Please enter verification code
Towards an All-Day Assignment of a Mobile Service Robot for Elderly Care Homes
American Journal of Nursing Science
Volume 9, Issue 5, October 2020, Pages: 324-332
Received: Aug. 7, 2020; Accepted: Aug. 20, 2020; Published: Sep. 3, 2020
Views 239      Downloads 120
Frank Bahrmann, Faculty of Computer Science & Mathematics, University of Applied Sciences Dresden, Dresden, Germany
Stefan Vogt, Faculty of Computer Science & Mathematics, University of Applied Sciences Dresden, Dresden, Germany
Catharina Wasic, Department of Psychiatry and Psychotherapy, University Clinic Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
Elmar Graessel, Department of Psychiatry and Psychotherapy, University Clinic Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
Hans-Joachim Boehme, Faculty of Computer Science & Mathematics, University of Applied Sciences Dresden, Dresden, Germany
Article Tools
Follow on us
Due to the demographic change in many industrial nations, the proportion of the older population is increasing. With this increase, the number of people who are dependent on outpatient or inpatient care is also rising across the board. Against this background, digital assistive systems could play an important role for improving the situation within those sectors. Therefore, the proposed novel approach describes a possible all-day use of a mobile assistive robot within an inpatient geriatric care facility, which should both relieve the staff and provide a therapeutic and entertaining contribution for the residents. The design of the components of the robot platform required for all-day use was carried out in an iterative development process. This process was started by convening a focus group, which first analyzed the requirements and then critically questioned the current status and actual benefits. Additionally, the accompanying occupational therapists and care assistants (N = 6) answered questionnaires after each of the 32 completed assignments, which were intended to draw attention to existing weaknesses and positive aspects. The main focus was to answer the question of how an assistive robot can be used meaningfully within an inpatient geriatric care facility with the means of the current state of science and whether this platform is perceived as support by the groups of people concerned. Due to the predominantly positive response to this question, the concept presented here for all-day use could be realized. Even if the response and operational capability were predominantly positive, there are still wishes from the staff and residents. These demands cannot yet be guaranteed with the current state of science to the required high degree of robustness under real world conditions. Consequently, the components identified as still in development or conceptually conceived require further research in the respective fields.
Assistive Robot, MAKS-Therapy, Nursing Home [MeSH], Feasability [MeSH], Focus Group [MeSH]
To cite this article
Frank Bahrmann, Stefan Vogt, Catharina Wasic, Elmar Graessel, Hans-Joachim Boehme, Towards an All-Day Assignment of a Mobile Service Robot for Elderly Care Homes, American Journal of Nursing Science. Vol. 9, No. 5, 2020, pp. 324-332. doi: 10.11648/j.ajns.20200905.14
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Hebesberger, D.; Koertner, T.; Gisinger, C.; Pripfl, J. and Dondrup, C. (2016). Lessons learned from the deployment of a long-term autonomous robot as companion in physical therapy for older adults with dementia a mixed methods study, 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pgs. 27-34, doi: 10.1109/HRI.2016.7451730.
Gross, H.-M.; Scheidig, A.; Debes, K.; Einhorn, E.; Eisenbach, M.; Mueller, S.; Schmiedel, T.; Trinh, T. Q.; Weinrich, C.; Wengefeld, T. and others (2017). ROREAS: robot coach for walking and orientation training in clinical post-stroke rehabilitation-prototype implementation and evaluation in field trials, Autonomous Robots 41, pgs. 679-698, doi: 10.1007/s10514-016-9552-6.
Gross, H.-M.; Schroeter, C.; Mueller, S.; Volkhardt, M.; Einhorn, E.; Bley, A.; Langner, T.; Merten, M.; Huijnen, C.; van den Heuvel, H. and others (2012). Further progress towards a home robot companion for people with mild cognitive impairment, IEEE International Conference on Systems, Man, and Cybernetics (SMC), pgs. 637-644, doi: 10.1109/ICSMC.2012.6377798.
Begum, M.; Wang, R.; Huq, R. and Mihailidis, A. (2013). Performance of daily activities by older adults with dementia: The role of an assistive robot, doi: 10.1109/ICORR.2013.6650405.
Quesenbery, W. and Brooks, K., 2010. Storytelling for user experience: Crafting stories for better design. Rosenfeld Media.
Valenti Soler, M.; Agüera-Ortiz, L.; Olazarán Rodriguez, J.; Mendoza Rebolledo, C.; Pérez Muñoz, A.; Rodriguez Pérez, I.; Osa Ruiz, E.; Barrios Sánchez, A.; Herrero Cano, V.; Carrasco Chillón, L. and others (2015). Social robots in advanced dementia, Frontiers in aging neuroscience 7, pg. 133, doi: 10.3389/fnagi.2015.00133.
He, K.; Zhang, X.; Ren, S. and Sun, J. (2016). Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pgs. 770-778, doi: 10.1109/CVPR.2016.90.
Ng, H.-W. and Winkler, S. (2014). A data-driven approach to cleaning large face datasets, IEEE international conference on image processing (ICIP), pgs. 343-347, doi: 10.1109/ICIP.2014.7025068.
Parkhi, O. M.; Vedaldi, A. and Zisserman, A. (2015). Deep face recognition, British Machine Vision Association, doi: 10.5244/C.29.41.
Vaughn, S.; Schumm, J. S. and Sinagub, J. M., (1996). Focus group interviews in education and psychology. Sage, doi: 10.4135/9781452243641.
Schulz, M.; Mack, B. and Renn, O., (2012). Fokusgruppen in der empirischen Sozialwissenschaft: Von der Konzeption bis zur Auswertung. Springer-Verlag.
Krueger, R. A., 2014. Focus groups: A practical guide for applied research. Sage publications, ISBN-10: 1483365247.
Graessel, E.; Stemmer, R.; Eichenseer, B.; Pickel, S.; Donath, C.; Kornhuber, J. and Luttenberger, K. (2011). Non-pharmacological, multicomponent group therapy in patients with degenerative dementia: a 12-month randomized, controlled trial, BMC medicine 9, pg. 129, doi: 10.1186/1741-7015-9-129.
Gräßel, E.; Behrndt, E.-M. and Straubmeier, M. (2016). Ressorcenerhaltende Therapie bei Demenz: die MAKS-Studie, Public Health Forum, pgs. 118-120, doi: 10.1515/pubhef-2016-1014.
Graessel, E., 2019. MAKS-m: Psychosoziale Intervention zur Therapie kognitiver Beeinträchtigungen (digital manual, web access:, last accessed 08/07/2020 2:00pm).
Luttenberger, K.; Donath, C.; Uter, W. and Graessel, E. (2012). Effects of multimodal nondrug therapy on dementia symptoms and need for care in nursing home residents with degenerative dementia: A randomized-controlled study with 6-month follow-up, Journal of the American Geriatrics Society 60, pgs. 830-840, doi: 10.1111/j.1532-5415.2012.03938.x.
Luttenberger, K.; Hofner, B. and Graessel, E. (2012). Are the effects of a non-drug multimodal activation therapy of dementia sustainable? Follow-up study 10 months after completion of a randomised controlled trial, BMC neurology 12, pg. 151, doi: 10.1186/1471-2377-12-151.
Straubmeier, M.; Behrndt, E.-M.; Seidl, H.; Özbe, D.; Luttenberger, K. and Gräßel, E. (2017). Nichtpharmakologische Therapie bei Menschen mit kognitven Einschränkungen, Dtsch Arztbl Int 114, pgs. 815-821.
Lischke, F.; Bahrmann, F.; Hellbach, S. and Böhme, H.-J. (2017) RoNiSCo: Robotic Night Shift Companion, Workshop New Challenges in Neural Computation 2017.
Hess, W.; Kohler, D.; Rapp, H. and Andor, D. (2016). Real-time loop closure in 2D LIDAR SLAM, IEEE International Conference on Robotics and Automation (ICRA), pgs. 1271-1278, doi: 10.1109/ICRA.2016.7487258.
Bahrmann, F.; Hellbach, S. and Böhme, H.-J. (2016). A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization, Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics, 2016, doi: 10.2316/P.2016.847-021.
Hernández, C.; Asin, R. and Baier, J. A. (2015). Reusing previously found A* paths for fast goal-directed navigation in dynamic terrain, Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015, doi: 10.5555/2887007.2887168.
Berti, H.; Sappa, A. and Agamennoni, O. E. (2008). Improved dynamic window approach by using Lyapunov stability criteria, Latin American applied research 38.
Bahrmann, F.; Hellbach, S.; Keil, S. and Böhme, H.-J. (2014). Understanding Dynamic Environments with Fuzzy Perception, International Conference on Neural Information Processing, pgs. 553-562, doi: 10.1007/978-3-319-12643-2_67.
Poschmann, P.; Donner, M.; Bahrmann, F.; Rudolph, M.; Fonfara, J.; Hellbach, S. and Böhme, H.-J. (2012). Wizard of Oz revisited: Researching on a tour guide robot while being faced with the public, IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, pgs. 701-706, doi: 10.1109/ROMAN.2012.6343833.
Moosbrugger, H. and Kelava, A., (2012). Testtheorie und Fragebogenkonstruktion. Springer-Verlag Berlin Heidelberg.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186