2019

Sensors

Continuous Analysis of Running Mechanics by Means of an Integrated INS/GPS Device

Pavel Davidson, Heikki Virekunnas, Dharmendra Sharma. Robert Piché, Neil Cronin

Faculty of Information Technology and Communication Sciences, Tampere University

Keywords

gait analysis, INS/GPS, machine learning, neural networks, sports equipment, velocity measurement

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.

Moticon's Summary

In this study the authors introduce a sensor platform including multiple sensor types to serve as a substitute for individual sensing devices commonly used in biomechanical analysis. The proposed sensor platform incorporates accelerometers, gyroscopes, compasses, barometers and a GPS receiver. Furthermore, a neural network is used to estimate certain movement parameters. Moticon sensor insoles are used to provide a training data set for the neural network as well as for validation purposes. Test data was acquired with three subjects during walking and running. The authors found promising results in terms of accuracy of the computed movement parameters.

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