Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution
Abstract
:1. Introduction
- Where are you (location)?
- How can you travel from point A to B (route navigation)?
- What are you doing?
- What is the environment around you?
- What is your current situation?
- What can be done for your benefit?
2. Background of Smartphone Mobility Sensing
3. Geo-Context Computing Based on Hidden Markov Models
3.1. Problem Formulation and Solutions of Hidden Markov Models
3.2. Radio Signals and MEMS Sensors Integration for Smartphone Positioning
No. | Combinations of MDI | Sensors and methods used to obtain MDI | |
---|---|---|---|
Distance | Heading | ||
1 | Measured distance & heading | accelerometers | compass |
accumulated step lengths | directly measured | ||
2 | Measured distance | accelerometers | none |
accumulated step lengths | unknown | ||
3 | Measured heading & assumed speed | none | compass |
a constant speed model of 1 m/s | directly measured | ||
4 | Assumed speed | none | none |
a constant speed model of 1 m/s | unknown |
3.3. Human Motion State Recognition
Area Types | Motion t + 1 | Sitting | Standing | Walking | Running | Falling | Turning |
---|---|---|---|---|---|---|---|
Motion t | |||||||
Working room | Sitting | 0.9913 | 0.0087 | 0.000002 | 0.000002 | 0.000002 | 0.000002 |
Standing | 0.3103 | 0.1524 | 0.4202 | 0.000002 | 0.000002 | 0.1172 | |
Walking | 0.0008 | 0.0615 | 0.8451 | 0.000017 | 0.000007 | 0.0925 | |
Running | 0.000002 | 0.0256 | 0.1039 | 0.8063 | 0.000007 | 0.0642 | |
Falling | 0.9925 | 0.0075 | 0.000002 | 0.000002 | 0.000002 | 0.000002 | |
Turning | 0.000002 | 0.2140 | 0.7705 | 0.000004 | 0.000006 | 0.0155 | |
Coffee room | Sitting | 0.8664 | 0.1336 | 0.000002 | 0.000002 | 0.000002 | 0.000002 |
Standing | 0.3258 | 0.2631 | 0.3938 | 0.000002 | 0.000002 | 0.0173 | |
Walking | 0.0008 | 0.0821 | 0.8522 | 0.000022 | 0.000008 | 0.0648 | |
Running | 0.000002 | 0.0369 | 0.1497 | 0.791438 | 0.000009 | 0.0219 | |
Falling | 0.9942 | 0.0058 | 0.000002 | 0.000002 | 0.000002 | 0.000002 | |
Turning | 0.000002 | 0.1180 | 0.8750 | 0.000004 | 0.000006 | 0.0069 | |
Intersection | Sitting | 0.6419 | 0.3581 | 0.000002 | 0.000002 | 0.000002 | 0.000002 |
Standing | 0.000002 | 0.1246 | 0.4010 | 0.0048 | 0.000002 | 0.4696 | |
Walking | 0.000002 | 0.1741 | 0.3454 | 0.0117 | 0.000002 | 0.4687 | |
Running | 0.000002 | 0.0677 | 0.2287 | 0.3070 | 0.000002 | 0.3966 | |
Falling | 0.9900 | 0.0100 | 0.000002 | 0.000002 | 0.000002 | 0.000002 | |
Turning | 0.000002 | 0.0033 | 0.8736 | 0.1195 | 0.000002 | 0.0035 | |
Staircase | Sitting | 0.6154 | 0.3846 | 0.000002 | 0.000002 | 0.000002 | 0.000002 |
Standing | 0.000002 | 0.0812 | 0.5412 | 0.2916 | 0.000002 | 0.0859 | |
Walking | 0.000002 | 0.0846 | 0.6725 | 0.0725 | 0.0862 | 0.0843 | |
Running | 0.000002 | 0.0405 | 0.2657 | 0.4470 | 0.1681 | 0.0785 | |
Falling | 0.9900 | 0.0100 | 0.000002 | 0.000002 | 0.000002 | 0.000002 | |
Turning | 0.000002 | 0.0073 | 0.6433 | 0.1345 | 0.1772 | 0.0377 | |
Generic area | Sitting | 0.7712 | 0.2288 | 0.000002 | 0.000002 | 0.000002 | 0.000002 |
Standing | 0.1079 | 0.3682 | 0.3194 | 0.1136 | 0.000002 | 0.0909 | |
Walking | 0.0682 | 0.1775 | 0.5934 | 0.1359 | 0.000002 | 0.0250 | |
Running | 0.000002 | 0.1863 | 0.3590 | 0.4489 | 0.000002 | 0.0058 | |
Falling | 0.9900 | 0.0100 | 0.000002 | 0.000002 | 0.000002 | 0.000002 | |
Turning | 0.0888 | 0.2166 | 0.6289 | 0.0657 | 0.000002 | 0.000002 |
Motion t + 1 | Sitting | Standing | Walking | Running | Falling | Turning |
---|---|---|---|---|---|---|
Motion t | ||||||
Sitting | 0.8664 | 0.1336 | 0.000002 | 0.000002 | 0.000002 | 0.000002 |
Standing | 0.3259 | 0.2637 | 0.3932 | 0.000002 | 0.000002 | 0.0172 |
Walking | 0.0008 | 0.0821 | 0.8522 | 0.000022 | 0.000008 | 0.0648 |
Running | 0.000002 | 0.0369 | 0.1497 | 0.7914 | 0.000009 | 0.0219 |
Falling | 0.9942 | 0.0058 | 0.000002 | 0.000002 | 0.000002 | 0.000002 |
Turning | 0.000002 | 0.1180 | 0.8750 | 0.000004 | 0.000006 | 0.0070 |
3.4. Geo-Context Inference and Interpretation
4. Conclusions and Outlook
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Liu, J.; Zhu, L.; Wang, Y.; Liang, X.; Hyyppä, J.; Chu, T.; Liu, K.; Chen, R. Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution. Micromachines 2015, 6, 699-717. https://doi.org/10.3390/mi6060699
Liu J, Zhu L, Wang Y, Liang X, Hyyppä J, Chu T, Liu K, Chen R. Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution. Micromachines. 2015; 6(6):699-717. https://doi.org/10.3390/mi6060699
Chicago/Turabian StyleLiu, Jingbin, Lingli Zhu, Yunsheng Wang, Xinlian Liang, Juha Hyyppä, Tianxing Chu, Keqiang Liu, and Ruizhi Chen. 2015. "Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution" Micromachines 6, no. 6: 699-717. https://doi.org/10.3390/mi6060699