Automatic Human Movement Assessment with Switching Linear Dynamic System: Motion Segmentation and Motor Performance

Roberto De Souza Baptista, Antonio P.L. Bó, Mitsuhiro Hayashibe

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


Performance assessment of human movement is critical in diagnosis and motor-control rehabilitation. Recent developments in portable sensor technology enable clinicians to measure spatiotemporal aspects to aid in the neurological assessment. However, the extraction of quantitative information from such measurements is usually done manually through visual inspection. This paper presents a novel framework for automatic human movement assessment that executes segmentation and motor performance parameter extraction in time-series of measurements from a sequence of human movements. We use the elements of a Switching Linear Dynamic System model as building blocks to translate formal definitions and procedures from human movement analysis. Our approach provides a method for users with no expertise in signal processing to create models for movements using labeled dataset and later use it for automatic assessment. We validated our framework on preliminary tests involving six healthy adult subjects that executed common movements in functional tests and rehabilitation exercise sessions, such as sit-to-stand and lateral elevation of the arms and five elderly subjects, two of which with limited mobility, that executed the sit-to-stand movement. The proposed method worked on random motion sequences for the dual purpose of movement segmentation (accuracy of 72%-100%) and motor performance assessment (mean error of 0%-12%).

Original languageEnglish
Article number7513405
Pages (from-to)628-640
Number of pages13
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number6
Publication statusPublished - 2017 Jun


  • Human movement assessment
  • motor performance
  • segmentation
  • switching lineardynamic systems


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