2609005264
  • Open Access
  • Review

Emerging Technologies in Human-Machine Interaction towards Industry 5.0 and Industrial Metaverse: Challenges and Opportunities

  • Dimitris Mourtzis *,   
  • Dimitris Tsakalos

Received: 24 Mar 2026 | Revised: 21 Sep 2026 | Accepted: 22 Sep 2026 | Published: 30 Sep 2026

Abstract

Since the first steps towards manufacturing system digitalization, the development of interfaces enabling interaction between humans and machines has progressed in parallel. With the appearance of novel technologies such as 3D-modeling or Artificial Intelligence, Human-Machine Interfaces are required to provide access to larger amounts and complexity of information, as well as more complex control capabilities. There are currently many different approaches to Human-machine Interaction, such as User Interfaces and Extended Reality. This research work summarizes the history of Human-Machine Interfaces, provides a timeline of their evolution based on the Industrial Revolutions, and showcases recent advancements and emerging technologies in the field. A review methodology considering the newest and most impactful works in the field is followed, and challenges and opportunities towards Industry 5.0 and the Industrial Metaverse are identified and discussed.

References 

  • 1.

    Mourtzis, D.; Angelopoulos, J.; Panopoulos, N. A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0. Energies 2022, 15, 6276. https://doi.org/10.3390/en15176276.

  • 2.

    Yang, J.; Liu, Y.; Morgan, P.L. Human–machine interaction towards Industry 5.0: Human-centric smart manufacturing. Digit. Eng. 2024, 2, 100013. https://doi.org/10.1016/j.dte.2024.100013.

  • 3.

    Yanytska, L. The rise of human-centric manufacturing in the industry 5.0 era. Int. J. Adv. Manuf. Technol. 2025, 139, 5067–5077. https://doi.org/10.1007/s00170-025-16192-5.

  • 4.

    Guo, J.; Leng, J.; Zhao, J.; et al. Industrial metaverse towards Industry 5.0: Connotation, architecture, enablers, and challenges. J. Manuf. Syst. 2024, 76, 25–42. https://doi.org/10.1016/j.jmsy.2024.07.007.

  • 5.

    Mourtzis, D.; Angelopoulos, J.; Panopoulos, N. The Future of the Human–Machine Interface (HMI) in Society 5.0. Future Internet 2023, 15, 162. https://doi.org/10.3390/fi15050162.

  • 6.

    Leng, J.; Sha, W.; Wang, B.; et al. Industry 5.0: Prospect and retrospect. J. Manuf. Syst. 2022, 65, 279–295. https://doi.org/10.1016/j.jmsy.2022.09.017.

  • 7.

    Ruiz-de-la-Torre, A.; Rio-Belver, R.M.; Guevara-Ramirez, W.; et al. Industry 5.0 and Human-Centered Approach. Bibliometric Review. In Lecture Notes on Data Engineering and Communications Technologies; Springer: Berlin/Heidelberg, Germany, 2023; Volume 160, pp. 402–408. https://doi.org/10.1007/978-3-031-27915-7_71.

  • 8.

    Tsakalos, D.; Krimpas, G.A.; Mourtzis, D. Industrial Metaverse challenges, opportunities, and the impact on sustainability. Digit. Twin 2026, 3, 2600190. https://doi.org/10.1080/27525783.2025.2600190.

  • 9.

    Orlando Fla. Top 10 Strategic Technology Trends for 2025. Gartner 2024. Available online: https://www.gartner.com/en/newsroom/press-releases/2024-10-21-gartner-identifies-the-top-10-strategic-technology-trends-for-2025?trk=public_post_comment-text (accessed on 11 February 2026).

  • 10.

    Krimpas, G.A.; Mourtzis, D.; Tsakalos, D. Metaverse: Industrial Applications—Challenges and opportunities. Procedia CIRP 2025, 136, 55–60. https://doi.org/10.1016/j.procir.2025.08.012.

  • 11.

    Krug, S. Digitale Transformation in Siemens Werken. 2024. Available online: https://www.siemens.com/en-us/company/global-locations/germany/erlangen-device-plant/ (accessed 28 September 2026)

  • 12.

    HMGMA—Hyundai Motor Group Metaplant America. 2026. Available online: https://www.hmgma.com/our-facility/ (accessed on 14 August 2026).

  • 13.

    Di Nardo, M.; Forino, D.; Murino, T. The evolution of man–machine interaction: The role of human in Industry 4.0 paradigm. Prod. Manuf. Res. 2020, 8, 20–34. https://doi.org/10.1080/21693277.2020.1737592.

  • 14.

    Jain, N.; Gupta, V.; Temperini, V.; et al. Human machine interactions: From past to future- a systematic literature review. J. Manag. Hist. 2024, 30, 263–302. https://doi.org/10.1108/jmh-12-2022-0085.

  • 15.

    Krupitzer, C.; Müller, S.; Lesch, V.; et al. A Survey on Human Machine Interaction in Industry 4.0. arXiv 2020, arXiv:2002.01025.

  • 16.

    Kumar, N.; Lee, S.C. Human-machine interface in smart factory: A systematic literature review. Technol. Forecast. Soc. Change 2022, 174, 121284. https://doi.org/10.1016/j.techfore.2021.121284.

  • 17.

    Onnasch, L.; Roesler, E. A Taxonomy to Structure and Analyze Human–Robot Interaction. Int. J. Soc. Robot. 2021, 13, 833–849. https://doi.org/10.1007/s12369-020-00666-5.

  • 18.

    Chen, R.; Cheung, C.F.; Zhang, Q.; et al. Ultra-Sensitive and Linear Flexible Pressure Sensors with Tri-Scale Graded Microstructures for Advanced Health Monitoring and Robotic Perception. Adv. Sci. 2026, 13, e16810. https://doi.org/10.1002/advs.202516810.

  • 19.

    Nnadi, S.N.; Ajadalu, I.; Rahmani, A.; et al. Development, Experimental, and Numerical Characterisation of Novel Flexible Strain Sensors for Soft Robotics Applications. Robotics 2024, 13, 103. https://doi.org/10.3390/robotics13070103.

  • 20.

    Li, X.; Liu, H.; Liang, Q.; et al. Design of a high-performance flexible pressure sensor based on AgNWs-MXene/MWCNTs composites and its multi-scenario application study. Sens. Actuators A Phys. 2026, 399, 117428. https://doi.org/10.1016/j.sna.2025.117428.

  • 21.

    Liang, Y.; Zhang, C.; Mi, X.; et al. Sequentially squeezed capacitive pressure sensor based on baguette-inspired porous ionic gel foams. Compos. Part A Appl. Sci. Manuf. 2025, 190, 108656. https://doi.org/10.1016/j.compositesa.2024.108656.

  • 22.

    Zhang, P.; He, W.; Wu, G.; et al. Hierarchically structured dual-mode sensor with directional moisture-wicking capability for weak physiological signal monitoring and long-range motion tracking. Chem. Eng. J. 2025, 520, 165585. https://doi.org/10.1016/j.cej.2025.165585.

  • 23.

    Xu, D.; Zhou, P.; Wu, J.; et al. Recent Progress in High-performance Flexible Pressure Sensors: Materials, Design, Fabrication, and Applications. Adv. Mater. Technol. 2025, 10, 2500192. https://doi.org/10.1002/admt.202500192.

  • 24.

    Jin, Y.; Wang, Y. Preparation, Integration, and piezoresistive performance of MXene materials for multifunctional pressure sensor design. Chem. Eng. J. 2025, 514, 163035. https://doi.org/10.1016/j.cej.2025.163035.

  • 25.

    Wen, L.; Fan, W.; Kang, J.; et al. Porous pressure sensors from mechanisms to application: A review. Sens. Actuators A Phys. 2025, 387, 116461. https://doi.org/10.1016/j.sna.2025.116461.

  • 26.

    Nie, L.; Sun, Y.; Ming, X.; et al. High-resolution 3D printed strain sensor with superb stretchability and sensitivity: Unveiling the potential of melt electrowriting. Mater. Today 2025, 84, 39–47. https://doi.org/10.1016/j.mattod.2025.01.017.

  • 27.

    Zhao, W.; Trung, V.D.; Hong, X.; et al. Textile-based interdigitated electrode piezoresistive sensor: Materials, fabrication and applications—A review. J. Alloys Compd. 2025, 1014, 178764. https://doi.org/10.1016/j.jallcom.2025.178764.

  • 28.

    Zheng, Y.; Xiao, Y.; Zhou, H.; et al. Recent Advances of Flexible Pressure Sensors: A Review. IEEE Sensors J. 2025, 25, 40978–41005. https://doi.org/10.1109/jsen.2025.3618897.

  • 29.

    Luo, X.; Cheng, H.; Chen, K.; et al. Multi-walled carbon nanotube-enhanced polyurethane composite materials and the application in high-performance 3D printed flexible strain sensors. Compos. Sci. Technol. 2024, 257, 110818. https://doi.org/10.1016/j.compscitech.2024.110818.

  • 30.

    Xiang, D.; Zhang, X.; Liu, Z.; et al. Preparation and performance of biaxially stretched CNT-SBS/POE flexible strain sensor with a double percolation structure. Sens. Actuators A Phys. 2024, 371, 115340. https://doi.org/10.1016/j.sna.2024.115340.

  • 31.

    Guo, X.; Li, H.; Hong, W.; et al. 3-D Printed Fourth-Order Star-Like Negative Poisson’s Ratio Structure for High-Sensitivity Bionic Flexible Capacitive Pressure Sensor. IEEE Sensors J. 2024, 24, 13937–13945. https://doi.org/10.1109/jsen.2024.3374304.

  • 32.

    Huang, L.; Wang, S.; Zhang, P.; et al. A Flexible Pressure Sensor with Wide Detection Range Based on Capacitive-Piezoresistive Dual Mode Conversion for Human-Machine Interaction. Adv. Mater. Technol. 2024, 9, 2301377. https://doi.org/10.1002/admt.202301377.

  • 33.

    Yang, Z.; Guo, Z.; Lan, W.; et al. Flexible, Breathable and Sweatproof Piezoelectric Sensors Based on Additive Manufacturing. In Proceedings of the 2024 6th International Conference on Electronic Engineering and Informatics (EEI), Chongqing, China, 28–30 June 2024; pp. 241–244. https://doi.org/10.1109/eei63073.2024.10696538.

  • 34.

    Lei, M.; Ji, B.; Ding, S.; et al. Template-free Formation of Hybrid Dielectric for Flexible Capacitive Sensors with Wide-Pressure-Range Linear Detection. Adv. Mater. Technol. 2023, 8, 2301077. https://doi.org/10.1002/admt.202301077.

  • 35.

    Zhang, C.; Ouyang, W.; Zhang, L.; et al. A dual-mode fiber-shaped flexible capacitive strain sensor fabricated by direct ink writing technology for wearable and implantable health monitoring applications. Microsystems Nanoeng. 2023, 9, 158. https://doi.org/10.1038/s41378-023-00634-9.

  • 36.

    Shi, Z.; Meng, L.; Shi, X.; et al. Morphological Engineering of Sensing Materials for Flexible Pressure Sensors and Artificial Intelligence Applications. Nano-Micro Lett. 2022, 14, 141. https://doi.org/10.1007/s40820-022-00874-w.

  • 37.

    Joo, D.H.; Kang, M.S.; Park, S.J.; et al. Fabrication method of flexible strain sensors with CNTs and solvents. Sens. Actuators A Phys. 2022, 345, 113775. https://doi.org/10.1016/j.sna.2022.113775.

  • 38.

    Liu, M.Y.; Hang, C.Z.; Wu, X.Y.; et al. Investigation of stretchable strain sensor based on CNT/AgNW applied in smart wearable devices. Nanotechnology 2022, 35, 255501. https://doi.org/10.1088/1361-6528/ac5ee6.

  • 39.

    Li, Y.; Liu, Y.; Bhuiyan, S.R.A.; et al. Printed Strain Sensors for On-Skin Electronics. Small Struct. 2022, 3, 2100131. https://doi.org/10.1002/sstr.202100131.

  • 40.

    Dragone, D.; Randazzini, L.; Stano, G.; et al. Design, testing and control of a smart haptic interface driven by 3D-printed soft pneumatic actuators for virtual reality-based hand rehabilitation. Smart Mater. Struct. 2023, 32, 045009. https://doi.org/10.1088/1361-665x/acbd76.

  • 41.

    Luo, H.; Niu, H.; Li, B.; et al. AI-Enhanced Materials Identification with Smart Directional Tangential Flexible Sensor. Adv. Mater. Technol. 2025, 10, 2401164. https://doi.org/10.1002/admt.202401164.

  • 42.

    Hui, Y.; Li, S.; Chen, X. Fully Flexible Vibration Sensor Based on Suspended Membrane with Metal Microcracks. In Proceedings of the 2025 IEEE 20th International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2025, Zhuhai, China, 11–14 May 2025; pp. 492–496. https://doi.org/10.1109/nems67320.2025.11169956.

  • 43.

    Kar, D.; Sridharan, K.; George, B. Design and Development of a Flexible Inductive Bend Sensor Using Planar Meander Coil Topology. IEEE Trans. Instrum. Meas. 2025, 74, 9538609. https://doi.org/10.1109/tim.2025.3621749.

  • 44.

    Peng, Y.; Li, S.; Xia, Z.; et al. Recent advances in flexible bending sensors and their applications. Int. J. Smart Nano Mater. 2024, 15, 697–729. https://doi.org/10.1080/19475411.2024.2417834.

  • 45.

    Guo, D.; Ling, J.; Huang, Y.; et al. Recrystallization-Induced Laser Lift-Off Strategy for Flexible Thermal Sensors with Near-Limit Sensitivity. Adv. Mater. Technol. 2024, 9, 2301444. https://doi.org/10.1002/admt.202301444.

  • 46.

    Ma, G.; Zhang, M.; Gao, F.; et al. Bioinspired, fiber-based, flexible self-powered sensor for wearable applications. Device 2024, 2, 100508. https://doi.org/10.1016/j.device.2024.100508.

  • 47.

    Ma, L.Y.; Soin, N. Recent Progress in Printed Physical Sensing Electronics for Wearable Health-Monitoring Devices: A Review. IEEE Sensors J. 2022, 22, 3844–3859. https://doi.org/10.1109/jsen.2022.3142328.

  • 48.

    Phan, T.T.T.; Nguyen, K.H.V.; Phu Do, X.; et al. A new design of an electromyography sensor for movement detection in human-machine interaction systems. Adv. Mech. Eng. 2025, 17, 16878132251338446. https://doi.org/10.1177/16878132251338446.

  • 49.

    Wang, B.; Wei, X.; Chen, J.; et al. Self-powered miniaturized acceleration sensor based on rationally patterned electrodes. IEEE Open J. Nanotechnol. 2021, 2, 78–85. https://doi.org/10.1109/ojnano.2021.3104961.

  • 50.

    Xie, Y.; Cheng, H.; Yuan, C.; et al. Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor. Microsyst. Nanoeng. 2024, 10, 165. https://doi.org/10.1038/s41378-024-00813-2.

  • 51.

    Srinivas, G.L.; Khan, S. Improving Soft Capacitive Tactile Sensors: Scalable Manufacturing, Reduced Crosstalk Design, and Machine Learning. In Proceedings of the 2024 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), Tampere, Finland, 30 June–3 July 2024. https://doi.org/10.1109/fleps61194.2024.10604006.

  • 52.

    Feng, Z.; He, Q.; Wang, X.; et al. Multimode human—Machine interface using a single-channel and patterned triboelectric sensor. Nano Res. 2022, 15, 9352–9358. https://doi.org/10.1007/s12274-022-4564-3.

  • 53.

    Qiang, W.; Qin, L.; Deng, S.; et al. Wafer-level high performance MIM humidity sensor based on nanofiber-microcavity hierarchical structures. Sens. Actuators B Chem. 2026, 451, 139301. https://doi.org/10.1016/j.snb.2025.139301.

  • 54.

    Xiao, C.; Liu, X.; Zhao, Y.; et al. Flexible humidity sensors for diverse applications. Microsystems Nanoeng. 2025, 11, 221. https://doi.org/10.1038/s41378-025-01086-z.

  • 55.

    Chen, A.; Lu, S.; Jiao, J.; et al. Highly Flexible and Sensitive Proximity Sensor Based on Liquid Metal Microfluidic Fibers. In Proceedings of the 2024 IEEE International Conference on Robotics and Biomimetics (ROBIO), Bangkok, Thailand, 10–14 December 2024; pp. 1066–1071. https://doi.org/10.1109/robio64047.2024.10907491.

  • 56.

    Kanoun, O.; Khriji, S.; Naifar, S.; et al. Prospects of wireless energy-aware sensors for smart factories in the industry 4.0 era. Electronics 2021, 10, 2929. https://doi.org/10.3390/electronics10232929.

  • 57.

    Hamdule, S.; Goilkar, S. An Overview of the Untapped Potential of Soft Robotic Arms with Integration of Machining Tools. In Lecture Notes in Mechanical Engineering; Springer: Singapore, 2024; pp. 107–115. https://doi.org/10.1007/978-981-99-6094-1_11.

  • 58.

    Chen, X.; Stegagno, P.; Yuan, C. A Cable-Driven Switching-Legged Inchworm Soft Robot: Design and Testing. In Proceedings of the American Control Conference, New Orleans, LA, USA, 25–28 May 2021; pp. 2–7. https://doi.org/10.23919/acc50511.2021.9482921.

  • 59.

    Zeng, S.; Qiu, Y.; Peng, Q.; et al. Neural network-assisted carbon nanotube hydrogel-based dual-modal sensors for material perception. J. Alloys Compd. 2025, 1013, 178534. https://doi.org/10.1016/j.jallcom.2025.178534.

  • 60.

    Liu, G.; Yu, P.; Tao, Y.; et al. Hybrid 3D printed three-axis force sensor aided by machine learning decoupling. Int. J. Smart Nano Mater. 2024, 15, 261–278. https://doi.org/10.1080/19475411.2024.2312356.

  • 61.

    Liang, Y.C.; Shiu, S.C.; Liu, C.W. Enhancing yield and process efficiency through dual internet of things and augmented reality for AI-driven human-machine interaction in centering mass production. J. Manuf. Syst. 2025, 82, 26–41. https://doi.org/10.1016/j.jmsy.2025.06.003.

  • 62.

    Anastasiou, E.; Gkouvas, K.; Papavasileiou, A.; et al. A User-Centric AI-Enhanced Approach to AR frameworks in Manufacturing. Procedia CIRP 2025, 134, 1071–1076. https://doi.org/10.1016/j.procir.2025.02.235.

  • 63.

    Fan, H.; Zhang, H.; Ma, C.; et al. Enhancing metal additive manufacturing training with the advanced vision language model: A pathway to immersive augmented reality training for non-experts. J. Manuf. Syst. 2024, 75, 257–269. https://doi.org/10.1016/j.jmsy.2024.06.007.

  • 64.

    Shu, Q.; Xu, Z.; Liu, S.; et al. Magnetic flexible sensor with tension and bending discriminating detection. Chem. Eng. J. 2022, 433, 134424. https://doi.org/10.1016/j.cej.2021.134424.

  • 65.

    Li, M.; Pei, Y.; Cao, Y.; et al. Flexible strain sensors: From devices to array integration. Flex. Print. Electron. 2021, 6, 043002. https://doi.org/10.1088/2058-8585/ac20bf.

  • 66.

    Contini, G.; Grandi, F.; Peruzzini, M. Human-Centric Green Design for automatic production lines: Using virtual and augmented reality to integrate industrial data and promote sustainability. J. Ind. Inf. Integr. 2025, 44, 100801. https://doi.org/10.1016/j.jii.2025.100801.

  • 67.

    Valentini, L.; Weistroffer, V.; Grandi, F.; et al. Digital toolkit for human-centered machine design: Development and testing of an innovative system integrating virtual reality and HMI digital prototypes. Int. J. Adv. Manuf. Technol. 2026, 144, 7717–7735. https://doi.org/10.1007/s00170-025-16943-4.

  • 68.

    Grandi, F.; Morganti, A.; Khamaisi, R.K.; et al. Enhancing automated production lines with human-centricity: How to combine virtual reality simulation and human data analysis. Int. J. Comput. Integr. Manuf. 2025, 38, 952–976. https://doi.org/10.1080/0951192x.2025.2478004.

  • 69.

    Zhao, C.; Xu, X.; Zhang, D.; et al. Intelligent Human-Machine Interaction Based on Digital Twin and Virtual Reality. In Proceedings of the 2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI), Hangzhou, China, 1–3 December 2023; pp. 374–378. https://doi.org/10.1109/ricai60863.2023.10489645.

  • 70.

    Machac, T.; Horejsi, P.; Simon, M. Augmented Reality-Enabled Visualisation and Process Control in 3D Printing and Additive Manufacturing. MM Sci. J. 2025, 8976. https://doi.org/10.17973/mmsj.2025_12_2025090.

  • 71.

    Pelka, M.; Willemsen, T. Step Into Context: Location-Aware AR Feedback for Smart Manufacturing Using Low-Cost Hardware. In Proceedings of the 15th International Conference on Indoor Positioning and Indoor Navigation, Tampere, Finland, 15–18 September 2025. https://doi.org/10.1109/ipin66788.2025.11213170.

  • 72.

    Hsieh, T.C.; Smith, S. An innovative AR-assisted self-adjusting digital twin for speed control of a servomechanism. Comput. Ind. Eng. 2024, 193, 110328. https://doi.org/10.1016/j.cie.2024.110328.

  • 73.

    Marino, E.; Barbieri, L.; Bruno, F.; et al. Context-Aware Recoloring Approach for AR-Assisted Maintenance Applications. In Lecture Notes in Mechanical Engineering; Springer: Cham, Switzerland, 2024; pp. 471–478. https://doi.org/10.1007/978-3-031-58094-9_52.

  • 74.

    Ajikumar, A.; Moghaddam, M. Not Lost In Translation—Spatiotemporally-Aligned Human-Machine Interfaces Through IoT-Integrated Augmented Reality. In Proceedings of the 2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), Bellevue, WA, USA, 21–25 October 2024; pp. 533–536. https://doi.org/10.1109/ismar-adjunct64951.2024.00154.

  • 75.

    Haegle, R.; Breitkopf, B.; Eiler, L.; et al. Developing Design Guidelines For Augmented Reality Applications As Operating Assistance For Manufacturing Machines. In Proceedings of the Conference on Production Systems and Logistics, Honolulu, HI, USA, 9–12 June 2024; pp. 192–203. https://doi.org/10.15488/17712.

  • 76.

    Ramalho, F.; Moreno, T.; Soares, A.; et al. Application of Augmented Reality to Support Manufacturing Resilience. In Lecture Notes in Mechanical Engineering; Springer: Cham, Switzerland, 2024; pp. 654–662. https://doi.org/10.1007/978-3-031-38165-2_76.

  • 77.

    Helmholz, J.; Vogt, M. Evaluating Digital Assistance in Form of Augmented Reality for Manual Processes in the Metal Binder Jetting Process Chain. In Springer Tracts in Additive Manufacturing; Springer: Cham, Switzerland, 2024; pp. 317–333. https://doi.org/10.1007/978-3-031-42983-5_22.

  • 78.

    Fang, W.; Chen, L.; Zhang, T.; et al. Head-mounted display augmented reality in manufacturing: A systematic review. Robot. Comput.-Integr. Manuf. 2023, 83, 102567. https://doi.org/10.1016/j.rcim.2023.102567.

  • 79.

    Yin, Y.; Zheng, P.; Li, C.; et al. A state-of-the-art survey on Augmented Reality-assisted Digital Twin for futuristic human-centric industry transformation. Robot. Comput.-Integr. Manuf. 2023, 81, 102515. https://doi.org/10.1016/j.rcim.2022.102515.

  • 80.

    Eswaran, M.; Gulivindala, A.K.; Inkulu, A.K.; et al. Augmented reality-based guidance in product assembly and maintenance/repair perspective: A state of the art review on challenges and opportunities. Expert Syst. Appl. 2023, 213, 118983. https://doi.org/10.1016/j.eswa.2022.118983.

  • 81.

    Eswaran, M.; Bahubalendruni, M.V.A.R. Challenges and opportunities on AR/VR technologies for manufacturing systems in the context of industry 4.0: A state of the art review. J. Manuf. Syst. 2022, 65, 260–278. https://doi.org/10.1016/j.jmsy.2022.09.016.

  • 82.

    Sundarramurthi, M.; Chandrashekar, K.; Nagarathna. Design and Development of a Smart Campus using Augmented Reality (SCAR). In Proceedings of the 2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022, Bangalore, India, 8–10 July 2022. https://doi.org/10.1109/conecct55679.2022.9865828.

  • 83.

    Murzynski, C.; Hussein-Abdeltawab; Ashour, O.; et al. Constructing Reconfigurable and Affordable Robotic Arm Platform to Teach Robotics and Automation. In Proceedings of the 2024 ASEE Annual Conference & Exposition, Portland, Oregon, 23 June–12 July 2024. https://doi.org/10.18260/1-2--47069.

  • 84.

    Janousek, P.; Slanina, Z.; Walendziuk, W. Target-following Robotic Platform Based on UWB Localization and Depth Camera. IFAC-Pap. 2024, 58, 247–252. https://doi.org/10.1016/j.ifacol.2024.07.404.

  • 85.

    Kumar, S.; Mishra, D.; Reddy, U.; et al. Human-Machine Interaction Systems for Training Industrial Robots. In Proceedings of the OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, Raigarh, India, 5–7 June 2024. https://doi.org/10.1109/otcon60325.2024.10687996.

  • 86.

    Cao, F.; Fang, C.; Qin, X.; et al. Towards a Cloud-Controlled Heterogeneous Robot System Based on Microservices. In Proceedings of the 9th International Conference on Control Science and Systems Engineering, ICCSSE 2023, Shenzhen, China, 16–18 June 2023; pp. 202–207. https://doi.org/10.1109/iccsse59359.2023.10245035.

  • 87.

    Soares, I.; Petry, M.; Moreira, A.P. Programming robots by demonstration using augmented reality. Sensors 2021, 21, 5976. https://doi.org/10.3390/s21175976.

  • 88.

    Wang, S.; Jiao, Y.; Wang, L.; et al. Research on the Digital Twin System of Welding Robots Driven by Data. Sensors 2025, 25, 3889. https://doi.org/10.3390/s25133889.

  • 89.

    Wu, W.; Li, M.; Hu, J.; et al. Research on Guidance Methods of Digital Twin Robotic Arms Based on User Interaction Experience Quantification. Sensors 2023, 23, 7602. https://doi.org/10.3390/s23177602.

  • 90.

    Niermann, D.; Doernbach, T.; Petzoldt, C.; et al. Software framework concept with visual programming and digital twin for intuitive process creation with multiple robotic systems. Robot. Comput. Integr. Manuf. 2023, 82, 102536. https://doi.org/10.1016/j.rcim.2023.102536.

  • 91.

    Sartori, A.; Schlette, C. Agile Visual Programming of a Multi-robot Cyber-Physical System with Operator Interaction. In Proceedings of the 2022 7th International Conference on Robotics and Automation Engineering (ICRAE), Singapore, 18–20 November 2022; pp. 142–149. https://doi.org/10.1109/icrae56463.2022.10056167.

  • 92.

    Keshvarparast, A.; Battini, D.; Battaia, O.; et al. Collaborative robots in manufacturing and assembly systems: Literature review and future research agenda. J. Intell. Manuf. 2024, 35, 2065–2118. https://doi.org/10.1007/s10845-023-02137-w.

  • 93.

    Latif, A.; Mughall, A.; Khan, M.; et al. A Safety-Enhancing Framework Based on Collaborative Robots (CoBot) for Industry 4.0. In Proceedings of the International Conference on Engineering and Computing, ICECT 2024, Islamabad, Pakistan, 23 May 2024. https://doi.org/10.1109/icect61618.2024.10581298.

  • 94.

    Abishek, B.A.; Kavyashree, T.; Jayalakshmi, R.; et al. Collaborative Robots and Cyber Security in Industry 5.0. In Proceedings of the International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023, Coimbatore, India, 16–17 June 2023. https://doi.org/10.1109/icaeca56562.2023.10200319.

  • 95.

    Álvarez, D.; Væhrens, L. Collaborative robots for smart production in SMEs. In The Future of Smart Production for SMEs: A Methodological and Practical Approach towards Digitalization in SMEs; Springer: Cham, Switzerland, 2022; pp. 363–369. https://doi.org/10.1007/978-3-031-15428-7_33.

  • 96.

    Gleirscher, M.; Johnson, N.; Karachristou, P.; et al. Challenges in the Safety-Security Co-Assurance of Collaborative Industrial Robots. Intell. Syst. Control. Autom. Sci. Eng. 2022, 81, 191–214. https://doi.org/10.1007/978-3-030-78513-0_11.

  • 97.

    Gualtieri, L.; Rauch, E.; Vidoni, R. Methodology for the definition of the optimal assembly cycle and calculation of the optimized assembly cycle time in human-robot collaborative assembly. Int. J. Adv. Manuf. Technol. 2021, 113, 2369–2384. https://doi.org/10.1007/s00170-021-06653-y.

  • 98.

    Nuzzi, C.; Pasinetti, S.; Pagani, R.; et al. MEGURU: A gesture-based robot program builder for Meta-Collaborative workstations. Robot. Comput.-Integr. Manuf. 2021, 68, 102085. https://doi.org/10.1016/j.rcim.2020.102085.

  • 99.

    Dhivagar, S.; Subramani, M.; Yuvanesh, D.; et al. HandMatic: Wireless Robotic Arm Control Using Real-Time Gesture Recognition for Seamless Human-Machine Interaction. In Proceedings of the 2025 International Conference on Computing Technologies (ICOCT), Bengaluru, India, 13–14 June 2025. https://doi.org/10.1109/icoct64433.2025.11118862.

  • 100.

    Han, C.; Lee, J.; Lee, H.; et al. Zero-shot autonomous robot manipulation via natural language. Manuf. Lett. 2024, 42, 16–20. https://doi.org/10.1016/j.mfglet.2024.09.067.

  • 101.

    Wang, S.; Xu, P.; Zuo, R.; et al. Three-Dimensional Co-Printing of Hydrogels and Elastomers for Facile Fabrication of Soft Electronics and Robotics. IEEE Sensors J. 2025, 25, 30420–30430. https://doi.org/10.1109/jsen.2025.3564978.

  • 102.

    Wang, Y.; Wang, W.; Bao, G. Design and Control Scheme Of A Rigid-flexible Coupled Duel-arm Hug Robots. In Proceedings of the 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids), Nancy, France, 22–24 November 2024; pp. 1027–1033. https://doi.org/10.1109/humanoids58906.2024.10769818.

  • 103.

    Nope-Giraldo, R.; Illapuma-Ccallo, L.; Cornejo, J.; et al. Mechatronic Systems Design of ROHNI-1: Hybrid Cyber-Human Medical Robot for COVID-19 Health Surveillance at Wholesale-Supermarket Entrances. In Proceedings of the Pan American Health Care Exchanges, PAHCE, Sevilla, Spain, 15–20 March 2021. https://doi.org/10.1109/gmepe/pahce50215.2021.9434874.

  • 104.

    Verified Market Research. Industrial Human-Machine Interaction Market Size and Forecast. 2024. Available online:  https://www.verifiedmarketresearch.com/product/global-human-machine-interface-market-size-and-forecast/ (accessed 12 August 2026)

  • 105.

    Markets and Markets. Human Machine Interface Market Size, Share & Growth Report. 2023. Available online: https://www.marketsandmarkets.com/Market-Reports/human-machine-interface-technology-market-461.html (accessed on 11 February 2026)

  • 106.

    Darragh, M. These Are the 5 Best VR Headsets to Buy in 2026 after Spending Hundreds of Hours in Virtual Reality. 2026. Available online: https://www.tomsguide.com/best-picks/best-vr-headsets (accessed on 25 September 2026)

  • 107.

    Markets and Markets. AR VR Software Market Size, Size, Growth & Latest Trends. 2023. Available online:  https://www.marketsandmarkets.com/Market-Reports/ar-vr-software-market-186416119.html#:~:text=The%20global%20AR%20VR%20Software%20Market%20size%20was,a%20CAGR%20of%2019.0%25%20from%202023%20to%202028. (accessed on 11 February 2026)

  • 108.

    Human-Machine Collaboration in Industrial Operations: Activation Playbook; 2026. Available online: https://weforum.org/publications/human-machine-collaboration-in-industrial-operations-activation-playbook/ (accessed on 11 July 2026)

  • 109.

    Héder, M. From NASA to EU: The evolution of the TRL scale in Public Sector Innovation. Innov. J. 2017, 22, 1.

  • 110.

    Kim, D.; Min, J.; Ko, S.H. Recent Developments and Future Directions of Wearable Skin Biosignal Sensors. Adv. Sens. Res. 2024, 3, 2300118. https://doi.org/10.1002/adsr.202300118.

  • 111.

    Tu, X.; Autiosalo, J.; Ala-Laurinaho, R.; et al. TwinXR: Method for using digital twin descriptions in industrial eXtended reality applications. Front. Virtual Real. 2023, 4, 1019080. https://doi.org/10.3389/frvir.2023.1019080.

  • 112.

    Bellini, S.; Francescato, G.; Giaimo, F.; et al. Applying social robotics principles to B2B manufacturing mobile robots to design new ways of working. In Proceedings of the IEEE Workshop on Advanced Robotics and Its Social Impacts, ARSO, Osaka, Japan, 17–19 July 2025; pp. 380–387. https://doi.org/10.1109/arso64737.2025.11124972.

  • 113.

    Johnson, M.; Bradshaw, J.M.; Feltovich, P.J.; et al. Beyond cooperative robotics: The central role of interdependence in coactive design. IEEE Intell. Syst. 2011, 26, 81–88. https://doi.org/10.1109/mis.2011.47.

  • 114.

    Forum Stories and Frontiers Media S.A. Top 10 Emerging Technologies of 2023. World Economic Forum 2023. Available online: https://www3.weforum.org/docs/WEF_Top_10_Emerging_Technologies_of_2023.pdf (accessed on 11 February 2026).

  • 115.

    Edelman, B.J.; Zhang, S.; Schalk, G.; et al. Non-Invasive Brain-Computer Interfaces: State of the Art and Trends. IEEE Rev. Biomed. Eng. 2025, 18, 26–49. https://doi.org/10.1109/rbme.2024.3449790.

  • 116.

    BRAIN Neuroethics Working Group (NEWG) Meeting—May 2025|BRAIN Initiative. Available online: https://braininitiative.nih.gov/news-events/events/brain-neuroethics-working-group-newg-meeting-may-2025# (accessed on 11 February 2026).

  • 117.

    Recommendation on the ethics of Neurotechnology. UNESCO 2025. Available online: https://unesdoc.unesco.org/ark:/48223/pf0000394866/PDF/394866eng.pdf.multi (accessed on 11 February 2026).

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Mourtzis, D.; Tsakalos, D. Emerging Technologies in Human-Machine Interaction towards Industry 5.0 and Industrial Metaverse: Challenges and Opportunities. Human-Machine Interaction 2026, 1 (1), 4.
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