Augmented Reality and AI: An Experimental Study of Worker Productivity Enhancement

Open Access

Issue		BIO Web Conf. Volume 86, 2024 International Conference on Recent Trends in Biomedical Sciences (RTBS-2023)


Article Number		01095
Number of page(s)		8
DOI		https://doi.org/10.1051/bioconf/20248601095
Published online		12 January 2024

Y. K. Dwivedi et al., “‘So what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy,” Int J Inf Manage, vol. 71, Aug. 2023, doi: 10.1016/j.ijinfomgt.2023.102642. [CrossRef] [Google Scholar]
K. E. Medeiros, R. L. Marrone, S. Joksimovic, D. H. Cropley, and G. Siemens, “Promises and realities of artificial creativity,” Handbook of Organizational Creativity: Leadership, Interventions, and Macro Level Issues, Second Edition, pp. 275–289, Jan. 2023, doi: 10.1016/B978-0-323-91841-1.00010-5. [Google Scholar]
C. van Noordt and G. Misuraca, “Artificial intelligence for the public sector: results of landscaping the use of AI in government across the European Union,” Gov Inf Q, vol. 39, no. 3, Jul. 2022, doi: 10.1016/j.giq.2022.101714. [CrossRef] [Google Scholar]
M. Ciccarelli, A. Brunzini, A. Papetti, and M. Germani, “Interface and interaction design principles for Mixed Reality applications: The case of operator training in wire harness activities,” Procedia Comput Sci, vol. 204, pp. 540–547, 2022, doi: 10.1016/j.procs.2022.08.066. [CrossRef] [Google Scholar]
L. Nazareno and D. S. Schiff, “The impact of automation and artificial intelligence on worker well-being,” Technol Soc, vol. 67, Nov. 2021, doi: 10.1016/j.techsoc.2021.101679. [CrossRef] [Google Scholar]
B. G. Mark, E. Rauch, and D. T. Matt, “Worker assistance systems in manufacturing: A review of the state of the art and future directions,” J Manuf Syst, vol. 59, pp. 228–250, Apr. 2021, doi: 10.1016/j.jmsy.2021.02.017. [CrossRef] [Google Scholar]
“Augmented Reality and AI: An Experimental Study of Worker Productivity Enhancement - Search | ScienceDirect.com.” Accessed: Nov. 02, 2023. [Online]. Available: https://www.sciencedirect.com/search?qs=Augmented%20Reality%20and%20AI%3A%20An%20Experimental%20Study%20of%20Worker%20Productivity%20Enhancement [Google Scholar]
M. Zhu, C. Liang, A. C. L. Yeung, and H. Zhou, “The impact of intelligent manufacturing on labor productivity: An empirical analysis of Chinese listed manufacturing companies,” Int J Prod Econ, vol. 267, Jan. 2024, doi: 10.1016/j.ijpe.2023.109070. [Google Scholar]
D. K. Baroroh and C. H. Chu, “Human-centric production system simulation in mixed reality: An exemplary case of logistic facility design,” J Manuf Syst, vol. 65, pp. 146–157, Oct. 2022, doi: 10.1016/j.jmsy.2022.09.005. [CrossRef] [Google Scholar]
M. Attaran and B. G. Celik, “Digital Twin: Benefits, use cases, challenges, and opportunities,” Decision Analytics Journal, vol. 6, Mar. 2023, doi: 10.1016/j.dajour.2023.100165. [CrossRef] [Google Scholar]
W. Li, Y. Wang, Z. Ye, Y. A. Liu, and L. Wang, “Development of a mixed reality assisted escape system for underground mine- based on the mine water-inrush accident background,” Tunnelling and Underground Space Technology, vol. 143, p. 105471, Jan. 2024, doi: 10.1016/J.TUST.2023.105471. [CrossRef] [Google Scholar]
Y. Yang, S. Deb, M. He, and M. H. Kobir, “The use of virtual reality in manufacturing education: State-of-the- art and future directions,” Manuf Lett, vol. 35, pp. 1214–1221, Aug. 2023, doi: 10.1016/J.MFGLET.2023.07.023. [CrossRef] [Google Scholar]
S. Vishnoi and R. K. Goel, “Climate smart agriculture for sustainable productivity and healthy landscapes,” Environ Sci Policy, vol. 151, Jan. 2024, doi: 10.1016/j.envsci.2023.103600. [CrossRef] [Google Scholar]
D. Marikyan, S. Papagiannidis, O. F. Rana, R. Ranjan, and G. Morgan, “‘Alexa, let’s talk about my productivity’: The impact of digital assistants on work productivity,” J Bus Res, vol. 142, pp. 572–584, Mar. 2022, doi: 10.1016/j.jbusres.2022.01.015. [CrossRef] [Google Scholar]
J. Chen, Y. Fu, W. Lu, and Y. Pan, “Augmented reality-enabled human-robot collaboration to balance construction waste sorting efficiency and occupational safety and health,” J Environ Manage, vol. 348, Dec. 2023, doi: 10.1016/j.jenvman.2023.119341. [Google Scholar]
F. Nucci, C. Puccioni, and O. Ricchi, “Digital technologies and productivity: A firm-level investigation,” Econ Model, p. 106524, Nov. 2023, doi: 10.1016/j.econmod.2023.106524. [Google Scholar]
G. Plakas, S. T. Ponis, K. Agalianos, E. Aretoulaki, and S. P. Gayalis, “Augmented reality in manufacturing and logistics: Lessons learnt from a real-life industrial application,” Procedia Manuf, vol. 51, pp. 1629–1635, 2020, doi: 10.1016/j.promfg.2020.10.227. [CrossRef] [Google Scholar]
D. K. Baroroh, C. H. Chu, and L. Wang, “Systematic literature review on augmented reality in smart manufacturing: Collaboration between human and computational intelligence,” J Manuf Syst, vol. 61, pp. 696–711, Oct. 2021, doi: 10.1016/j.jmsy.2020.10.017. [CrossRef] [Google Scholar]
S. Tuli et al., “AI augmented Edge and Fog computing: Trends and challenges,” Journal of Network and Computer Applications, vol. 216, Jul. 2023, doi: 10.1016/j.jnca.2023.103648. [CrossRef] [Google Scholar]
R. Maio et al., “Pervasive Augmented Reality to support real-time data monitoring in industrial scenarios: Shop floor visualization evaluation and user study,” Comput Graph, Oct. 2023, doi: 10.1016/J.CAG.2023.10.025. [Google Scholar]
M. Moghaddam, N. C. Wilson, A. S. Modestino, K. Jona, and S. C. Marsella, “Exploring augmented reality for worker assistance versus training,” Advanced Engineering Informatics, vol. 50, Oct. 2021, doi: 10.1016/j.aei.2021.101410. [CrossRef] [Google Scholar]
S. Vernim, H. Bauer, E. Rauch, M. T. Ziegler, and S. Umbrello, “A value sensitive design approach for designing AI-based worker assistance systems in manufacturing,” Procedia Comput Sci, vol. 200, pp. 505–516, 2022, doi: 10.1016/j.procs.2022.01.248. [CrossRef] [Google Scholar]
C. H. Chu and Y. L. Liu, “Augmented reality user interface design and experimental evaluation for human- robot collaborative assembly,” J Manuf Syst, vol. 68, pp. 313–324, Jun. 2023, doi: 10.1016/j.jmsy.2023.04.007. [CrossRef] [Google Scholar]
Z. H. Lai, W. Tao, M. C. Leu, and Z. Yin, “Smart augmented reality instructional system for mechanical assembly towards worker-centered intelligent manufacturing,” J Manuf Syst, vol. 55, pp. 69–81, Apr. 2020, doi: 10.1016/j.jmsy.2020.02.010. [CrossRef] [Google Scholar]
A. Zirar, S. I. Ali, and N. Islam, “Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda,” Technovation, vol. 124, Jun. 2023, doi: 10.1016/j.technovation.2023.102747. [CrossRef] [Google Scholar]
Md. Z. ul Haq, H. Sood, and R. Kumar, “Effect of using plastic waste on mechanical properties of fly ash based geopolymer concrete,” Mater Today Proc, 2022. [Google Scholar]
H. Sood, R. Kumar, P. C. Jena, and S. K. Joshi, “Eco-friendly approach to construction: Incorporating waste plastic in geopolymer concrete,” Mater Today Proc, 2023. [Google Scholar]
M. Z. ul Haq et al., “Sustainable Infrastructure Solutions: Advancing Geopolymer Bricks via Eco- Polymerization of Plastic Waste,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01203. [Google Scholar]
M. Z. ul Haq et al., “Geopolymerization of Plastic Waste for Sustainable Construction: Unveiling Novel Opportunities in Building Materials,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01204. [Google Scholar]
M. Z. ul Haq et al., “Eco-Friendly Building Material Innovation: Geopolymer Bricks from Repurposed Plastic Waste,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01201. [Google Scholar]
M. Z. ul Haq et al., “Circular Economy Enabler: Enhancing High-Performance Bricks through Geopolymerization of Plastic Waste,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01202. [Google Scholar]
S. Bali et al., “A framework to assess the smartphone buying behaviour using DEMATEL method in the Indian context,” Ain Shams Engineering Journal, 2023, doi: 10.1016/J.ASEJ.2023.102129. [Google Scholar]
P. Singh et al., “Comparative Study of Concrete Cylinders Confined Using Natural and Artificial Fibre Reinforced Polymers,” Lecture Notes in Mechanical Engineering, pp. 79–91, 2023, doi: 10.1007/978-981-19-4147-4_8. [Google Scholar]
T. K. Miroshnikova, I. A. Kirichenko, and S. Dixit, “Analytical aspects of anti-crisis measures of public administration,” UPRAVLENIE / MANAGEMENT (Russia), vol. 10, no. 4, pp. 5–13, Jan. 2023, doi: 10.26425/2309-3633-2022-10-4-5-13. [CrossRef] [Google Scholar]
S. Dixit et al., “Numerical simulation of sand–water slurry flow through pipe bend using CFD,” International Journal on Interactive Design and Manufacturing, Oct. 2022, doi: 10.1007/S12008-022-01004-X. [Google Scholar]
R. Gera et al., “A systematic literature review of supply chain management practices and performance,” Mater Today Proc, vol. 69, pp. 624–632, Jan. 2022, doi: 10.1016/J.MATPR.2022.10.203. [CrossRef] [Google Scholar]
V. S. Rana et al., “Correction: Assortment of latent heat storage materials using multi criterion decision making techniques in Scheffler solar reflector (International Journal on Interactive Design and Manufacturing (IJIDeM), (2023), 10.1007/s12008-023-01456-9),” International Journal on Interactive Design and Manufacturing, 2023, doi: 10.1007/S12008-023-01518-Y. [Google Scholar]
Vinnik, D.A., Zhivulin, V.E., Sherstyuk, D.P., Starikov, A.Y., Zezyulina, P.A., Gudkova, S.A., Zherebtsov, D.A., Rozanov, K.N., Trukhanov, S.V., Astapovich, K.A. and Sombra, A.S.B., 2021. Ni substitution effect on the structure, magnetization, resistivity and permeability of zinc ferrites. Journal of Materials Chemistry C, 9(16), pp.5425-5436. [CrossRef] [Google Scholar]
Khamparia, A., Singh, P.K., Rani, P., Samanta, D., Khanna, A. and Bhushan, B., 2021. An internet of health things‐driven deep learning framework for detection and classification of skin cancer using transfer learning. Transactions on Emerging Telecommunications Technologies, 32(7), p.e3963. [CrossRef] [Google Scholar]
Prakash, C., Singh, S., Pabla, B.S. and Uddin, M.S., 2018. Synthesis, characterization, corrosion and bioactivity investigation of nano-HA coating deposited on biodegradable Mg-Zn-Mn alloy. Surface and Coatings Technology, 346, pp.9-18. [CrossRef] [Google Scholar]
Masud, M., Gaba, G.S., Choudhary, K., Hossain, M.S., Alhamid, M.F. and Muhammad, G., 2021. Lightweight and anonymity-preserving user authentication scheme for IoT-based healthcare. IEEE Internet of Things Journal, 9(4), pp.2649-2656. [Google Scholar]
Uddin, M.S., Tewari, D., Sharma, G., Kabir, M.T., Barreto, G.E., Bin-Jumah, M.N., Perveen, A., Abdel-Daim, M.M. and Ashraf, G.M., 2020. Molecular Mechanisms of ER Stress and UPR in the Pathogenesis of Alzheimer’s Disease. Molecular Neurobiology, 57, pp.2902-2919. [CrossRef] [PubMed] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.