On-Body Smartphone Position Detection with Position Transition Correction Based on the Hand State
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Smartphone users tend to store their devices at manifold on-body positions: in their trouser pocket, in their backpack, on the table, or simply in their hands. Depending on the position, it might be required to adapt the ringtone and notification type to enhance their perception. To do so, the smartphone needs to be able to automatically detect the device’s position.
In this paper, we present an approach to detect the on-body position of the smartphone based on the smartphone features such as accelerometer data. In addition, we propose a position transition correction (PTC) algorithm to improve the position detection. The PTC assumes that each position transition involves the position “hand” as the user has to hold the phone into their hands to take them out of one position and place them another.
We gathered data from 20 participants and ran different classification methods. The KStar classifier achieved an accuracy of 81.97%. By applying the PTC we were able to correct about 50% of the errors on a simulated transition sequence, leading to an accuracy of almost 90%.
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