Skip to main content Accessibility help

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

  • Ricardo C. de Mello (a1), Mario F. Jimenez (a2), Moises R. N. Ribeiro (a1), Rodrigo Laiola Guimarães (a3) and Anselmo Frizera-Neto (a1)...


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.


Corresponding author

*Corresponding author. E-mail:


Hide All
1.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), 575591 (2014).
2.Liu, Y., Peng, Y., Wang, B., Yao, S. and Liu, Z., “Review on cyber-physical systems,IEEE/CAA J. Autom. Sinica 4(1), 2740 (2017).
3.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, 16 (2017).
4.Haque, S. A., Aziz, S. M. and Rahman, M., “Review of cyber-physical system in healthcare,” Int. J. Distrib. Sens. Netw. 217415 (2014).
5.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), 539552 (2016).
6.Hu, G., Tay, W. and Wen, Y., “Cloud robotics: Architecture, challenges and applicationsIEEE Netw. 26(3), 2128 (2012).
7.Wan, J., Tang, S., Yan, H., Li, D., Wang, S. and Vasilakos, A. V., “Cloud robotics: Current status and open issues,IEEE Access 4, 27972807 (2016).
8.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), 398409 (2015).
9.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, 157173 (2017).
10.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, 113 (2017).
11.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), 4048 (2016).
12.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. 16.
13.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. 8596.
14.Pons, J. L., Wearable Robots: Biomechatronic Exoskeletons (John Wiley & Sons, Ltd, Chichester, UK, 2008).
15.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, 156184 (2017).
16.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), 7590 (2012).
17.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. 13.
18.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).
19.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), 343360 (2014).
20.Walsh, C., “Human-in-the-loop development of soft wearable robots,Nat. Rev. Mater. 3, 7880 (2018).
21.Hossain, M. S., “Cloud-supported cyber-physical localization framework for patients monitoring,IEEE Syst. J. 11(1), 118127 (2017).
22.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), 8895 (2017).
23.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), 825845 (2016).
24.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, 100109 (2015).
25.Tsuji, T., Kaneko, T. and Sakaino, S., “Motion matching in rehabilitation databases with force and position information,IEEE Trans. Ind. Electron. 63(3), 19351942 (2016).
26.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), 12631276 (2017).
27.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. 14.
28.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), 258268 (2017).
29.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), 105114 (2016).
30.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).
31.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.
32.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. 407423.
33.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), 933943 (2016).
34.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. 8186.
35.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, 152166 (2018).
36.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), 1219 (2014).
37.Pocovi, G. et al., “Achieving ultra-reliable low-latency communications: Challenges and envisioned system enhancements,IEEE Netw. 32(2), 815 (2018).
38.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).
39.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, 123 (2016).
40.Streijl, R. C., Winkler, S. and Hands, D. S., “Mean opinion score (MOS) revisited: Methods and applications, limitations and alternatives,Multimedia Syst. 22(2), 213227 (2016).
41.Talman, L. S. et al., “Longitudinal study of vision and retinal nerve fiber layer thickness in multiple sclerosis,Ann. Neurol. 67(6), 749760 (2010).
42.Alexander, N. B. and Goldberg, A., “Gait disorders: Search for multiple causes,” Cleveland Clin. J. Med. 72(7), 586600 (2005).
43.Zhang, X., Han, Q. and Yu, X., “Survey on recent advances in networked control systems,” IEEE Trans. Ind. Inform. 12(5), 17401752 (2016).
44.Dorf, R. C., The Engineering Handbook, 2nd edn (CRC PRESS, New York, 2004).
45.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. 16.
46.Fiedler, M., Hossfeld, T. and Tran-Gia, P., “A generic quantitative relationship between quality of experience and quality of service,IEEE Netw. 24(2), 3641 (2010).
47.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. 16.
48.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. 16.
49.Lazar, J., Feng, J. H. and Hochheiser, H., Research Methods in Human-Computer Interaction, 2 edn (Morgan Kaufmann, Boston, 2017).
50.McDonald, J. H., Handbook of Biological Statistics, 3rd edn (Sparky House Publishing, Baltimore, 2014).
51.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. 3740.
52.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), 3139 (2018).
53.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), 278290 (2018). PMID: 28125298.


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

  • Ricardo C. de Mello (a1), Mario F. Jimenez (a2), Moises R. N. Ribeiro (a1), Rodrigo Laiola Guimarães (a3) and Anselmo Frizera-Neto (a1)...


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed