Component-level tuning of kinematic features from composite therapist impressions of movement quality

Vinay Venkataraman, Pavan Turaga, Michael Baran, Nicole Lehrer, Tingfang Du, Long Cheng, Thanassis Rikakis, Steven L. Wolf

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In this paper, we propose a general framework for tuning component-level kinematic features using therapists overall impressions ofmovement quality, in the context of a home-based adaptive mixed reality rehabilitation (HAMRR) system. We propose a linear combination of nonlinear kinematic features to model wrist movement, and propose an approach to learn feature thresholds and weights using high-level labels of overallmovement quality provided by a therapist. The kinematic features are chosen such that they correlate with the quality of wrist movements to clinical assessment scores. Further, the proposed features are designed to be reliably extracted from an inexpensive and portable motion capture system using a single reflective marker on the wrist. Using a dataset collected from ten stroke survivors, we demonstrate that the framework can be reliably used for movement quality assessment in HAMRR systems. The system is currently being deployed for large-scale evaluations, and will represent an increasingly important application area of motion capture and activity analysis.

Original languageEnglish (US)
Article number6967759
Pages (from-to)143-152
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
StatePublished - Jan 2016


  • Kinematic features
  • Movement quality assessment
  • Stroke rehabilitation

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Electrical and Electronic Engineering
  • Computer Science Applications


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