A1 Journal article (refereed)
Continuous Analysis of Running Mechanics by Means of an Integrated INS/GPS Device (2019)

Davidson, P., Virekunnas, H., Sharma, D., Piché, R., & Cronin, N. (2019). Continuous Analysis of Running Mechanics by Means of an Integrated INS/GPS Device. Sensors, 19(6), Article 1480. https://doi.org/10.3390/s19061480

JYU authors or editors

Publication details

All authors or editors: Davidson, Pavel; Virekunnas, Heikki; Sharma, Dharmendra; Piché, Robert; Cronin, Neil

Journal or series: Sensors

ISSN: 1424-8220

eISSN: 1424-8220

Publication year: 2019

Volume: 19

Issue number: 6

Article number: 1480

Publisher: MDPI AG

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.3390/s19061480

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/63432

Abstract

This paper describes a single body-mounted sensor that integrates accelerometers, gyroscopes, compasses, barometers, a GPS receiver, and a methodology to process the data for biomechanical studies. The sensor and its data processing system can accurately compute the speed, acceleration, angular velocity, and angular orientation at an output rate of 400 Hz and has the ability to collect large volumes of ecologically-valid data. The system also segments steps and computes metrics for each step. We analyzed the sensitivity of these metrics to changing the start time of the gait cycle. Along with traditional metrics, such as cadence, speed, step length, and vertical oscillation, this system estimates ground contact time and ground reaction forces using machine learning techniques. This equipment is less expensive and cumbersome than the currently used alternatives: Optical tracking systems, in-shoe pressure measurement systems, and force plates. Another advantage, compared to existing methods, is that natural movement is not impeded at the expense of measurement accuracy. The proposed technology could be applied to different sports and activities, including walking, running, motion disorder diagnosis, and geriatric studies. In this paper, we present the results of tests in which the system performed real-time estimation of some parameters of walking and running which are relevant to biomechanical research. Contact time and ground reaction forces computed by the neural network were found to be as accurate as those obtained by an in-shoe pressure measurement system.

Keywords: biomechanics; running; measuring instruments (devices); satellite navigation; machine learning; neural networks (information technology)

Free keywords: gait analysis; INS/GPS; neural networks; sports equipment; velocity measurement

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2019

JUFO rating: 1