SPIRA: an automatic system to support lower limb injury assessment

Carlos Bailon*, Miguel Damas, Hector Pomares, Oresti Banos

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    2 Citations (Scopus)

    Abstract

    Lower limb injuries, especially those related to the knee joint, are some of the most common and severe injuries among sport practitioners. Consequently, a growing interest in the identification of subjects with high risk of injury has emerged during last years. One of the most commonly used injury risk factor is the measurement of joint angles during the execution of dynamic movements. To that end, techniques such as human motion capture and video analysis have been widely used. However, traditional procedures to measure joint angles present certain limitations, which makes this practice not practical in common clinical settings. This work presents SPIRA, a novel 2D video analysis system directed to support practitioners during the evaluation of joint angles in functional tests. The system employs an infrared camera to track retro-reflective markers attached to the patient’s body joints and provide a real-time measurement of the joint angles in a cost-and-time-effective way. The information gathered by the sensor is processed and managed through a computer application that guides the expert during the execution of the tests and expedites the analysis of the results. In order to show the potential of the SPIRA system, a case study has been conducted, performing the analysis with the both the proposed system and a gold-standard in 2D offline video analysis. The results (ICC(ρ) = 0.996) reveal a good agreement between both tools and prove the reliability of SPIRA.

    Original languageEnglish
    Pages (from-to)2111-2123
    Number of pages13
    JournalJournal of ambient intelligence and humanized computing
    Volume10
    Issue number6
    DOIs
    Publication statusPublished - 1 Jun 2019

    Keywords

    • 2D video analysis
    • Computer vision
    • Injury risk
    • Joint angles
    • Kinect
    • Lower limb
    • Marker tracking

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