On-Body Smartphone Localization with an Accelerometer

Abstract

A user of a smartphone may feel convenient, happy, safe, etc., if his/her smartphone works smartly based on his/her context or the context of the device. In this article, we deal with the position of a smartphone on the body and carrying items like bags as the context of a device. The storing position of a smartphone impacts the performance of the notification to a user, as well as the measurement of embedded sensors, which plays an important role in a device's functionality control, accurate activity recognition and reliable environmental sensing. In this article, nine storing positions, including four types of bags, are subject to recognition using an accelerometer on a smartphone. In total, 63 features are selected as a set of features among 182 systematically-defined features, which can characterize and discriminate the motion of a smartphone terminal during walking. As a result of leave-one-subject-out cross-validation, an accuracy of 0.801 for the nine-class classification is shown, while an accuracy of 0.859 is obtained against five classes, which merges the subclasses of trouser pockets and bags. We also show the basic performance evaluation to select the proper window size and classifier. Furthermore, the analysis of the contributive features is presented.

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

“…Incel [14] shows an extensive study on acceleration-based phone localization, in which recognition features are proposed that represent the movement, rotation and orientation of devices during diverse activities of a person such as walking, sitting and biking. Fujinami proposed 63 classifier-independent features for 9 on-body phone positions including bags during walking, which selected based on as what are more predictive of classes and less correlated with each other [7]. Shi et al [25], Alanezi et al [1], and Incel [14] utilized a gyroscope in combination with an accelerometer.…”

“…By contrast, Leave-One-Subject-Out (LOSO) cross validation is carried out by testing a dataset from a particular person with a classifier that is trained without a dataset from the person. So, LOSO-CV is regarded as a fairer and practical test method, and recently getting attention [1], [7], [14], [30]. To validate the generalization of a classification model, the number of subjects is important, i.e., small number of subjects fail in capturing the characteristic of the population.…”

“…A research direction is on the type of a device which is actually realized or intended to be utilized in the future as wearable devices [18], [21], [29] or a smartphone [1], [5]- [7], [12], [14], [22], [25], [30]. The type of a device relates to the selection of target positions.…”

“…On-body position sensing is getting attention to researchers in machine learning and ubiquitous computing communities [7], [25], [29]. A research direction is on the type of a device which is actually realized or intended to be utilized in the future as wearable devices [18], [21], [29] or a smartphone [1], [5]- [7], [12], [14], [22], [25], [30].…”

“…This suggests that a context, on-body device position, has great potentials in improving the usability of a smartphone and the quality of sensordependent services, facilitating human-human communication, the reduction of unnecessary energy consumption, etc. [7]. Note that the position is not an exact 3D coordinate, but the names of the parts of the body, clothes and items to carry the device during walking such as "inside a chest pocket", "inside a bag", and "calling (attaching to the ear)".…”