title: A single vs. multi-sensor approach to enhanced detection of smartphone placement.
abstract: In this paper, the authors evaluate the ability to detect on-body device placement of smartphones. A feasibility study is undertaken with N=5 participants to identify nine key locations, including in the hand, thigh and backpack, using a multitude of commonly available smartphone sensors. Sensors examined include the accelerometer, magnetometer, gyroscope, pressure and light sensors. Each sensor is examined independently, to identify the potential contributions it can offer, before a fused approach, using all sensors is adopted. A total of 139 features are generated from these sensors, and used to train five machine learning algorithms, i.e. C4.5, CART, Naïve Bayes, Multilayer Perceptrons, and Support Vector Machines. Ten-fold cross validation is used to validate these models, achieving classification results as high as 99%.
authors.forename: John J
authors.initials: JJ
authors.lastname: Guiry
authors.forename: Chris J
authors.initials: CJ
authors.lastname: Karr
authors.forename: Pepijn
authors.initials: P
authors.lastname: van de Ven
authors.forename: John
authors.initials: J
authors.lastname: Nelson
authors.forename: Mark
authors.initials: M
authors.lastname: Begale
mesh_terms: D061725:Accelerometry; D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D040421:Cell Phone; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D016571:Neural Networks, Computer; D060388:Support Vector Machine