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Binary Classification of Running Fatigue using a Single Inertial Measurement Unit


Cailbhe Doherty, Martin O'Reilly, Darragh Whelan, Vallely Farrell, Lawrence Clark, V Longo, M Gilchrist, Brian Caulfield

Publication Type: 
Refereed Conference Meeting Proceeding
The popularity of running has increased in recent years. A rise in the incidence of running-related overuse musculoskeletal injuries has occurred parallel to this. This study investigates the capability of using data from a single inertial measurement unit (IMU) to differentiate between running form in a non-fatigued and fatigued state. Data was captured from an IMU placed on the lumbar spine, right shank and left shank in 21 recreational runners (10 male, 11 female) during separate 400m running trials. The trials were performed prior to and following a fatiguing protocol. Following stride segmentation, IMU signal features were extracted from the labelled (non-fatigued vs fatigued) sensor data and used to train both a Global and Personalised classifier for each individual IMU location. A single IMU on the Lumbar spine displayed 75% accuracy, 73% sensitivity and 77% specificity when using a Global Classifier. A single IMU on the Right Shank displayed 100% accuracy, 100% sensitivity and 100% specificity when using a Personalised Classifier. These results indicate that a single IMU has the potential to differentiate between non-fatigued and fatigued running states with a high level of accuracy.
Conference Name: 
Wearable and Implantable Body Sensor Networks (BSN), 2017
Digital Object Identifer (DOI): 
Publication Date: 
Research Group: 
National University of Ireland, Dublin (UCD)
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