Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking

Abstract

Abstract-Due to the dependency of our daily lives on smartphones, the states of the device have impact on the quality of services offered through a smartphone. In this article, we focus on the carrying states of the device while the user is walking, in which 17 states, e.g., in the front-left trouser pocket, calling phone in the right hand, in a backpack are subjects to recognition based on supervised learning with accelerometer-derived features. A large-scale data collection from 70 persons with three walking speeds allows reliable evaluation regarding suitable features and classifiers model, the feature selection method, robustness of localization against unknown person, and effect of walking speed in training a classifier. Person-independent evaluation shows that average F-measures of 17 class classification and merged 9 class classification were 0.823 and 0.913, respectively.

Unknown On-Body Device Position Detection Based on Ensemble Novelty Detection

“…Generally, in ensemble classification, accuracy increases with the number of weak classifiers and converges at a certain level. (6) Thus, the number of weak detectors was changed to 20 and 50 to confirm this tendency in ensemble novelty detection. At the same time, the feasibility of the ensemble novelty detection and the proposed T max estimation method for various numbers of weak detectors was evaluated.…”

“…Positions supported in the work Fujinami et al (6) neck (hanging), chest pocket, jacket pocket, trouser front/back pockets, bag (backpack, handbag, and shoulder bag), hand (calling, watching the screen in the portrait direction, and swinging during walking)…”

“…Three datasets were collected from smartphones carried in a wide variety of ways, such as in trouser pockets, in the hand, or in the handbag (Table 2). Datasets A and B (2,6) were collected in the authors' laboratory, whereas dataset C (23) is a publicly available dataset. All datasets are composed of three-axis accelerometer signals.…”

“…(2)(3)(4) On-body device position recognition is gaining attention in ubiquitous computing communities that use multiclass classification techniques and where the number of positions is fixed before use. (2,(5)(6)(7)(8) However, in reality, a variety of on-body device positions are available, and a user may carry the device in a position not originally intended for the system, i.e., an unknown position. In this case, the classifier performs incorrect recognition because the current position of the device is not in the range of recognition.…”

New Position Candidate Identification via Clustering toward an Extensible On-Body Smartphone Localization System

“…In the last 15 years, the problem has been addressed by a number of researchers using machine learning techniques, in which inertial sensors are often utilized due to the fact that the moving patterns of the terminal differ by storing positions and recognition features; additionally, recognition models have been investigated [ 4 , 5 , 6 ]. However, is such investigations, it was assumed that the positions to be recognized, i.e., classes, are fixed and that a recognition component classifies input data into one of predefined classes.…”