Skip to main content Accessibility help
×
Home
Hostname: page-component-59b7f5684b-569ts Total loading time: 0.293 Render date: 2022-10-02T20:50:36.153Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

Comparison of EMG-based and Accelerometer-based Speed Estimation Methods in Pedestrian Dead Reckoning

Published online by Cambridge University Press:  02 March 2011

Wei Chen*
Affiliation:
(Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China) (Department of Navigation and Positioning, Finnish Geodetic Institute, Masala, Finland)
Ruizhi Chen
Affiliation:
(Department of Navigation and Positioning, Finnish Geodetic Institute, Masala, Finland)
Xiang Chen*
Affiliation:
(Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China)
Xu Zhang
Affiliation:
(Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China)
Yuwei Chen
Affiliation:
(Department of Navigation and Positioning, Finnish Geodetic Institute, Masala, Finland)
Jianyu Wang
Affiliation:
(Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China) (Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China)
Zhongqian Fu
Affiliation:
(Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China)

Abstract

In low-cost self-contained pedestrian navigation systems, traditional Pedestrian Dead Reckoning (PDR) solutions utilize accelerometers to derive the speed as well as the distance travelled, and obtain the walking heading from magnetic compasses or gyros. However, these measurements are sensitive to instrument errors and disturbances from ambient environment. To be totally different from these signals in nature, the electromyography (EMG) signal is a typical kind of biomedical signal that measures electrical potentials generated by muscle contractions from the human body. This kind of signal would reflect muscle activities during human locomotion, so that it can not only be used for speed estimation, but also disclose the azimuth information from the contractions of lumbar muscles when changing the direction of walking. Therefore, investigating how to utilize the EMG signal for PDR is interesting and promising. In this paper, a novel EMG-based speed estimation method is presented, including setup of the EMG equipment, pre-processing procedure, stride detection and stride length estimation. Furthermore, this method suggested is compared with the traditional one based on accelerometers by means of several field tests. The results demonstrate that the EMG-based method is effective and its performance in PDR can be comparable to that of the accelerometer-based method.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anders, C., Wagner, H., Puta, C., Grassme, R., Petrovitch, A. and Scholle, H. C. (2007). Trunk muscle activation patterns during walking at different speeds. Journal of Electromyography and Kinesiology, 17, 245252.CrossRefGoogle ScholarPubMed
Beauregard, S. and Haas, H. (2006). Pedestrian dead reckoning: a basis for personal positioning. Proceedings Of the 3rd Workshop on Positioning, Navigation and Communication, Hannover, Germany.Google Scholar
Campanini, I., Merlo, A., Degola, P., Merletti, R., Vezzosi, G. and Farina, D. (2007). Effect of electrode location on EMG signal envelope in leg muscles during gait. Journal of Electromyography and Kinesiology, 17, 515526.CrossRefGoogle ScholarPubMed
Chai, H. M. (2004). Applications of kinesiology – gait during ambulation. (Web: http://www.pt.ntu.edu.tw/hmchai/Kines04/KINapplication/Gait.htm).Google Scholar
Chen, X., Zhang, X., Zhao, Z., Yang, J., Lantz, V. and Wang, K. (2007). Hand gesture recognition research based on surface EMG sensors and 2D-accelerometers. Proceedings of the 11th IEEE International Symposium on Wearable Computers, Boston, MA, USA.Google Scholar
Chen, R., Chen, Y., Pei, L., Chen, W., Kuusniemi, H., Liu, J., Leppäkoski, H. and Takala, J. (2009a). A DSP-based multi-sensor multi-network positioning platform. Proceedings of ION GNSS 2009, Savannah, Georgia, USA.Google Scholar
Chen, W., Fu, Z., Chen, R., Chen, Y., Andrei, O., Kroger, T. and Wang, J. (2009b). An integrated GPS and multi-sensor pedestrian positioning system for 3D urban navigation. Proceedings of the Joint Urban Remote Sensing Event 2009, Shanghai, China.Google Scholar
Chen, W., Chen, R., Chen, Y., Kuusniemi, H., Wang, J. and Fu, Z. (2010). An adaptive calibration approach for a 2-axis digital compass in a low-cost pedestrian navigation system. Proceedings of the IEEE International Instrumentation and Measurement Technology Conference 2010, Austin, TX, USA.CrossRefGoogle Scholar
Cho, S. and Park, C. (2006). MEMS based pedestrian navigation system. Journal of Navigation, 59, 135153.CrossRefGoogle Scholar
Cram, J. R., Kasman, G. S. and Holtz, J. (1998). Introduction to Surface Electromyography. Aspen Publishers.Google Scholar
Fang, L., Antsaklis, P., Montestruque, L., McMickell, M., Lemmon, M., Sun, Y., Fang, H., Koutroulis, I., Haenggi, M., Xie, M. and Xie, X. (2005). Design of a wireless assisted pedestrian dead reckoning system – the NavMote experience. IEEE Transactions on Instrumentation and Measurement, 54, 23422358.CrossRefGoogle Scholar
Godha, S., Lachapelle, G. and Cannon, M. E. (2006). Integrated GPS/INS system for pedestrian navigation in a signal degraded environment. Proceedings of ION GNSS 2006, Fort Worth, TX, USA.Google Scholar
Grejner-Brzezinska, D., Toth, C. and Moafipoor, S. (2007). Pedestrian tracking and navigation using an adaptive knowledge system based on neural networks. Journal of Applied Geodesy, 1, 111123.CrossRefGoogle Scholar
Ivanenko, Y. P., Poppele, R. E. and Lacquaniti, F. (2004). Five basic muscle activation patterns account for muscle activity during human locomotion. Journal of Physiology, 556, 267282.CrossRefGoogle ScholarPubMed
Judd, T. (1997). A personal dead reckoning module. Proceedings of ION GPS 1997, Kansas, Missouri, USA.Google Scholar
Käppi, J., Syrjärinne, J. and Saarinen, J. (2001). MEMS-IMU based pedestrian navigator for handheld devices. Proceedings of ION GPS 2001, Salt Lake City, UT, USA.Google Scholar
Kim, J., Jang, J., Hwang, D. H. and Park, C. (2004). A step, stride and heading determination for the pedestrian navigation system. Journal of Global Positioning Systems, 3, 273276.CrossRefGoogle Scholar
Ladetto, Q. (2000). On foot navigation: continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering. Proceedings of ION GPS 2000, Salt Lake City, UT, USA.Google Scholar
Leppäkoski, H., Käppi, J., Syrjärinne, J. and Takala, J. (2002). Error analysis of step length estimation in pedestrian dead reckoning. Proceedings of ION GPS 2002, Portland, OR, USA.Google Scholar
Levi, R. and Judd, T. (1999). Dead reckoning navigational system using accelerometer to measure foot impacts. United State Patent, No. 5,583,776.Google Scholar
Mezentsev, O. (2005). Sensor aiding of HSGPS pedestrian navigation. Ph. D thesis, University of Calgary.Google Scholar
Moafipoor, S., Grejner-Brzezinska, D. and Toth, C. (2008). A fuzzy dead reckoning algorithm for a personal navigator. Journal of the Institute of Navigation, 55, 241254.CrossRefGoogle Scholar
Reaz, M. B. I., Hussain, M. S. and Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: detection, processing, classification and applications. Biological Procedures Online, 8, 1135.CrossRefGoogle ScholarPubMed
Retscher, G. (2007). Test and integration of location sensors for a multi-sensor personal navigator. Journal of Navigation, 60, 107117.CrossRefGoogle Scholar
Saponas, T. S., Tan, D. S., Morris, D. and Balakrishnan, R. (2008). Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces. Proceedings of CHI 2008, Florence, Italy.Google Scholar
Vildjiounaite, E., Malm, E., Kaartinen, J. and Alahuhta, P. (2002). Location estimation indoors by means of small computing power devices, accelerometers, magnetic sensors, and map knowledge. Proceedings of 1st Int'l Conf. Pervasive Computing, Lecture Notes In Computer Science, 2414, 211224.Google Scholar
Weimann, F. and Abwerzger, G. (2007). A pedestrian navigation system for urban and indoor environments. Proceedings of ION GNSS 20th International Technical Meeting, Fort Worth, TX, USA.Google Scholar
Zhang, X., Chen, X., Wang, W., Yang, J., Lantz, V. and Wang, K. (2009). Hand gesture recognition and virtual game control based on 3D accelerometer and EMG sensors. Proceedings of IUI'09, Sanibel Island, Florida, USA.Google Scholar
26
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Comparison of EMG-based and Accelerometer-based Speed Estimation Methods in Pedestrian Dead Reckoning
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Comparison of EMG-based and Accelerometer-based Speed Estimation Methods in Pedestrian Dead Reckoning
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Comparison of EMG-based and Accelerometer-based Speed Estimation Methods in Pedestrian Dead Reckoning
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *