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. 2015 Oct 5;15(10):25474-506.
doi: 10.3390/s151025474.

Analysis of Movement, Orientation and Rotation-Based Sensing for Phone Placement Recognition

Ozlem Durmaz Incel  1
Affiliations

Analysis of Movement, Orientation and Rotation-Based Sensing for Phone Placement Recognition

Ozlem Durmaz Incel. Sensors (Basel). .

Abstract

Phone placement, i.e., where the phone is carried/stored, is an important source of information for context-aware applications. Extracting information from the integrated smart phone sensors, such as motion, light and proximity, is a common technique for phone placement detection. In this paper, the efficiency of an accelerometer-only solution is explored, and it is investigated whether the phone position can be detected with high accuracy by analyzing the movement, orientation and rotation changes. The impact of these changes on the performance is analyzed individually and both in combination to explore which features are more efficient, whether they should be fused and, if yes, how they should be fused. Using three different datasets, collected from 35 people from eight different positions, the performance of different classification algorithms is explored. It is shown that while utilizing only motion information can achieve accuracies around 70%, this ratio increases up to 85% by utilizing information also from orientation and rotation changes. The performance of an accelerometer-only solution is compared to solutions where linear acceleration, gyroscope and magnetic field sensors are used, and it is shown that the accelerometer-only solution performs as well as utilizing other sensing information. Hence, it is not necessary to use extra sensing information where battery power consumption may increase. Additionally, I explore the impact of the performed activities on position recognition and show that the accelerometer-only solution can achieve 80% recognition accuracy with stationary activities where movement data are very limited. Finally, other phone placement problems, such as in-pocket and on-body detections, are also investigated, and higher accuracies, ranging from 88% to 93%, are reported, with an accelerometer-only solution.

Keywords: accelerometer; classification; gravity; gyroscope; linear acceleration; magnetometer; mobile phone sensing; motion sensors; phone placement recognition.

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Figures

Figure 1
Figure 1
Pitch features. (a) Dataset 1; (b) Dataset 2; (c) Dataset 3.
Figure 2
Figure 2
Roll features. (a) Dataset 1; (b) Dataset 2; (c) Dataset 3.
Figure 3
Figure 3
Standard deviation, y axis features. (a) Dataset 1; (b) Dataset 2; (c) Dataset 3.
Figure 4
Figure 4
Position recognition performance with only motion features.
Figure 5
Figure 5
Position recognition performance with motion and orientation features.
Figure 6
Figure 6
Position recognition performance with all features.
Figure 7
Figure 7
Different feature combinations (M: motion; O: orientation; R: rotation).
Figure 8
Figure 8
Accuracy when the activity information is available, individual datasets.
Figure 9
Figure 9
Accuracy when the activity information is available, datasets combined.
See this image and copyright information in PMC

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