Human Digital Twin-based interactive dashboards for informal caregivers of stroke patients
Open accessAbstract
Informal caregivers (i.e., family and friends) play a vital role in the care of stroke patients during at-home rehabilitation but often face significant challenges due to lacking medical experience and insufficient professional assistance. Human Digital Twins, i.e., digital representations of the patient, could help address this challenge by monitoring the patient’s condition via sensors and communicating it to the caregivers through corresponding user interfaces. However, existing user interfaces in stroke rehabilitation primarily focus on the interaction between patients or therapists. This paper proposes a novel dashboard designed explicitly for informal caregivers in upper-limb stroke rehabilitation. The dashboard supports easy-to-understand communication of complex patient data and provides tools to involve informal caregivers in the patient’s therapy process. Feedback from semi-structured interviews with informal caregivers validates the critical need for corresponding user interfaces but also raises questions concerning the design of visualization and data privacy.
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Existing stroke recovery monitoring systems were designed to be used by healthcare providers or caregivers, not by both healthcare providers and patients for communication during inpatient stroke rehabilitation.
“Furthermore, these systems were designed to be used by healthcare providers [46, 47] or caregivers [28] for monitoring patient's recovery progress. They were not developed to be used by healthcare providers and patients for communication during inpatient stroke rehabilitation.”
From A Data Visualization Tool for Patients and Healthcare Providers to Communicate during Inpatient Stroke Rehabilitation (2024) · cites this paper as [28]
Several technological and visualization tools were developed to monitor stroke recovery progress, particularly monitoring the patient's upper limb movement during stroke recovery.
“Several technological and visualization tools were developed to monitor stroke recovery progress, particularly monitoring the patient's upper limb movement during stroke recovery [28, 46, 47].”
From A Data Visualization Tool for Patients and Healthcare Providers to Communicate during Inpatient Stroke Rehabilitation (2024) · cites this paper as [28]
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Cites
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083 · see citing works
Large Language Models Encode Clinical Knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.13138 · Cited by 4,083 · see citing works
The Uncanny Valley [From the Field]
Mori, M., MacDorman, K., & Kageki, N. (2012) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2012.2192811 · Cited by 3,542 · see citing works
A survey on digital twin: definitions, characteristics, applications, and design implications
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019) · IEEE Access · doi:10.1109/access.2019.2953499 · Cited by 1,480 · see citing works
Wolff, J. L., Spillman, B. C., Freedman, V. A., & Kasper, J. D. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2015.7664 · Cited by 585 · see citing works
Emotion Recognition from Physiological Signal Analysis: A Review
Egger, M., Ley, M., & Hanke, S. (2019) · Electronic Notes in Theoretical Computer Science · doi:10.1016/j.entcs.2019.04.009 · Cited by 568 · see citing works
European Cardiovascular Disease Statistics 2017
Wilkins, E., Wilson, L. R., Wickramasinghe, K. K., Bhatnagar, P., Leal, J. L., Luengo-Fernández, R., Burns, R., Rayner, M., & Townsend, N. P. (2017) · Bristol Research (University of Bristol) · Cited by 516 · see citing works
Multifactorial Examination of Caregiver Burden in a National Sample of Family and Unpaid Caregivers
Riffin, C., Van Ness, P. H., Wolff, J. L., & Fried, T. (2019) · Journal of the American Geriatrics Society · doi:10.1111/jgs.15664 · Cited by 238 · see citing works
Cardio Twin: A Digital Twin of the human heart running on the edge
Velazquez, R., Díaz, R. G., & Saddik, A. E. (2019) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2019.8802162 · Cited by 170 · see citing works
Improving Stroke Caregiver Readiness for Transition From Inpatient Rehabilitation to Home
Lutz, B. J., Young, M. E., Creasy, K. R., Martz, C., Eisenbrandt, L. L., Brunny, J. N., & Cook, C. L. (2016) · The Gerontologist · doi:10.1093/geront/gnw135 · Cited by 162 · see citing works
Sahal, R., Alsamhi, S. H., & Brown, K. N. (2022) · Italian National Conference on Sensors · doi:10.3390/s22155918 · Cited by 145 · see citing works
Welsh, D., Morrissey, K., Foley, S., McNaney, R., Salis, C., McCarthy, J., & Vines, J. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173949 · Cited by 94 · see citing works
Yousif, H. A., Zakaria, A., Rahim, N. A., Salleh, A. F., Mahmood, M., Alfarhan, K. A., Kamarudin, L. M., Zakaria, S. M. M. S., Hasan, A. M., & Hussain, M. K. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/705/1/012010 · Cited by 90 · see citing works
Digital Twin Coaching for Physical Activities: A Survey
Díaz, R. G., Yu, Q., Ding, Y., Laamarti, F., & Saddik, A. E. (2020) · Italian National Conference on Sensors · doi:10.3390/s20205936 · Cited by 81 · see citing works
Neuromusculoskeletal Modeling-Based Prostheses for Recovery After Spinal Cord Injury
Pizzolato, C., Saxby, D. J., Palipana, D. B., Diamond, L. E., Barrett, R., Teng, Y. D., & Lloyd, D. G. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00097 · Cited by 60 · see citing works
ArmSleeve: A Patient Monitoring System to Support Occupational Therapists in Stroke Rehabilitation
Ploderer, B., Fong, J., Withana, A., Klaic, M., Nair, S., Crocher, V., Vetere, F., & Nanayakkara, S. (2016) · Proceedings of the 2016 ACM Conference on Designing Interactive Systems · doi:10.1145/2901790.2901799 · Cited by 46 · see citing works
Interpersonal relationship challenges among stroke survivors and family caregivers
McCarthy, M. J., Lyons, K. S., Schellinger, J., Stapleton, K., & Bakas, T. (2020) · Social Work in Health Care · doi:10.1080/00981389.2020.1714827 · Cited by 45 · see citing works
Tang, S., Chen, L., Barsotti, M., Hu, L., Li, Y., Wu, X., Bai, L., Frisoli, A., & Hou, W. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00099 · Cited by 44 · see citing works
A Digital Twin Platform for Diagnostics and Rehabilitation of Multiple Sclerosis
Petrova‐Antonova, D., Spasov, I., Krasteva, I., Manova, I., & Ilieva, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58799-4_37 · Cited by 29 · see citing works
The effects of anthropomorphic avatars vs. non-anthropomorphic avatars in a jumping game
Kao, D. (2019) · International Conference on the Foundations of Digital Games (FDG) · doi:10.1145/3337722.3341829 · Cited by 27 · see citing works
User-Avatar Relationships in Various Contexts
Praetorius, A. S., Krautmacher, L., Tullius, G., & Curio, C. (2021) · Mensch und Computer (MuC) · doi:10.1145/3473856.3474007 · Cited by 15 · see citing works
Modeling for Rapid Systems Prototyping: Hospital Situational Awareness System Design
Shaked, A. (2021) · Systems · doi:10.3390/systems9010012 · Cited by 13 · see citing works
Real-time visual analytics for in-home medical rehabilitation of stroke patient—systematic review
Boumrah, M., Garbaya, S., & Radgui, A. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02493-w · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Luis F Rivera, Miguel Jiménez, Prashanti Angara, Norha M Villegas, Gabriel Tamura, and Hausi A Müller. 2019. Towards Continuous Monitoring in Per- sonalized Healthcare through Digital Twins. Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, 329-335.
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Johnson, Z., & Saikia, M. J. (2024) · Bioengineering · doi:10.3390/bioengineering11060606 · Cited by 74 · see citing works
Chaparro-Cárdenas, S. L., Ramirez-Bautista, J. A., Terven, J. R., Esparza, D. M. C., Romero-González, J.-A., Ramírez-Pedraza, A., & Chávez‐Urbiola, E. A. (2025) · Healthcare · doi:10.3390/healthcare13141763 · Cited by 70 · see citing works
Roopa, M. S., & Venugopal, K. R. (2025) · IEEE Access · doi:10.1109/access.2025.3547991 · Cited by 56 · see citing works
Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review
Borna, S., Maniaci, M. J., Haider, C. R., Gomez-Cabello, C. A., Pressman, S. M., Haider, S. A., Demaerschalk, B. M., Cowart, J. B., & Forte, A. J. (2024) · Bioengineering · doi:10.3390/bioengineering11050483 · Cited by 40 · see citing works
Lauer-Schmaltz, M. W., Cash, P. J., Hansen, J. P., & Das, N. (2024) · IEEE Access · doi:10.1109/access.2024.3508029 · Cited by 41 · see citing works
Self-citationWentzel, A., Kamel, S., Zhang, X., Canahuate, G. M., Fuller, C. D., & Marai, G. E. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456160 · Cited by 35 · see citing works
ETHICA: Designing Human Digital Twins—A Systematic Review and Proposed Methodology
Lauer-Schmaltz, M. W., Cash, P. J., & Rivera, D. G. T. (2024) · IEEE Access · doi:10.1109/access.2024.3416517 · Cited by 33 · see citing works
Self-citationRamesh, S. H., Ouskine, A., Khorasani, E., Ebrahimipour, M., Finestone, H., Chan, A. D. C., & Rajabiyazdi, F. (2024) · Graphics Interface · doi:10.1145/3670947.3670978 · Cited by 7 · see citing works
García‐Rudolph, A., Wright, M. A., Teixidó-Font, C., Sánchez-Carrión, R., Cedersund, G., & Opisso, E. (2025) · Topics in Stroke Rehabilitation · doi:10.1080/10749357.2025.2584031 · Cited by 4 · see citing works
Seah, C., Ng, T. P., Teo, S. Y., Lee, J.-J., Wu, Y., Zheng, C., Foong, P. S., & Cho, J. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3799455 · Cited by 1 · see citing works
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Yu, Z., Hirpara, H. J., Zhang, X., Canahuate, G. M., Wentzel, A., Wang, Y., Floricel, C., Kamel, S., Mohamed, A., Naser, M. A., et al. (2025) · medRxiv · doi:10.1101/2025.10.10.25337766 · Cited by 1 · see citing works
van Vroonhoven, W., James, L., & Gorp, P. M. E. V. (2025) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3733155.3734890
Fazio, R. D., de Biase, M. S., Marzuillo, P., Tirelli, P., Marulli, F., Marrone, S., & Verde, L. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.10.026
Mandischer, N., Atanasyan, A., Dahmen, U., Schluse, M., Roßmann, J., & Mikelsons, L. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11343658
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Liu, P., & Chen, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30424-7_9
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Yin, M., Luo, R., Hu, S., Lin, H., Sun, H., & Sun, Y. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03673-0
Li, Y., Bernhaupt, R., & Lallemand, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-22934-2_16
Li, Y., Lallemand, C., & Bernhaupt, R. (2026) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3829807.3829813
- Authors
- Lauer-Schmaltz, M. W., Kerim, I., Hansen, J. P., Gulyás, G. M., & Andersen, H. B.
- Affiliations
- Technical University of Denmark (DK)
- ORCID
- Venue
- Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments
- Publisher
- ACM
- Published
- 2023-07-05
- Pages
- 215-221
- OpenAlex
- W4385730019
- Cited by
- 18
- References
- 20
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 6
- FWCI
- 19.69
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) (publishedVersion, cc-by-nd)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- Funders
- The Bevica Foundation
- Horizon 2020 Framework Programme (87176)
- Topics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Demography › Technology Use by Older Adults
- Keywords
- human digital twin, informal caregivers, stroke rehabilitation, upper limb rehabilitation, interactive dashboard, patient monitoring, sensor data visualization, data privacy, home-based rehabilitation
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