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Smartphone Motion Mode Recognition^{ †}

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^{†}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Strategy

#### 2.2. Smartphone Sensors

#### 2.3. Feature Extraction

#### 2.3.1. Statistical Features

- Mean. The mean of a signal.
- Median. The median is the middle value separating the higher half of a data sample from the lower half.
- Standard deviation. The square root of the variance (measure of the spread of data around the mean).
- Average absolute difference. Measure of the spread of data around its mean, taking the absolute difference between values and the mean.
- Interquartile range (iqr). It is the difference between 75th percentile and 25th percentile of the data where percentile of $Y\%$ is the value separating the higher 100-$Y\%$ of a data sample from the lower $Y\%$ of the data.
- Skewness. A measure of the asymmetry of the probability distribution of a signal.
- Kurtosis. A measure of the ‘tailedness’ of the probability distribution of a signal.
- Signal energy. The sum of the squares of signal values.
- Signal magnitude area. The sum of absolute values of a signal.
- Max. The maximum value in the window of the signal.
- Min. The minimum value in the window of the signal.
- Amplitude. The absolute difference between the maximum value and minimum value.

#### 2.3.2. Time-Domain Features

- Number of peaks. The count of the number of maximum points within the desired window of the signal where the maximum points should be above a predefined value and located after w samples from the last maximum point.

#### 2.3.3. Cross Sensor Features

- Gyro-Accelerometer Correlation. Is the cross-correlation coefficient between the gyro and acceleration sensors.
- Gyro-Accelerometer Maximum. The multiplication result of the gyro and acceleration maximum values.
- Gyro-Accelerometer Standard Deviation. The multiplication result of the gyro and acceleration standard deviation values.

## 3. Experimental Results and Discussion

#### 3.1. Experimental Setup

#### 3.2. The Learning Process

## 4. Conclusions

## Conflicts of Interest

## References

- Rainer, M. Indoor Positioning Technologies. Ph.D. Thesis, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland, 2012. [Google Scholar]
- Groves, P.D. Principles of GNSS, Inertial and Multisensor Integrated Navigation Systems, 2nd ed.; Artech House: Norwood, MA, USA, 2013. [Google Scholar]
- Cliff, C.; Randell, D.; Muller, H.L. Personal position measurement using dead reckoning. In Proceedings of the Seventh IEEE International Symposium on Wearable Computers, White Plains, NY, USA, 21–23 October 2003; pp. 166–173. [Google Scholar]
- Beauregard, S.; Haas, H. Pedestrian Dead Reckoning: A Basis for Personal Positioning. In Proceedings of the 3rd Workshop on Positioning, Navigation and Communication, Hannover, Germany, 16 March 2006. [Google Scholar]
- Qian, L.; Ma, J.; Ying, R.; Liu, P.; Pei, P. An improved indoor localization method using smartphone inertial sensors. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Montbeliard-Belfort, France, 28–31 October 2013; pp. 1–7. [Google Scholar]
- Elhoushi, M.; Georgy, J.; Noureldin, A.; Korenberg, M. Online motion mode recognition for portable navigation using low-cost sensors. J. Inst. Navig.
**2015**, 62, 273–290. [Google Scholar] [CrossRef] - Elhoushi, M.; Georgy, J.; Noureldin, A.; Korenberg, M. A Survey on approaches of motion mode recognition using sensors. IEEE Trans. Intell. Transp. Syst.
**2017**, 18, 1662–1686. [Google Scholar] [CrossRef] - Jekeli, C. Inertial Navigation Systems with Geodetic Applications; Walter de Gruyter: Berlin, Germany, 2000. [Google Scholar]
- Titterton, D.H.; Weston, J.L. Strapdown Inertial Navigation Technology, 2nd ed.; The American Institute of Aeronautics and Astronautics and the institution of electrical engineers: Reston, VA, USA, 2004. [Google Scholar]
- Kempe, V. Inertial MEMS Principles and Practice; Cambridge University Press: Cambridge, UK, 2011. [Google Scholar]
- Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning, Data Mining, Inference and Prediction, 2nd ed.; Springer: Berlin, Germany, 2009. [Google Scholar]
- Raschka, S. Python Machine Learning; Packt Publishing: Birmingham, UK, 2016. [Google Scholar]

Mode | Number of Windows Training | Number of Windows Test |
---|---|---|

38,492 | 6320 | |

Swing | 60,812 | 17,523 |

Talking | 27,697 | 7348 |

Texting | 27,434 | 13,655 |

Classifier | Accuracy (%) |
---|---|

MLP | 86.2 |

SVM | 84.1 |

KNN | 82.7 |

RF | 86.7 |

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**MDPI and ACS Style**

Klein, I.; Solaz, Y.; Ohayon, G.
Smartphone Motion Mode Recognition. *Proceedings* **2018**, *2*, 145.
https://doi.org/10.3390/ecsa-4-04929

**AMA Style**

Klein I, Solaz Y, Ohayon G.
Smartphone Motion Mode Recognition. *Proceedings*. 2018; 2(3):145.
https://doi.org/10.3390/ecsa-4-04929

**Chicago/Turabian Style**

Klein, Itzik, Yuval Solaz, and Guy Ohayon.
2018. "Smartphone Motion Mode Recognition" *Proceedings* 2, no. 3: 145.
https://doi.org/10.3390/ecsa-4-04929