Abstract
The exoskeleton robot is a new type of cooperative robot that forms a typical human-robot system with its wearer. In order to achieve a common task goal, human-exoskeleton cooperative tasks need to integrate natural cooperation. Exoskeleton robots must not only predict the task goals of human partners but also make human partners anticipate their own intentions and perceive the execution of tasks during cooperation. Therefore, studying the transparency of exoskeleton robots plays a very important role in improving the mutual understanding between humans and exoskeletons and enhancing the interoperability and naturalness of human-robot cooperation. In previous studies, we investigated the human-exoskeleton behavioral information transfer path given the lack of an efficient communication mechanism between the wearer and the exoskeleton named AIDER. A tactile feedback method based on the change of vibration intensity was proposed to transmit the current walking state of the human-exoskeleton system to the wearer, and a voice-based interaction method was proposed to express the human-robot behavioral intention. To verify the effectiveness of the above methods, this study analyzed the wearer’s mental workload using functional near-infrared spectroscopy while performing the human-exoskeleton walking tasks in the different modes. The results showed that the mental load of the transparency mode (TM) was similar to that of the normal walking mode (NWM). However, the data fluctuated more than in the other two modes in the NTM (non-transparency mode), indicating that the mental workload is heavier than in the other groups. Therefore, transparency can help reduce the mental workload of the wearer. It was shown that transparency plays an important role in the collaboration between humans and robots, which can improve efficiency and trust between the exoskeleton and the wearer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schaefer, K. E., Brewer, R.W., Putney, J., et al.: Relinquishing manual control: collaboration requires the capability to understand robot intent. In: International Conference on Collaboration Technologies and Systems, Orlando, pp. 359–366. IEEE (2016)
Chen, J.Y.C., Barnes, M.J.: Human-agent teaming for multirobot control: a review of human factors issues. IEEE Trans. Hum.-Mach. Syst. 44(1), 13–29 (2014)
Ali, A., Azevedo-Sa, H., Tilbury, D.M., et al.: Heterogeneous human-robot task allocation based on artificial trust. Sci. Rep. 12(1), 1–15 (2022)
Chena, J.Y.C., Lakhmanib, S.G., Stowersb, K., et al.: Situation awareness-based agent transparency and human-autonomy teaming effectiveness. Theor. Issues Ergon. Sci. 19(3), 259–282 (2018)
David, R.A., Nielsen, P.: Defense science board summer study on autonomy. Defense Science Board Washington United States, Washington DC (2016)
Gunning, D., Aha, D.: DARPA’s explainable artificial intelligence (XAI) program. AI Mag. 40(2), 44–58 (2019)
Boden, M., Bryson, J., Caldwell, D., et al.: Principles of robotics: regulating robots in the real world. Connect. Sci. 29(2), 124–129 (2017)
Roncone, A., Mangin, O., Scassellati, B.: Transparent role assignment and task allocation in human robot collaboration. In: IEEE International Conference on Robotics and Automation, Singapore, pp. 1014–1021. IEEE (2017)
Chen, L., Zhou, M., Wu, M., et al.: Three-layer weighted fuzzy support vector regression for emotional intention understanding in human-robot interaction. IEEE Trans. Fuzzy Syst. 26(5), 2524–2538 (2018)
Guznov, S., Lyons, J., Pfahler, M., et al.: Robot transparency and team orientation effects on human-robot teaming. Int. J. Hum.-Comput. Interact. 36(7), 650–660 (2020)
Wortham, R.H., Theodorou, A.: Robot transparency, trust and utility. Connection Sci. 29(3), 242–248 (2017)
Sharkey, A., Sharkey, N.: Granny and the robots: ethical issues in robot care for the elderly. Ethics Inf. Technol. 14(1), 27–40 (2012)
Ramaraj, P., Sahay, S., Kumar, S.H., et al.: Towards using transparency mechanisms to build better mental models. In: Advances in Cognitive Systems, Cambridge, Massachusetts, vol. 7, pp. 1–6 (2019)
Qiu, J., Wang, Y., Cheng, H., et al.: Auditory movement feedforward for a lower-limb exoskeleton device (AIDER) to increase transparency. Int. J. Hum. Factors Model. Simul. 7(3–4), 247–261 (2022)
Qiu, J., Wang, Y., Cheng, H., Wang, Lu., Yang, X.: A pilot study on auditory feedback for a lower-limb exoskeleton to increase walking safety. In: Black, N.L., Neumann, W.P., Noy, I. (eds.) IEA 2021. LNNS, vol. 223, pp. 325–334. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-74614-8_39
Wang, Y., Qiu, J., Cheng, H., Wang, L.: A prospective study of haptic feedback method on a lower-extremity exoskeleton. In: Gao, Q., Zhou, J. (eds.) HCII 2021. LNCS, vol. 12786, pp. 253–261. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78108-8_19
Roy, C.S., Sherrington, C.S.: On the regulation of the blood supply of the brain. J. Physiol. 11(1–2), 85–158 (1890)
Ma, R., Xia, X., Zhang, W., et al.: High gamma and beta temporal interference stimulation in the human motor cortex improves motor functions. Front. Neurosci. 15, 1743 (2022)
Cools, R., Arnsten, A.F.T.: Neuromodulation of prefrontal cortex cognitive function in primates: the powerful roles of monoamines and acetylcholine. Neuropsychopharmacology 47(1), 309–328 (2022)
Asgher, U., Ahmad, R., Naseer, N., et al.: Assessment and classification of mental workload in the prefrontal cortex (PFC) using fixed-value modified beer-lambert law. IEEE Access 7, 143250–143262 (2019)
Wang, Y., Qiu, J., Cheng, H., et al.:Analysis of human-exoskeleton system interaction for ergonomic design. Hum. Factors 0018720820913789 (2020)
Acknowledgments
This research project was supported by the key project of the Joint Foundation of the National Natural Science Foundation of China (No. U19A2082) and the National Natural Science Foundation of China (No. 62103081).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y. et al. (2023). The Effect of Transparency on Human-Exoskeleton Interaction. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2023. Lecture Notes in Computer Science, vol 14028. Springer, Cham. https://doi.org/10.1007/978-3-031-35741-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-031-35741-1_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35740-4
Online ISBN: 978-3-031-35741-1
eBook Packages: Computer ScienceComputer Science (R0)