Agent's Motor Performance: an Index of Difficulty-based Model

Andrea Lucchese, Giovanni Mummolo, Salvatore Digiesi, Carlotta Mummolo

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Motor performance of operators is extensively studied in physical tasks where movement's accuracy and control are required. In the present work, authors propose a new formulation of the Index of Difficulty (ID) to capture the performance of an agent (e.g., human, robot, co-bot) in executing a given (reference) motor task characterized by a nominal trajectory, spatially constrained along the entire path. The novelty of the model relies on considering the behaviour of an observed agent (e.g., movement variability, average trajectory), and evaluating its performance compared to a reference agent, whose behaviour corresponds to the best execution of the reference motor task. The novel ID can capture differences in performance due to age, and therefore be applied as an indicator to choose the proper agent for the specific physical task (i.e., resource allocation), as well as to evaluate the effectiveness of the human-robot collaboration in work environments. Further research will be focused on extending the model to three-dimensional motor tasks and validating it through real case studies.

Original languageEnglish (US)
Pages (from-to)347-352
Number of pages6
Issue number10
StatePublished - 2022
Externally publishedYes
Event10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022 - Nantes, France
Duration: Jun 22 2022Jun 24 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


  • Accuracy
  • Agent
  • Index of Difficulty
  • Motor Performance
  • Resource Allocation


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