A METHODOLOGY FOR TRACKING THE SCAPULAUNDER DYNAMIC CONDITIONS

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Abstract

In the diagnosis and analysis of shoulder instability a precise determination of the location and orientation of the Glenohumeral joint is important. A better understanding of shoulder kinematics and kinetics will help clinicians and therapists in the diagnosis and treatment of shoulder pathologies. To-date, non-invasive skin-based methods are often either restricted to quasi-static measurements or are inaccurate during dynamic assessments at high humeral elevations as a result of soft skin artefact.Tracking the orientation of the scapula is difficult because it is surrounded by soft tissues, is held mainly by muscles and has only one direct point of attachment to the thorax. Instability of the glenohumeral joint generates poor functionality of the shoulder labrum and capsule as well as in the muscle and connective tissue structures that surround the shoulder. As the clinical phenomenon of shoulder instability is extremely complex, one of the priorities for the specialist in avoiding a faulty diagnosis is to recognise, identify and classify shoulder pathologies such as muscle patterning instability in the early stages of the investigation.A two stage methodology for non-invasive tracking of the scapula under dynamic conditions is presented in this work. The methodology provides scapula location by combining data from two surface mounted sensors using a regression-type equation formulated from quasi-static trials undertaken using a scapula locator and three IMUs (first stage). In the second stage, the least square fit is used to improve the scapular orientation by utilising data from only two IMUs (humerus and scapula) under dynamic conditions. Accuracy was assessed in an animal study by comparing results with those from a bone based method during quasi static and dynamic tests. Tests were also undertaken to investigate the errors induced by the soft tissue artefact in surface based scapula location measurement. In dynamic trials the methodology proved more accurate in determining scapula location than a standard skin-based approach, and showed that the greatest contribution to soft tissue artefact was from the epidermal, dermal and subcutaneous tissue layers as opposed to the muscle layer. We confirmed that, in cases where subjects have relatively small amounts of soft tissue surrounding the scapula, surface based methods could provide reasonable accuracy. Our methodology utilised subject-specific data to formulate a regression equation, and can be used to provide accurate, non-invasive tracking of the scapula under dynamic conditions in subjects regardless of individual body morphology. After the methodology validation, study tests were undertaken in a case study in order to estimate the scapula orientation under dynamic conditions in a human without symptoms of any shoulder pathologies and in one participant diagnosed with shoulder instability due to muscle patterning.The two stage methodology is proven to work in a healthy human participant in dynamic tests, in a person with no suspicion of shoulder instability. This methodology allows the error reduction generated by the soft tissues surrounded the scapula. The work presented here can be used as a framework for developing diagnosis protocols by using modern technology.

Layman's Abstract

In the diagnosis and analysis of shoulder instability a precise determination of the location and orientation of the Glenohumeral joint is important. A better understanding of shoulder kinematics and kinetics will help clinicians and therapists in the diagnosis and treatment of shoulder pathologies. To-date, non-invasive skin-based methods are often either restricted to quasi-static measurements or are inaccurate during dynamic assessments at high humeral elevations as a result of soft skin artefact.Tracking the orientation of the scapula is difficult because it is surrounded by soft tissues, is held mainly by muscles and has only one direct point of attachment to the thorax. Instability of the glenohumeral joint generates poor functionality of the shoulder labrum and capsule as well as in the muscle and connective tissue structures that surround the shoulder. As the clinical phenomenon of shoulder instability is extremely complex, one of the priorities for the specialist in avoiding a faulty diagnosis is to recognise, identify and classify shoulder pathologies such as muscle patterning instability in the early stages of the investigation.A two stage methodology for non-invasive tracking of the scapula under dynamic conditions is presented in this work. The methodology provides scapula location by combining data from two surface mounted sensors using a regression-type equation formulated from quasi-static trials undertaken using a scapula locator and three IMUs (first stage). In the second stage, the least square fit is used to improve the scapular orientation by utilising data from only two IMUs (humerus and scapula) under dynamic conditions. Accuracy was assessed in an animal study by comparing results with those from a bone based method during quasi static and dynamic tests. Tests were also undertaken to investigate the errors induced by the soft tissue artefact in surface based scapula location measurement. In dynamic trials the methodology proved more accurate in determining scapula location than a standard skin-based approach, and showed that the greatest contribution to soft tissue artefact was from the epidermal, dermal and subcutaneous tissue layers as opposed to the muscle layer. We confirmed that, in cases where subjects have relatively small amounts of soft tissue surrounding the scapula, surface based methods could provide reasonable accuracy. Our methodology utilised subject-specific data to formulate a regression equation, and can be used to provide accurate, non-invasive tracking of the scapula under dynamic conditions in subjects regardless of individual body morphology. After the methodology validation, study tests were undertaken in a case study in order to estimate the scapula orientation under dynamic conditions in a human without symptoms of any shoulder pathologies and in one participant diagnosed with shoulder instability due to muscle patterning.The two stage methodology is proven to work in a healthy human participant in dynamic tests, in a person with no suspicion of shoulder instability. This methodology allows the error reduction generated by the soft tissues surrounded the scapula. The work presented here can be used as a framework for developing diagnosis protocols by using modern technology.

Keyword(s)

inertial; scapula; sensors

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