Title :
A Data-Driven Approach to Kinematic Analysis in Running Using Wearable Technology
Author :
Strohrmann, Christina ; Rossi, Mirco ; Arnrich, Bert ; Tröster, Gerhard
Author_Institution :
Wearable Comput. Lab., ETH Zurich, Zurich, Switzerland
Abstract :
Millions of people run. Movement scientists investigate the relationship of running kinematics to fatigue, injury, or running economy mainly using optical motion capture. It was found that running kinematics are highly individual and often cannot be summarized by single variables. We thus present a data-driven analysis of running technique using wearable technology, combining statistical features and machine learning techniques, which allows to identify non-linear, complex relationships. Wearable technology enables running kinematic analysis to a broad mass in unconstrained environments. 20 runners wore 12 sensor units during two experiments: an all out test and a fatiguing run. We used a Support Vector Machine (SVM) to distinguish skill level groups and achieved an accuracy of 76.92% with an acceleration sensor on the upper body. Sensor positions were ranked according to the movement change with fatigue using a feature selection. This ranking was consistent with visual annotations of a movement scientist. We propose a quantitative measure of movement change using a principal component analysis (PCA) and found an average correlation of 0.8369 for all runners with their perceived rating of fatigue.
Keywords :
acceleration measurement; body sensor networks; fatigue; gait analysis; injuries; kinematics; learning (artificial intelligence); principal component analysis; support vector machines; wearable computers; acceleration sensor; data-driven approach; fatigue; feature selection; injury; kinematic analysis; machine learning techniques; optical motion capture; principal component analysis; skill level; statistical features; support vector machine; upper body sensor; visual annotations; wearable technology; Acceleration; Accuracy; Fatigue; Feature extraction; Kinematics; Sensor phenomena and characterization; machine learning; measurement; wearable sensors;
Conference_Titel :
Wearable and Implantable Body Sensor Networks (BSN), 2012 Ninth International Conference on
Conference_Location :
London
Print_ISBN :
978-1-4673-1393-3