An Adaptive Heading Estimation Method based on Holding Styles Recognition Using Smartphone Sensors

Authors

  • Khanh Nguyen-Huu Dalat University
  • Ninh Duong-Bao Hunan University
  • Luong Nguyen Thi Dalat University
  • Le Do Thi Dalat University
  • Thuy Huynh Thi Thu Dalat University
  • Seon-Woo Lee Hallym University

DOI:

https://doi.org/10.21512/commit.v18i1.9196

Keywords:

Adaptive Heading Estimation Method, Holding Styles Recognition, Smartphone Sensors

Abstract

Pedestrian Dead Reckoning (PDR), which comes with many sensors integrated into widely available smartphones, is known as one of the most popular indoor positioning techniques. Sensors such as accelerometers, gyroscopes, and magnetometers are used to determine three important components in PDR: step detection, step length estimation, and heading estimation. Among them, the last component is the most challenging since a small heading error accumulates to produce a very large positioning error, especially when the pedestrian holds the smartphone in unconstrained styles such as swinging the phone freely along the pedestrian’s walking direction or putting the phone into the pants’ front pockets. The research proposes an adaptive heading estimation method to deal with heading errors caused by smartphone holding styles. The novelties are described as follows. Firstly, the proposed method attempts to classify the four basic smartphone holding styles using a machine learning algorithm based on simple features of acceleration values to give pedestrians more freedom during the walking period. Secondly, the proposed method adaptively combines the two heading estimation methods, which are calculated from the integrated sensors, to determine the walking direction for different smartphone holding styles. The experimental results show that the proposed heading estimation method achieves average heading errors of less than 30 degrees when testing in two different walking paths with the smartphone held in dynamic styles. It helps to reduce the heading errors by more than 15% compared to previous heading estimation methods.

Dimensions

Plum Analytics

Author Biographies

Khanh Nguyen-Huu, Dalat University

Department of Electronics & Telecommunications

Ninh Duong-Bao, Hunan University

College of Computer Science and Electronic Engineering

Luong Nguyen Thi, Dalat University

Faculty of Information Technology

Le Do Thi, Dalat University

Department of Electronics & Telecommunications

Thuy Huynh Thi Thu, Dalat University

Department of Electronics & Telecommunications

Seon-Woo Lee, Hallym University

Division of Software

References

S. Tomaˇziˇc, D. Dovˇzan, and I. ˇ Skrjanc, “Confidence-interval-fuzzy-model-based indoor localization,” IEEE Transactions on Industrial Electronics, vol. 66, no. 3, pp. 2015–2024, 2018.

S. Hildebrandt, T. Kubota, H. A. Sani, and U. Surahman, “Indoor air quality and health in newly constructed apartments in developing countries: A case study of Surabaya, Indonesia,” Atmosphere, vol. 10, no. 4, pp. 1–22, 2019.

P. Kumar, A. Singh, and R. Singh, “Comprehensive health risk assessment of microbial indoor air quality in microenvironments,” PloS ONE, vol. 17, no. 2, pp. 1–16, 2022.

L. Zhao, H. Zhou, R. Chen, Z. Shen., “Efficient monitoring and adaptive control of indoor air quality based on IoT technology and fuzzy inference,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–14, 2022.

B. Huang, J. Liu, W. Sun, and F. Yang, “A robust indoor positioning method based on bluetooth low energy with separate channel information,” Sensors, vol. 19, no. 16, pp. 1–19, 2019.

P. Bencak, D. Hercog, and T. Lerher, “Indoor positioning system based on bluetooth low energy technology and a nature-inspired optimization algorithm,” Electronics, vol. 11, no. 3, pp. 1–27, 2022.

O. P. Babalola and V. Balyan, “WiFi fingerprinting indoor localization based on dynamic mode decomposition feature selection with Hidden Markov model,” Sensors, vol. 21, no. 20, pp. 1–13, 2021.

J. Zheng, K. Li, and X. Zhang, “Wi-Fi fingerprintbased indoor localization method via standard particle swarm optimization,” Sensors, vol. 22, no. 13, pp. 1–16, 2022.

N. Duong-Bao, J. He, L. N. Thi, K. Nguyen-Huu, and S.-W. Lee, “A novel valued tolerance rough set and decision rules method for indoor positioning using WiFi fingerprinting,” Sensors, vol. 22, no. 15, pp. 1–26, 2022.

Y. C. Chuang, Z. Q. Li, C. W. Hsu, Y. Liu, and C. W. Chow, “Visible light communication and positioning using positioning cells and machine learning algorithms,” Optics Express, vol. 27, no. 11, pp. 16 377–16 383, 2019.

J. C. Torres, A. Montes, S. L. Mendoza, P. R. Fern´andez, J. S. Betancourt, L. Escandell, C. I. Del Valle, and J. M. S´anchez-Pena, “A lowcost visible light positioning system for indoor positioning,” Sensors, vol. 20, no. 18, pp. 1–14, 2020.

A. Xiao, R. Chen, D. Li, Y. Chen, and D. Wu, “An indoor positioning system based on static objects in large indoor scenes by using smartphone cameras,” Sensors, vol. 18, no. 7, pp. 1–17, 2018.

S. Li, B. Yu, Y. Jin, L. Huang, H. Zhang, and X. Liang, “Image-based indoor localization using smartphone camera,” Wireless Communications and Mobile Computing, vol. 2021, pp. 1–9, 2021.

K. Nguyen-Huu and S. W. Lee, “A multi-floor indoor pedestrian localization method using landmarks detection for different holding styles,” Mobile Information Systems, vol. 2021, pp. 1–15, 2021.

Y. Wu, R. Chen, W. Fu, W. Li, H. Zhou, and G. Guo, “Indoor positioning based on tightly coupling of PDR and one single Wi-Fi FTM AP,” Geo-spatial Information Science, vol. 26, no. 3, pp. 480–495, 2023.

J. Chen, S. Song, and Z. Liu, “A PDR/WiFi indoor navigation algorithm using the federated particle filter,” Electronics, vol. 11, no. 20, pp. 1–17, 2022.

X. Wang, G. Chen, M. Yang, and S. Jin, “A multimode pdr perception and positioning system assisted by map matching and particle filtering,” ISPRS International Journal of Geo-Information, vol. 9, no. 2, pp. 1–23, 2020.

K. Nguyen-Huu, C. G. Song, and S. W. Lee, “Smartphone holding styles based step detection and length estimation,” Journal of Information Science & Engineering, vol. 35, no. 3, pp. 537–554, 2019.

Y. Yao, L. Pan, W. Fen, X. Xu, X. Liang, and X. Xu, “A robust step detection and stride length estimation for pedestrian dead reckoning using a smartphone,” IEEE Sensors Journal, vol. 20, no. 17, pp. 9685–9697, 2020.

L. Luu, A. Pillai, H. Lea, R. Buendia, F. M. Khan, and G. Dennis, “Accurate step count with generalized and personalized deep learning on accelerometer data,” Sensors, vol. 22, no. 11, pp. 1–18, 2022.

M. Vezoˇcnik, R. Kamnik, and M. B. Juric, “Inertial sensor-based step length estimation model by means of principal component analysis,” Sensors, vol. 21, no. 10, pp. 1–22, 2021.

Q. Wang, L. Ye, H. Luo, A. Men, F. Zhao, and Y. Huang, “Pedestrian stride-length estimation based on LSTM and denoising autoencoders,” Sensors, vol. 19, no. 4, p. 840, 2019.

M. Shu, G. Chen, and Z. Zhang, “EL-SLE: Efficient learning based stride-length estimation using a smartphone,” Sensors, vol. 22, no. 18, pp. 1–24, 2022.

P. Lawitzki and J. Charzinski, “Application of dynamic binaural signals in acoustic games,” Master’s thesis, Stuttgart Media University, 2012.

A. Poulose, J. Kim, and D. S. Han, “A sensor fusion framework for indoor localization using smartphone sensors and Wi-Fi RSSI measurements,” Applied Sciences, vol. 9, no. 20, pp. 1–17, 2019.

M. Sun, Y. Wang, S. Xu, H. Cao, and M. Si, “Indoor positioning integrating PDR/geomagnetic positioning based on the genetic-particle filter,” Applied Sciences, vol. 10, no. 2, pp. 1–22, 2020.

L. F. Shi, R. He, and B. L. Feng, “Indoor localization scheme using magnetic map for smartphones,” Wireless Personal Communications, vol. 122, pp. 1329–1347, 2022.

M. Fan, J. Li, and W. Wang, “Inertial indoor pedestrian navigation based on cascade filtering integrated INS/map information,” Sensors, vol. 22, no. 22, pp. 1–16, 2022.

W. Sun, J. Wu, W. Ding, and S. Duan, “A robust indirect Kalman filter based on the gradient descent algorithm for attitude estimation during dynamic conditions,” IEEE Access, vol. 8, pp. 96 487–96 494, 2020.

Y. J. Yu, X. Zhang, and M. S. A. Khan, “Attitude heading reference algorithm based on transformed cubature Kalman filter,” Measurement and Control, vol. 53, no. 7-8, pp. 1446–1453, 2020.

S. B. Farahan, J. J. M. Machado, F. G. De Almeida, and J. M. R. S. Tavares, “9-DOF IMU-based attitude and heading estimation using an extended Kalman filter with bias consideration,” Sensors, vol. 22, no. 9, pp. 1–25, 2022.

D. Yan, C. Shi, and T. Li, “An improved PDR system with accurate heading and step length estimation using handheld smartphone,” The Journal of Navigation, vol. 75, no. 1, pp. 141–159, 2022.

F. Jurado Romero, E. Munoz Diaz, and D. Bousdar Ahmed, “Smartphone-based localization for passengers commuting in traffic hubs,” Sensors, vol. 22, no. 19, pp. 1–16, 2022.

Q. Tian, Z. Salcic, I. Kevin, K. Wang, and Y. Pan, “A multi-mode dead reckoning system for pedestrian tracking using smartphones,” IEEE Sensors Journal, vol. 16, no. 7, pp. 2079–2093, 2015.

K. Kunze, P. Lukowicz, K. Partridge, and B. Begole, “Which way am I facing: Inferring horizontal device orientation from an accelerometer signal,” in 2009 International Symposium on Wearable Computers. Linz, Austria: IEEE, Sept. 4–7, 2009, pp. 149–150.

B. Wang, X. Liu, B. Yu, R. Jia, and X. Gan, “Pedestrian dead reckoning based on motion mode recognition using a smartphone,” Sensors, vol. 18, no. 6, p. 1811, 2018.

H. I. Ashqar, M. H. Almannaa, M. Elhenawy, H. A. Rakha, and L. House, “Smartphone transportation mode recognition using a hierarchical machine learning classifier and pooled features from time and frequency domains,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 1, pp. 244–252, 2018.

A. A. Orlov, K. V. Makarov, and E. S. Tarantova, “Features selection for human activity recognition in telerehabilitation,” in 2019 International Science and Technology Conference” EastConf”. Vladivostok, Russia: IEEE, March 1–2, 2019, pp. 1–5.

M. B. Dehkordi, A. Zaraki, and R. Setchi, “Feature extraction and feature selection in smartphone-based activity recognition,” Procedia Computer Science, vol. 176, pp. 2655–2664, 2020.

P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine Learning, vol. 63, pp. 3–42, 2006.

S. Colton, “The balance filter: A simple solution for integrating accelerometer and gyroscope measurements for a balancing platform,” 2007.

Downloads

Published

2024-04-29
Abstract 164  .
PDF downloaded 65  .