Evaluating Performance of the Lunge Exercise with Multiple and Individual Inertial Measurement Units

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Title: Evaluating Performance of the Lunge Exercise with Multiple and Individual Inertial Measurement Units
Authors: Whelan, Darragh
O'Reilly, Martin
Ward, Tomás
Delahunt, Eamonn
Caulfield, Brian
Permanent link: http://hdl.handle.net/10197/7876
Date: 19-May-2016
Abstract: The lunge is an important component of lower limb rehabilitation, strengthening and injury risk screening. Completing the movement incorrectly alters muscle activation and increases stress on knee, hip and ankle joints. This study sought to investigate whether IMUs are capable of discriminating between correct and incorrect performance of the lunge. Eighty volunteers (57 males, 23 females, age: 24.68± 4.91 years, height: 1.75± 0.094m, body mass: 76.01±13.29kg) were fitted with five IMUs positioned on the lumbar spine, thighs and shanks. They then performed the lunge exercise with correct form and 11 specific deviations from acceptable form. Features were extracted from the labelled sensor data and used to train and evaluate random-forests classifiers. The system achieved 83% accuracy, 62% sensitivity and 90% specificity in binary classification with a single sensor placed on the right thigh and 90% accuracy, 80% sensitivity and 92% specificity using five IMUs. This multi-sensor set up can detect specific deviations with 70% accuracy. These results indicate that a single IMU has the potential to differentiate between correct and incorrect lunge form and using multiple IMUs adds the possibility of identifying specific deviations a user is making when completing the lunge.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2016 ACM
Keywords: Personal sensing;Exercise;Classification;Inertial Measurement Units;Lunge;Functional screening tools;Biofeedback;Rehabilitation
Language: en
Status of Item: Peer reviewed
Conference Details: Pervasive Health 2016: 10th EAI International Conference on Pervasive Computing Technologies for Healthcare, Cancun, Mexico, 16-19 May 2016
Appears in Collections:Public Health, Physiotherapy and Sports Science Research Collection
Insight Research Collection
Institute for Sport & Health Research Collection

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