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Human Digital Twin for Fitness Management
Open accessAbstract
Our research work describes a team of human Digital Twins (DTs), each tracking fitness-related measurements describing an athlete's behavior in consecutive days (e.g. food income, activity, sleep). After collecting enough measurements, the DT firstly predicts the physical twin performance during training and, in case of non-optimal result, it suggests modifications in the athlete's behavior. The athlete's team is integrated into SmartFit, a software framework for supporting trainers and coaches in monitoring and manage athletes' fitness activity and results. Through IoT sensors embedded in wearable devices and applications for manual logging (e.g. mood, food income), SmartFit continuously captures measurements, initially treated as the dynamic data describing the current physical twins' status. Dynamic data allows adapting each DT's status and triggering the DT's predictions and suggestions. The analyzed measurements are stored as the historical data, further processed by the DT to update (increase) its knowledge and ability to provide reliable predictions. Results show that, thanks to the team of DTs, SmartFit computes trustable predictions of the physical twins' conditions and produces understandable suggestions which can be used by trainers to trigger optimization actions in the athletes' behavior. Though applied in the sport context, SmartFit can be easily adapted to other monitoring tasks.
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“A challenge from this type of DT is that the amount of data and the processing time needed to build individual models constantly increase.”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [52]
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- molnar, 2019, Interpretable Machine Learning A Guide for Making Black Box Models Explainable
Cited by
Sorted by citations
Digital Twin Technology Challenges and Applications: A Comprehensive Review
Botín-Sanabria, D. M., Mihaita, A.-S., Peimbert-García, R. E., Ramírez-Moreno, M. A., Ramírez-Mendoza, R. A., & Lozoya-Santos, J. D. J. (2022) · Remote Sensing · doi:10.3390/rs14061335 · Cited by 878
Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E., & Bakiras, S. (2021) · IEEE Access · doi:10.1109/access.2021.3060863 · Cited by 616
“[133] focuses on fitness-related measurements of athletes.”
BackgroundPham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020) · IEEE Access · doi:10.1109/access.2020.3009328 · Cited by 413
Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021) · IEEE Access · doi:10.1109/access.2021.3051315 · Cited by 376
“Thus, the outlined classification-oriented frameworks are evaluated on classifiers based on logistic regression [55], [136], [153], [158], decision trees [46], [80], [122], [140], [150], [155], [159], gradient boosted decision trees [147], support vector machines [131], [138], [146], random forests [81], [86], [142]–[…”
BackgroundMethodA probabilistic graphical model foundation for enabling predictive digital twins at scale
Kapteyn, M. G., Pretorius, J. V. R., & Willcox, K. E. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00069-0 · Cited by 363
Metadata
- Authors
- Barricelli, B. R., Casiraghi, E., Gliozzo, J., Petrini, A., & Valtolina, S.
- Affiliations
- University of Brescia (IT)
- University of Milan (IT)
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 26637-26664
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3004825562
- Cited by
- 259
- References
- 114
- Citations by year
- 2026: 24 · 2025: 58 · 2024: 60 · 2023: 36 · 2022: 47 · 2021: 26 · 2020: 8
- FWCI
- 18.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Institutional Research Information System (Università degli Studi di Brescia) (submittedVersion, other-oa)
- Funders
- Bando Sostegno alla Ricerca 2019 through the University of Milan
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- human digital twin, wearable sensors, Internet of Things, dynamic data, dietary monitoring, sleep monitoring, activity monitoring
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