Predicting MIRA Patients’ Performance Using Virtual Rehabilitation Programme by Decision Tree Modelling

  • Nurezayana Zainal
  • Ismail Ahmed Al-Qasem Al-Hadi
  • Safwan M. Ghaleb
  • Hafiz Hussain
  • Waidah IsmailEmail author
  • Ali Y. Aldailamy
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 295)


An effective rehabilitation procedure is required to successfully manage the disabilities caused by diseases such as stroke, spinal cord injury (SCI), traumatic brain injury (TBI), and cerebral palsy (CP). In this regard, Medical Interactive Recovery Assistant (MIRA) platform proffers virtual rehabilitation through exergames. Virtual reality therapy (VRT) has recently gained attention for upper limb rehabilitation due to its positive impacts on patients’ performance. VRT is a modern interactive application that integrates computer software with hardware devices to create an interactive virtual environment when playing different types of games and exercises (exergames). The output of playing the game generates statistical features (parameters) reflecting the patients’ performance. However, physiotherapists who manage the input settings of exergames according to specific movements cannot easily predict the future performance of the patients based on their observations. Thus, this study proposes a decision tree model to predict MIRA patients’ future performance for three difficulty levels (easy, medium and hard) with respect to their previous/last session records. Patients’ data in the previous/last session are used to determine the prediction values according to the proposed model. This helps physiotherapists to monitor and also predict their patients’ progress using certain prediction values. Results prove the efficiency of the proposed decision tree-based statistical tool for prediction in medical monitoring applications.


Rehabilitation Virtual reality therapy Exergame Decision tree 



This work is supported by the International Grant USIM/INT-NEWTON/FST/IHRAM/053000/41616 under Newton-Ungku Omar Fund.


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© Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Nurezayana Zainal
    • 1
  • Ismail Ahmed Al-Qasem Al-Hadi
    • 1
  • Safwan M. Ghaleb
    • 1
  • Hafiz Hussain
    • 2
  • Waidah Ismail
    • 1
    Email author
  • Ali Y. Aldailamy
    • 3
  1. 1.Faculty of Science and TechnologyUniversiti Sains Islam MalaysiaNegeri SembilanMalaysia
  2. 2.PERKESO Rehabilitation Centre Bandar HijauMelakaMalaysia
  3. 3.Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSelangorMalaysia

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