Detecting impaired movements of stroke patients in bimanual training from motion sensor data

Roni Barak Ventura, Ligao Ruan, Maurizio Porfiri

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Stroke-induced hemiparesis is associated with loss of mobility and independence, preventing survivors from participation in activities of daily living. Survivors can recover motor function of their paretic limbs by adhering to a rehabilitation regimen, consisting of repetitive, high-intensity exercises. In telerehabilitation, information and communication technologies are leveraged to deliver such physical therapy to patients’ homes. However, monitoring motor performance remotely remains a challenging task, especially in light of high variability of motor impairments among patients. In order to evaluate motor performance, therapists require the aid of technicians, who would analyze sensor data and produce meaningful metrics. The therapists would then provide patients with feedback, after a few days at best. To automate this process and offer patients real-time feedback, we propose to train machine learning algorithms that detect impaired movements. We test this approach with ten healthy participants who interact with a low-cost telerehabilitation platform we have previously developed. The platform engages users in bimanual training, where movement of the affected arm is supported by the unaffected arm, and relies on a Microsoft Kinect sensor to record user movement. We report the accuracy of a classification algorithm in distinguishing movements of simulated of disability from normal ones. This effort constitutes a significant step toward programmed assessment of upper-limb movements in authentic telerehabilitation paradigms.

Original languageEnglish (US)
Title of host publicationSoft Mechatronics and Wearable Systems
EditorsIlkwon Oh, Sang-Woo Kim, Maurizio Porfiri, Woon-Hong Yeo
ISBN (Electronic)9781510672024
StatePublished - 2024
EventSoft Mechatronics and Wearable Systems 2024 - Long Beach, United States
Duration: Mar 25 2024Mar 28 2024

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceSoft Mechatronics and Wearable Systems 2024
Country/TerritoryUnited States
CityLong Beach


  • Data science
  • Microsoft Kinect
  • motion analysis
  • stroke rehabilitation
  • telerehabilitation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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