title: Fitness activity classification by using multiclass support vector machines on head-worn sensors.
abstract: Fitness activity classification on wearable devices can provide activity-specific information and generate more accurate performance metrics. Recently, optical head-mounted displays (OHMD) like Google Glass, Sony SmartEyeglass and Recon Jet have emerged. This paper presents a novel method to classify fitness activities using head-worn accelerometer, barometric pressure sensor and GPS, with comparisons to other common mounting locations on the body. Using multiclass SVM on head-worn sensors, we obtained an average F-score of 96.66% for classifying standing, walking, running, ascending/descending stairs and cycling. The best sensor location combinations were found to be on the ankle plus another upper body location. Using three or more sensors did not show a notable improvement over the best two-sensor combinations.
authors.forename: Darrell
authors.initials: D
authors.lastname: Loh
authors.forename: Tien J
authors.initials: TJ
authors.lastname: Lee
authors.forename: Shaghayegh
authors.initials: S
authors.lastname: Zihajehzadeh
authors.forename: Reynald
authors.initials: R
authors.lastname: Hoskinson
authors.forename: Edward J
authors.initials: EJ
authors.lastname: Park
mesh_terms: D015444:Exercise; D006801:Humans; D011187:Posture; D060388:Support Vector Machine