Quantitative evaluation of performance during robot-assisted treatment

Elisabetta Peri, Emilia Biffi, Cristina Maghini, F. Servodio Iammarrone, Chiara Gagliardi, Chiara Germiniasi, A. Pedrocchi, Anna Carla Turconi, Gianluigi Reni

Research output: Contribution to journalArticlepeer-review


Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Methodologies, Models and Algorithms for Patients Rehabilitation”. Objectives: The great potential of robots in extracting quantitative and meaningful data is not always exploited in clinical practice. The aim of the present work is to describe a simple parameter to assess the performance of subjects during upper limb robotic training exploiting data automatically recorded by the robot, with no additional effort for patients and clinicians. Methods: Fourteen children affected by cerebral palsy (CP) performed a training with Armeo®Spring. Each session was evaluated with P, a simple parameter that depends on the overall performance recorded, and median and interquartile values were computed to perform a group analysis. Results: Median (interquartile) values of P significantly increased from 0.27 (0.21) at T0 to 0.55 (0.27) at T1. This improvement was functionally validated by a significant increase of the Melbourne Assessment of Unilateral Upper Limb Function. Conclusions: The parameter described here was able to show variations in performance over time and enabled a quantitative evaluation of motion abilities in a way that is reliable with respect to a well-known clinical scale.

Original languageEnglish
Pages (from-to)84-88
Number of pages5
JournalMethods of Information in Medicine
Issue number1
Publication statusPublished - 2016


  • Assessment
  • Cerebral palsy
  • Rehabilitation
  • Robotics
  • Upper limb

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management
  • Advanced and Specialised Nursing


Dive into the research topics of 'Quantitative evaluation of performance during robot-assisted treatment'. Together they form a unique fingerprint.

Cite this