Authors:
Cecilia Signorelli
;
Nicola Lopomo
;
Simone Bignozzi
;
Stefano Zaffagnini
and
Maurilio Marcacci
Affiliation:
Biomechanics Lab, Rizzoli Orthopaedic Institute, Italy
Keyword(s):
Pivot-Shift test, ACL reconstruction, Acceleration, Pearson’s correlation coefficient, Template.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Anterior cruciate ligament injury produces a pathologic kinematics of the limb that can lead to the evidence of a pivot-shift (PS) phenomenon. PS-test, specifically performed to highlighted this knee dynamic instability, is however difficult to quantify. From a clinical point of view is therefore mandatory to find a set of parameters able to quantitatively characterize PS phenomenon, thus distinguishing between pathologic and healthy knees. This study proposed a methodology able to automatically quantify PS phenomenon, analysing the signal recorded by means of a tri-axial accelerometer while executing PS-test itself. A signal template, which reproduced the 3D acceleration average trend while PS phenomenon occurs, was passed along the signal in order to recognise the presence of similar patterns. The recognition of the signal interesting share was based on the calculation of the Pearson’s correlation coefficient between the template and the corresponding part of the windowed signal. T
he data acquisition concerning to the first 35 patients was used to testing the template; in this analysis we considered both the data relative to pathologic and healthy knee, as well as pre- and post-anaesthesia data, in order to evaluate the influence of active muscular resistance. The methodology followed had assured a recognition of PS repetitions with an accuracy of 96.7%, a sensitivity of 81.9% and a specificity of 99.3%; therefore can be considered a valid and easily computable method for the automatic screening of the acceleration signal during PS test. In the future this method will be uptake in order to quantify the possibility to discern between pathologic and healthy knee.
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