Clinically feasible artificial intelligence solutions for rehabilitation via motion analysis

LUNEX invites you to submit your paper to the AIHA* focus session:

Clinically feasible artificial intelligence solutions for rehabilitation via motion analysis

* International Workshop on Artificial Intelligence for Healthcare Applications (AIHA 2024) – December 1, 2024

Submission instructions are available at this link 


The integration of articificial intelligence (AI) solutions into rehabilitation protocols represents a fascinating opportunity to enhance patient outcomes and streamline therapeutic processes. By leveraging motion analysis techniques, AI can offer solutions which are clinically feasible as characterised by a quick, simple and inexpensive set-up to help precise and individualised rehabilitation needs.

AI-driven motion analysis enables precise and objective assessment of movement patterns, identifying weaknesses and dysfunctions with high accuracy. Such data-driven approaches allow clinical professionals to tailor rehabilitation programs optimising treatment efficacy and recovery.

AI algorithms can continuously adapt rehabilitation protocols based on real-time feedback, providing personalised progression as well. The dynamic adjustment capability enhances the efficiency of rehabilitation and fosters patient engagement and motivation through interactive and adaptive interventions.

AI-powered motion analysis systems can facilitate remote monitoring and tele-rehabilitation. Such possibility extends the access to specialised care beyond traditional clinical settingsand provides a feasible framework for the continuity of care. This is particularly beneficial for those living in remote areas, promoting adherence to rehabilitation protocols and facilitating timely interventions.


The main aim fo the proposed AIHA focused workshop is to exhibit the advancements in AI-based solutions to improve assessment and recovery in rehabilitation with a proven feasible approach. Such approaches should be driven by real-world generated data and lead to real world clinical solutions.


  • Markerless motion analysis in rehabilitation
  • Exercise gaming
  • Telerehabilitation and telemonitoring
  • Digitalised solutions for objective assessment of motor, sensory and cognitive performance
  • Virtual, augmented and mixed reality in rehabilitation
  • AI-driven biofeedback approaches in rehabilitation
  • Data-driven injury prevention assessment and interventions
  • Data-driven approaches to reduce injury recurrence
  • Data-driven impairment stratification and early recognition
  • Natural language processing solutions in rehabilitation
  • Generative AI in rehabilitation


Paper Submission: July 26, 2024
Notification of Acceptance: September 6, 2024
Full Paper Submission: September 27, 2024

Submission Details

Contributions may be submitted in one of the following forms:

  • Full research papers (12-15 pages)
  • Short research papers (6-11)
  • Simple Abstracts (1-4 pages)

Full research papers should present original unpublished work. Simple abstracts should describe original work in progress or a summary of a full paper. At least one author of each accepted work has to register for the conference, attend the conference, and present his work.

Accepted papers will be included in the ICPR 2024 Conference Proceedings, which will be published by Springer in the Lecture Notes in Computer Science series (LNCS).

Accepted abstracts describing unpublished work will be considered for publication of extended versions (at least 6 pages) in the same volume.


Nicole Dalia Cilia,  Kore University of Enna, Italy

Francesco Fontanella,  University of Cassino and Southern Lazio, Italy

Claudio Marrocco,  University of Cassino and Southern Lazio, Italy


Prof.  Francesco Fontanella,


50, avenue du Parc des Sports / 4671 Differdange / Luxembourg

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