COMPARISON OF TIME DOMAIN STEP COUNTING METHOD FOR PEDESTRIAN DEAD RECKONING
DOI:
https://doi.org/10.18372/1990-5548.64.14854Keywords:
Pedestrian dead reckoning, autocorrelation function, thresholding, peaks detection, zero crossingAbstract
The problem of step counting has been considered for personal navigation while walking and using mobile sensors with low accuracy. Step counting has been realized by three main methods of processing the acceleration vector magnitude in time domain. The comparison of this methods has been done while processing the data from mobile phone sensors for different conditions and types of pedestrian/their motion patterns. In order to have representative statistics the walking trajectories have been selected long enough (at least 100 meters) except the method of normalized auto-correlation based step counting where short distances have been processed. The requirements to definite method of step counting have been formulated.
References
S. Beauregard and H. Haas, “Pedestrian dead reckoning: A basis for personal positioning,” in Proceedings of the 3rd Workshop on Positioning, Navigation and Communication, March 2006, pp. 27–35.
A. R. Jimenez, F. Seco, C. Prieto, and J. Guevara,
“A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU,” in 2009 IEEE International Symposium on Intelligent Signal Processing, August 2009, pp. 37–42.
P. Kasebzadeh, C. Fritsche, G. Hendeby, F. Gunnarsson, and F. Gustafsson, “Improved pedestrian dead reckoning positioning with gait parameter learning,” in 2016 19th International Conference on Information Fusion (FUSION), July 2016, pp. 379–385.
A. Brajdic, R. Harle, “Walk detection and step counting on unconstrained smartphones,” in Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, September, 2013, pp. 225–234. https://doi.org/10.1145/2493432.2493449
B. Huang, G. Qi, X. Yang, L. Zhao, and H. Zou, “Exploiting cyclic features of walking for pedestrian dead reckoning with unconstrained smartphones,” in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, September, 2016, pp. 374–385. https://doi.org/10.1145/2971648.2971742
B. Kibushi, S. Hagio, T. Moritani, and M. Kouzaki, “Speed-dependent modulation of muscle activity based on muscle synergies during treadmill walking,” Human Neuroscience, 2018, 12, p. 4. https://doi.org/10.3389/fnhum.2018.00004
A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen, “Zee: Zero-effort crowdsourcing for indoor localization,” in Proceedings of the 18th annual international conference on Mobile computing and networking, August 2012, pp. 293–304. https://doi.org/10.1145/2348543.2348580
A. R. Pratama and R. Hidayat, “Smartphone-based pedestrian dead reckoning as an indoor positioning system,” in 2012 International Conference on System Engineering and Technology (ICSET), September 2012, pp. 1–6. https://doi.org/10.1109/ICSEngT.2012.6339316
Downloads
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
