Taking a lesson from patients' recovery strategies to optimize training during robot-aided rehabilitation

Roberto Colombo, Irma Sterpi, Alessandra Mazzone, Carmen Delconte, Fabrizio Pisano

Research output: Contribution to journalArticlepeer-review


In robot-assisted neurorehabilitation, matching the task difficulty level to the patient's needs and abilities, both initially and as the relearning process progresses, can enhance the effectiveness of training and improve patients' motivation and outcome. This study presents a Progressive Task Regulation algorithm implemented in a robot for upper limb rehabilitation. It evaluates the patient's performance during training through the computation of robot-measured parameters, and automatically changes the features of the reaching movements, adapting the difficulty level of the motor task to the patient's abilities. In particular, it can select different types of assistance (time-triggered, activity-triggered, and negative assistance) and implement varied therapy practice to promote generalization processes. The algorithm was tuned by assessing the performance data obtained in 22 chronic stroke patients who underwent robotic rehabilitation, in which the difficulty level of the task was manually adjusted by the therapist. Thus, we could verify the patient's recovery strategies and implement task transition rules to match both the patient's and therapist's behavior. In addition, the algorithm was tested in a sample of five chronic stroke patients. The findings show good agreement with the therapist decisions so indicating that it could be useful for the implementation of training protocols allowing individualized and gradual treatment of upper limb disabilities in patients after stroke. The application of this algorithm during robot-assisted therapy should allow an easier management of the different motor tasks administered during training, thereby facilitating the therapist's activity in the treatment of different pathologic conditions of the neuromuscular system.

Original languageEnglish
Article number6202779
Pages (from-to)276-285
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number3
Publication statusPublished - 2012


  • Motor recovery
  • neurorehabilitation
  • robotic therapy
  • stroke
  • training optimization

ASJC Scopus subject areas

  • Computer Science Applications
  • Biomedical Engineering
  • Medicine(all)
  • Neuroscience(all)


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