Do you hear what I hear? Using acoustic probing to detect smartphone locations

Abstract

Many context-aware smartphone applications depend on specific conditions for gathering data, e.g., specific phone locations or orientations. As a result, the significant overhead of keeping all this information in mind is imposed on their users. Besides averting the interest of potential application users, these requirements defeat one of the main purposes of these mobile data collection, namely simplifying life through mobile sensing applications. This is not a problem that solely affects the users, but the developers of the applications alike. As even the most diligent users often do not manage to follow the strict data collection guidelines at all times, errors in the collected data may ultimately lead to the provision of wrong services and thus to degraded application quality. In this paper, we thus present a solution to determine the location of a phone in order to support context-aware applications. It offers the possibility to detect the position of the phone with an accuracy of 97 %, as well as being able to correlate it with the type of the location of the user. Our system can be used to improve existing mobile sensing applications by facilitating various services that depend on the phone location, e.g., seamlessly adapting the ringtone volume or setting a phone's flight mode

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

“…In addition to the three axes, i.e., x, y and z, we introduce the magnitude of the three-axes signals (m) as the forth dimension for series s as shown in (6).…”

On-Body Smartphone Localization with an Accelerometer

“…One static solution is to set an audio level sufficiently high, so that a user could perceive it at any storing position; however, it is annoying in the vicinity when the smartphone is hanging from the neck, because a user can notice it with an even lower audio level at the position. Therefore, the audio volume can be adjusted at a minimum level by the information of the storing position, so that only the user can receive the notification, as Diaconita et al intended [11,12]. Other functionalities, such as a display component and a keypad, can be controlled to avoid power drain due to an invisible display, as well as accidental inputs when a smartphone is inside a bag or pocket [13].…”

“…The research direction is on the type of device that is actually realized or intended to be utilized in the future as a wearable device [23][24][25] or a smartphone [11][12][13]17,22,[26][27][28][29]. The type of device relates to the selection of target positions.…”

“…We equally collect data from four types of bags, which is a unique aspect of our work. In existing work, the type of bag is not clearly defined [27] or limited to a backpack [11] and messenger bag [29]. Therefore, the trained classifier has a bias on the collected types of bags.…”

“…For example, a bag with cellular fabric might pass light inside, which may have similar light components even with active sensing. Active sensing methods were also utilized in [11,12] to regulate the environment that sensors capture. However, as pointed out by Jung and Choi [32], a vibration motor is a relatively high power component in a smartphone.…”