Human Digital Twin for Fitness Management

Barbara Rita Barricelli, Elena Casiraghi, Jessica Gliozzo, Alessandro Petrini, Stefano Valtolina

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)26637-26664
Number of pages28
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - Jan 1 2020

Keywords

  • Counterfactual explanations
  • Digital twins
  • Internet of Things
  • Machine learning
  • Smart health
  • Sociotechnical design
  • Wearables

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Fingerprint Dive into the research topics of 'Human Digital Twin for Fitness Management'. Together they form a unique fingerprint.

  • Cite this

    Barricelli, B. R., Casiraghi, E., Gliozzo, J., Petrini, A., & Valtolina, S. (2020). Human Digital Twin for Fitness Management. IEEE Access, 8, 26637-26664. https://doi.org/10.1109/ACCESS.2020.2971576