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On Human-in-the-Loop CPS in Healthcare: A Cloud-Enabled Mobility Assistance Service

Published online by Cambridge University Press:  06 February 2019

Ricardo C. de Mello Open the ORCID record for Ricardo C. de Mello [Opens in a new window] ,
Mario F. Jimenez ,
Moises R. N. Ribeiro ,
Rodrigo Laiola Guimarães  and
Anselmo Frizera-Neto Open the ORCID record for Anselmo Frizera-Neto [Opens in a new window]
Ricardo C. de Mello*
Affiliation:
Electrical Engineering Graduate Program, Federal University of Espírito Santo, Av. Fernando Ferrari, 514, Goiabeiras, Vitoria, 29075-910, Brazil. E-mails: [email protected], [email protected]
Mario F. Jimenez
Affiliation:
Bioengineering Graduate Program, El Bosque University, Bogotá, Colombia. E-mail: [email protected]
Moises R. N. Ribeiro
Affiliation:
Electrical Engineering Graduate Program, Federal University of Espírito Santo, Av. Fernando Ferrari, 514, Goiabeiras, Vitoria, 29075-910, Brazil. E-mails: [email protected], [email protected]
Rodrigo Laiola Guimarães
Affiliation:
Postgraduate Program in Computer Science, Federal University of Espírito Santo, Av. Fernando Ferrari, 514, Goiabeiras, Vitoria, 29075-910, Brazil. E-mail: [email protected]
Anselmo Frizera-Neto
Affiliation:
Electrical Engineering Graduate Program, Federal University of Espírito Santo, Av. Fernando Ferrari, 514, Goiabeiras, Vitoria, 29075-910, Brazil. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

Despite recent advancements on cloud-enabled and human-in-the-loop cyber-physical systems, there is still a lack of understanding of how infrastructure-related quality of service (QoS) issues affect user-perceived quality of experience (QoE). This work presents a pilot experiment over a cloud-enabled mobility assistive device providing a guidance service and investigates the relationship between QoS and QoE in such a system. In our pilot experiment, we employed the CloudWalker, a system linking smart walkers and cloud platforms, to physically interact with users. Different QoS conditions were emulated to represent an architecture in which control algorithms are performed remotely. Results point out that users report satisfactory interaction with the system even under unfavorable QoS conditions. We also found statistically significant data linking QoE degradation to poor QoS conditions. We finalize discussing the interplay between QoS requirements, the human-in-the-loop effect, and the perceived QoE in healthcare applications.

Keywords

HealthcareCyber-physical systemsAssistive roboticsCommunication networkscloud roboticsQoSQoE
Type
Articles
Information
Robotica , Volume 37 , Issue 9 , September 2019 , pp. 1477 - 1493
Copyright
© Cambridge University Press 2019 

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References

Robinson, H., MacDonald, B. and Broadbent, E., “The role of healthcare robots for older people at home: A Review,” Int. J. Soc. Robot. 6(4), 575–591 (2014).CrossRefGoogle Scholar
Liu, Y., Peng, Y., Wang, B., Yao, S. and Liu, Z., “Review on cyber-physical systems,” IEEE/CAA J. Autom. Sinica 4(1), 27–40 (2017).CrossRefGoogle Scholar
Beckerle, P., Salvietti, G., Unal, R., Prattichizzo, D., Rossi, S., Castellini, C., Hirche, S., Endo, S., Amor, H. B., Ciocarlie, M., Mastrogiovanni, F., Argall, B. D. and Bianchi, M., “A human-robot interaction perspective on assistive and rehabilitation robotics,” Front. Neurorob. 11, 1–6 (2017).CrossRefGoogle Scholar
Haque, S. A., Aziz, S. M. and Rahman, M., “Review of cyber-physical system in healthcare,” Int. J. Distrib. Sens. Netw. 217415 (2014).CrossRefGoogle Scholar
Reppou, S. E., Tsardoulias, E. G., Kintsakis, A. M., Symeonidis, A. L., Mitkas, P. A., Psomopoulos, F. E., Karagiannis, G. T., Zielinski, C., Prunet, V., Merlet, J. P., Iturburu, M. and Gkiokas, A., “RAPP: A robotic-oriented ecosystem for delivering smart user empowering applications for older people,” Int. J. Soc. Robot. 8(4), 539–552 (2016).CrossRefGoogle Scholar
Hu, G., Tay, W. and Wen, Y., “Cloud robotics: Architecture, challenges and applications” IEEE Netw. 26(3), 21–28 (2012).CrossRefGoogle Scholar
Wan, J., Tang, S., Yan, H., Li, D., Wang, S. and Vasilakos, A. V., “Cloud robotics: Current status and open issues,” IEEE Access 4, 2797–2807 (2016).Google Scholar
Kehoe, B., Patil, S., Abbeel, P. and Goldberg, K., “A survey of research on cloud robotics and automation,” IEEE Trans. Autom. Sci. Eng. 12(2), 398–409 (2015).CrossRefGoogle Scholar
Tsardoulias, E. G., Kintsakis, A. M., Panayiotou, K., Thallas, A. G., Reppou, S. E., Karagiannis, G. G., Iturburu, M., Arampatzis, S., Zielinski, C., Prunet, V., Psomopoulos, F. E., Symeonidis, A. L. and Mitkas, P. A., “Towards an integrated robotics architecture for social inclusion – The RAPP paradigm,” Cognit. Syst. Res. 43, 157–173 (2017).CrossRefGoogle Scholar
Cardarelli, E., Digani, V., Sabattini, L., Secchi, C. and Fantuzzi, C., “Cooperative cloud robotics architecture for the coordination of multi-AGV systems in industrial warehouses,” Mechatronics 45, 1–13 (2017).CrossRefGoogle Scholar
Shah, T., Yavari, A., Mitra, K., Saguna, S., Jayaraman, P. P., Rabhi, F. and Ranjan, R., “Remote health care cyber-physical system: Quality of service (QoS) challenges and opportunities,” IET Cyber-Phys. Syst.: Theory Appl. 1(1), 40–48 (2016).Google Scholar
Roy, A., Roy, C. and Misra, S., “CARE: Criticality-Aware Data Transmission in CPS-based Healthcare Systems,” In: 2018 IEEE International Conference on Communications Workshops (ICC Workshops) (2018) pp. 1–6.Google Scholar
Skorin-kapov, L. and Ebrahimi, T., “Quality of Service Versus Quality of Experience,” In: Quality of Experience: Advanced Concepts, Applications and Methods (Möller, S. and Raake, A., eds.) (Springer International Publishing, Cham, 2014) pp. 85–96.Google Scholar
Pons, J. L., Wearable Robots: Biomechatronic Exoskeletons (John Wiley & Sons, Ltd, Chichester, UK, 2008).CrossRefGoogle Scholar
Bordel, B., Alcarria, R., Robles, T. and Martín, D., “Cyber–physical systems: Extending pervasive sensing from control theory to the internet of things,” Pervasive Mob. Comput. 40, 156–184 (2017).CrossRefGoogle Scholar
Lee, I., Sokolsky, O., Chen, S., Hatcliff, J., Jee, E., Kim, B., King, A., Mullen-Fortino, M., Park, S., Roederer, A. and Venkatasubramanian, K. K., “Challenges and research directions in medical cyber-physical systems,” Proc. IEEE 100(1), 75–90 (2012).Google Scholar
Hammer, F., Egger-lampl, S. and Moller, S., “Position Paper: Quality-of-Experience of Cyber-Physical System Applications,” In: 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX) (2017) pp. 1–3.Google Scholar
Munir, S., Stankovic, J., Mike Liang, C. and Lin, S., “Cyber Physical System Challenges for Human-in-the-Loop Control,” In: The 8th International Workshop on Feedback Computing (2013).Google Scholar
Flemisch, F. O., Bengler, K., Bubb, H., Winner, H. and Bruder, R., “Towards cooperative guidance and control of highly automated vehicles: H-Mode and Conduct-by-Wire,” Ergonomics 57(3), 343–360 (2014).CrossRefGoogle Scholar
Walsh, C., “Human-in-the-loop development of soft wearable robots,” Nat. Rev. Mater. 3, 78–80 (2018).CrossRefGoogle Scholar
Hossain, M. S., “Cloud-supported cyber-physical localization framework for patients monitoring,” IEEE Syst. J. 11(1), 118–127 (2017).CrossRefGoogle Scholar
Zhang, Y., Qiu, M., Tsai, C., Hassan, M. and Alamri, A., “Health-CPS: Healthcare cyber-physical system assisted by cloud and big data,” IEEE Syst. J. 11(1), 88–95 (2017).CrossRefGoogle Scholar
Chen, M., Ma, Y., Song, J., Lai, C. and Hu, B., “Smart clothing: Connecting human with clouds and big data for sustainable health monitoring,” Mob. Netw. Appl. 21(5), 825–845 (2016).CrossRefGoogle Scholar
Dogmus, K., Erdem, E. and Patoglu, V., “REHABROBO–ONTO: Design, development and maintenance of a rehabilitation robotics ontology on the cloud,” Robot. Comput.-Integr. Manuf. 33, 100–109 (2015).CrossRefGoogle Scholar
Tsuji, T., Kaneko, T. and Sakaino, S., “Motion matching in rehabilitation databases with force and position information,” IEEE Trans. Ind. Electron. 63(3), 1935–1942 (2016).CrossRefGoogle Scholar
Fiorini, L., Esposito, R., Bonaccorsi, M., Petrazzuolo, C., Saponara, F., Giannantonio, R., De Petris, G., Dario, P. and Cavallo, F., “Enabling personalised medical support for chronic disease management through a hybrid robot-cloud approach,” Auton. Robots 41(5), 1263–1276 (2017).CrossRefGoogle Scholar
Radu, C., Candea, C. and Candea, G., “Towards an Assistive System for Human,” In: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments - PETRA ’16 (2016) pp. 1–4.Google Scholar
Li, H. J. and Song, A. G., “Architectural design of a cloud robotic system for upper-limb rehabilitation with multimodal interaction,” J. Comput. Sci. Technol. 32(2), 258–268 (2017).CrossRefGoogle Scholar
Fu, J., Jones, M., Liu, T., Hao, W., Yan, Y., Qian, G. and Jan, Y. K., “A novel mobile-cloud system for capturing and analyzing wheelchair maneuvering data: A pilot study,” Assist. Technol. 28(2), 105–114 (2016).CrossRefGoogle Scholar
Salhi, K., Alimi, A. M., Gorce, P. and Ben Khelifa, M. M., “Navigation Assistance to Disabled Persons with Powered Wheelchairs using Tracking System and Cloud Computing Technologies,” In: Proceedings - International Conference on Research Challenges in Information Science (2016).Google Scholar
Wachaja, A., Agarwal, P., Zink, M., Adame, M. R., Möller, K. and Burgard, W., “Navigating blind people with walking impairments using a smart walker,” Auton. Robots Dec 2015 to 1–19, Aug 2016.Google Scholar
Panteleris, P. and Argyros, A. A., “Vision-based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform,” In: Lecture Notes in Computer Science, vol. 8927 (Springer International Publishing, Cham, 2015) pp. 407–423.Google Scholar
Cifuentes, C. A., Rodriguez, C., Frizera-Neto, A., Bastos-Filho, T. F. and Carelli, R., “Multimodal human–robot interaction for walker-assisted gait,” IEEE Syst. J. 10(3), 933–943 (2016).CrossRefGoogle Scholar
Dominicini, C. K., Vassoler, G. L., Ribeiro, M. R. N. and Martinello, M., “VirtPhy: A Fully Programmable Infrastructure for Efficient NFV in Small Data Centers,” In: 2016 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2016 (2017) pp. 81–86.Google Scholar
Naman, A. T., Wang, Y., Gharakheili, H. H., Sivaraman, V. and Taubman, D., “Responsive high throughput congestion control for interactive applications over SDN-enabled networks,” Comput. Netw. 134, 152–166 (2018).CrossRefGoogle Scholar
Martinello, M., Ribeiro, M. R N, De Oliveira, R. E. Z. and De Angelis Vitoi, R., “Keyflow: A prototype for evolving SDN toward core network fabrics,” IEEE Netw. 28(2), 12–19 (2014).CrossRefGoogle Scholar
Pocovi, G. et al., “Achieving ultra-reliable low-latency communications: Challenges and envisioned system enhancements,” IEEE Netw. 32(2), 8–15 (2018).CrossRefGoogle Scholar
Martinez, V. G., Mello, R. C., Guimaraes, R. S., Ribeiro, M. R. N., Martinello, M., Hasse, P. and Frascolla, V., “Ultra Reliable Communication for Robot Mobility Enabled by SDN Splitting of Wifi Functions,” In: IEEE Symposium on Computers and Communications (IEEE, Natal, Brazil, 2018).Google Scholar
Hoßfeld, T., Heegaard, P. E., Varela, M. and Möller, S., “QoE beyond the MOS: An in-depth look at QoE via better metrics and their relation to MOS,” Qual. User Exp. 1, 1–23 (2016).CrossRefGoogle Scholar
Streijl, R. C., Winkler, S. and Hands, D. S., “Mean opinion score (MOS) revisited: Methods and applications, limitations and alternatives,” Multimedia Syst. 22(2), 213–227 (2016).CrossRefGoogle Scholar
Talman, L. S. et al., “Longitudinal study of vision and retinal nerve fiber layer thickness in multiple sclerosis,” Ann. Neurol. 67(6), 749–760 (2010).Google Scholar
Alexander, N. B. and Goldberg, A., “Gait disorders: Search for multiple causes,” Cleveland Clin. J. Med. 72(7), 586–600 (2005).CrossRefGoogle Scholar
Zhang, X., Han, Q. and Yu, X., “Survey on recent advances in networked control systems,” IEEE Trans. Ind. Inform. 12(5), 1740–1752 (2016).CrossRefGoogle Scholar
Dorf, R. C., The Engineering Handbook, 2nd edn (CRC PRESS, New York, 2004).Google Scholar
Monllor, M., Roberti, F., Jimenez, M., Frizera, A. and Carelli, R., “Path Following Control for Assistance Robots,” In: 2017 XVII Workshop on Information Processing and Control (RPIC) (2017) pp. 1–6.Google Scholar
Fiedler, M., Hossfeld, T. and Tran-Gia, P., “A generic quantitative relationship between quality of experience and quality of service,” IEEE Netw. 24(2), 36–41 (2010).CrossRefGoogle Scholar
Tatematsu, A., Ishibashi, Y., Fukushima, N. and Sugawara, S., “QoE assessment in tele-operation with 3d video and haptic media,” In: 2011 IEEE International Conference on Multimedia and Expo (2011) pp. 1–6.Google Scholar
Xu, X., Liu, Q. and Steinbach, E., “Toward QoE-Driven Dynamic Control Scheme Switching for Time-Delayed Teleoperation Systems: A Dedicated Case Study,” In: 2017 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE) (2017) pp. 1–6.Google Scholar
Lazar, J., Feng, J. H. and Hochheiser, H., Research Methods in Human-Computer Interaction, 2 edn (Morgan Kaufmann, Boston, 2017).Google Scholar
McDonald, J. H., Handbook of Biological Statistics, 3rd edn (Sparky House Publishing, Baltimore, 2014).Google Scholar
Wang, C. and Cesar, P., “Measuring Audience Responses of Video Advertisements Using Physiological Sensors,” In: Proceedings of the 3rd International Workshop on Immersive Media Experiences - ImmersiveME ’15 (2015) pp. 37–40.CrossRefGoogle Scholar
Werner, C., Ullrich, P., Geravand, M., Peer, A., Bauer, J. M. and Hauer, K., “A systematic review of study results reported for the evaluation of robotic rollators from the perspective of users,” Disabil. Rehabil.: Assist. Technol. 13(1), 31–39 (2018).Google Scholar
Werner, C., Moustris, G., Tzafestas, C. and Hauer, K., “User-oriented evaluation of a robotic rollator that provides navigation assistance in frail older adults with and without cognitive impairment,” Gerontology 64(3), 278–290 (2018). PMID: 28125298.CrossRefGoogle Scholar