, Jan. 2023, doi: 10.1016/j.clsr.2023.105825.
  • [14] T. Jowsey, C. Deng, and J. Weller, “General-purpose thematic analysis: A useful qualitative method for anaesthesia research,” BJA Educ., vol. 21, no. 12, pp. 472–478, Dec. 2021, doi: 10.1016/j.bjae.2021.07.006.
  • [15] A. J. Kleinheksel, N. Rockich-Winston, H. Tawfik, and T. R. Wyatt, “Demystifying content analysis,” Am. J. Pharm. Educ., vol. 84, no. 1, Art. no. 7113, Jan. 2020, doi: 10.5688/ajpe7113.
  • [16] S. Ling, Y. Yuan, D. Yan, Y. Leng, Y. Rong, and G. Q. Huang, “RHYTHMS: Real-time data-driven human-machine synchronization for proactive ergonomic risk mitigation in the context of Industry 4.0 and beyond,” Robot. Comput.-Integr. Manuf., vol. 87, Art. no. 102709, Jan. 2024, doi: 10.1016/j.rcim.2023.102709.
  • [17] MākuSafe, “FleetPride case study: Wearable tech drives record safety performance,” Case study, 2024. [Online]. Available: https://makusafe.com/downloads/
  • [18] E. Mayo-Wilson, T. Li, N. Fusco, K. Dickersin, and MUDS investigators, “Practical guidance for using multiple data sources in systematic reviews and meta-analyses (with examples from the MUDS study),” Res. Synth. Methods, vol. 9, no. 1, pp. 2–12, Jan. 2018, doi: 10.1002/jrsm.1277.
  • [19] Minew, “5 IoT solutions for industrial safety,” IoT For All, Dec. 3, 2024. [Online]. Available: https://www.iotforall.com/5-iot-solutions-for-industrial-safety
  • [20] L. Montesinos, R. Castaldo, and L. Pecchia, “Wearable inertial sensors for fall risk assessment and prediction in older adults: A systematic review and meta-analysis,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 26, no. 3, pp. 573–582, Mar. 2018, doi: 10.1109/TNSRE.2017.2771383.
  • [21] A. Morley, G. DeBord, and M. D. Hoover, “Wearable sensors: An ethical framework for decision-making,” NIOSH Sci. Blog, Jan. 20, 2017. [Online]. Available: https://blogs.cdc.gov/niosh-science-blog/2017/01/20/wearable-sensors-ethics/
  • [22] J. E. Naranjo, C. A. Mora, D. F. Bustamante Villagómez, M. G. Mancheno Falconi, and M. V. Garcia, “Wearable sensors in industrial ergonomics: Enhancing safety and productivity in Industry 4.0,” Sensors (Basel), vol. 25, no. 5, Art. no. 1526, Mar. 2025, doi: 10.3390/s25051526.
  • [23] N. Nath, R. Akhavian, and A. Behzadan, “Ergonomic analysis of construction worker’s body postures using wearable mobile sensors,” Appl. Ergon., vol. 62, pp. 107–117, Jul. 2017, doi: 10.1016/j.apergo.2017.02.015.
  • [24] NIOSH Sci. Blog, “Wearable exoskeletons to reduce physical load at work,” Centers for Disease Control and Prevention, Mar. 4, 2016. [Online]. Available: https://blogs.cdc.gov/niosh-science-blog/2016/03/04/exoskeletons/
  • [25] NIOSH Sci. Blog, “Wearable sensors: An ethical framework for decision making,” Centers for Disease Control and Prevention, Jan. 20, 2017. [Online]. Available: https://blogs.cdc.gov/niosh-science-blog/2017/01/20/wearable-sensors-ethics/
  • [26] A. Nioata, A. Țăpirdea, O. R. Chivu, A. Feier, I. C. Enache, M. Gheorghe, and C. Borda, “Workplace safety in Industry 4.0 and beyond: A case study on risk reduction through smart manufacturing systems in the automotive sector,” Safety, vol. 11, no. 2, Art. no. 50, Feb. 2025, doi: 10.3390/safety11020050.
  • [27] Optalert, “Optalert Eagle Industrial,” Optalert, May 27, 2021. [Online]. Available: https://www.optalert.com/explore-products/eagle-industrial
  • [28] M. J. Page et al., “PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews,” BMJ, vol. 372, Art. no. n160, Mar. 2021, doi: 10.1136/bmj.n160.
  • [29] J. Pavelko, “Integrating technology in workplace safety: The role of AI and IoT,” Occup. Health Saf., Jul. 17, 2024. [Online]. Available: https://ohsonline.com/articles/2024/07/17/integrating-technology-in-workplace- safety-the-role-of-ai-and-iot.aspx
  • [30] G. Peterson, “Enhancing workplace safety through wearable technology: A modern approach for workers’ compensation professionals,” WCI360, 2025. [Online]. Available: https://wci360.com/enhancing-workplace-safety-through-wearable-technology/
  • [31] PwC, “How wearable technology could promote trust and wellness at work,” Dec. 2, 2021. [Online]. Available: https://www.pwc.com/gx/en/services/workforce/leveraging-wearable-technology.html
  • [32] J. Saldaña, The Coding Manual for Qualitative Researchers, 1st ed. London, U.K.: SAGE Publ., 2009.
  • [33] M. Salehijam, “The value of systematic content analysis in legal research,” Tilburg Law Rev., vol. 23, no. 1–2, pp. 55–70, 2018.
  • [34] M. C. Schall, Jr., R. F. Sesek, and L. A. Cavuoto, “Barriers to the adoption of wearable sensors in the workplace: A survey of occupational safety and health professionals,” Hum. Factors, vol. 60, no. 3, pp. 351–362, May 2018, doi: 10.1177/0018720817753907.
  • [35] I. A. Shah and S. Mishra, “Artificial intelligence in advancing occupational health and safety: An encapsulation of developments,” J. Occup. Health, vol. 66, pp. 12–29, Jan. 2024, doi: 10.1093/joccuh/uiad017.
  • [36] E. Svertoka, S. Saafi, A. Rusu-Casandra, R. Burget, I. Marghescu, J. Hosek, and A. Ometov, “Wearables for industrial work safety: A survey,” Sensors (Basel), vol. 21, no. 11, Art. no. 3844, Jun. 2021, doi: 10.3390/s21113844.
  • [37] U.S. Government Accountability Office, Wearable Technologies in the Workplace (GAO-24-107303), Washington, DC, USA, Mar. 2024. [Online]. Available: https://www.gao.gov/assets/d24107303.pdf
  • [38] D. Wang, J. Chen, D. Zhao, F. Dai, C. Zheng, and X. Wu, “Monitoring workers’ attention and vigilance in construction activities through a wireless and wearable electroencephalography system,” Autom. Constr., vol. 82, pp. 122–137, Sep. 2017, doi: 10.1016/j.autcon.2017.03.001.
  • [39] Z. Wang, J. P. Brito, A. Tsapas, M. L. Griebeler, F. Alahdab, and M. H. Murad, “Systematic reviews with language restrictions and no author contact have lower overall credibility: A methodology study,” Clin. Epidemiol., vol. 7, pp. 243–247, Apr. 2015, doi: 10.2147/CLEP.S78879.
  • [40] D. J. Warrington, E. J. Shortis, and P. J. Whittaker, “Are wearable devices effective for preventing and detecting falls: An umbrella review (a review of systematic reviews),” BMC Public Health, vol. 21, no. 1, Art. no. 2091, Dec. 2021, doi: 10.1186/s12889-021-12169-7.
  • [41] A. Puranik, M. Kanthi, and A. V. Nayak, “Wearable device for yogic breathing with real-time heart rate and posture monitoring,” J. Med. Signals Sens., vol. 11, no. 4, pp. 253–261, 2021.
  • [42] M. C. Schall Jr, H. Chen, and L. Cavuoto, “Wearable inertial sensors for objective kinematic assessments: A brief overview,” J. Occup. Environ. Hyg., vol. 19, no. 9, pp. 501–508, 2022.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka (Diğer)
Bölüm Research Article
Yazarlar

Ahmet Yurtcu 0000-0002-2234-1928

Yayımlanma Tarihi 29 Eylül 2025
Gönderilme Tarihi 20 Ağustos 2025
Kabul Tarihi 9 Eylül 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 1 Sayı: 2

Kaynak Göster

APA Yurtcu, A. (2025). Wearable Technologies and Internet of Things for Real-Time Hazard Monitoring in Occupational Health and Safety: A Systematic Content Analysis. Innovative Approaches to Engineering Problems, 1(2), 45-54.