Tracking rower motion without on-body sensors using an instrumented machine and an artificial neural network

BenSiSaid K, Ababou N, Ababou A, Roth D, von Mammen S (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Proceedings of the Institution of Mechanical Engineers Part P-Journal of Sports Engineering and Technology SAGE Publications (UK and US)

DOI: 10.1177/17543371211014108

Abstract

Human motion tracking is an active field of research driven by its diverse applications in areas such as health care, daily activity recognition, sports, etc. In sports applications, tracking rowing motion is performed to meet several goals, including prevention of injuries, improvement of performance or provision of virtual coaching. Different established approaches rely on on-body sensors to capture rowing motion. While on-body sensors are effective and straight forward to implement, they can disturb the athlete and negatively impact the training. In this paper, an approach is presented to track rowing motion without body-worn sensors or cameras. Instead, sensors were attached to an indoor rowing machine that tracked the motion of its sliding seat, lever handle and the force exercised on the seat. In particular, the motion was tracked by means of linear and angular displacement sensors, as well as force sensors placed underneath the seat. The respective variables were fed into an Artificial Neural Network (ANN) to predict the coordinates of the rower’s shoulder, which in turn, were used to geometrically infer the angles of the shoulder, elbow, hip and thoracolumbar flexion-extension. A successful ANN architecture was iteratively designed by using the Levenberg-Marquardt algorithm and varying the number of hidden neurons in one hidden layer. A comparison between ANN-predicted and experimentally obtained shoulder coordinates from an optical motion capture system showed a mean error of less than 4 cm, which led to an angle mean error value as low as 2.01°. A rigged avatar was used to visually verify the reproduced motion. The avatar animation was well-received by experts, especially considering the shoulder adduction-abduction in the frontal plane.

Authors with CRIS profile

Daniel Roth Juniorprofessur für Human-Centered Computing and Extended Reality

Involved external institutions

University of Science and Technology Houari Boumediene

Algeria (DZ) Julius-Maximilians-Universität Würzburg

Germany (DE)

How to cite

APA:

BenSiSaid, K., Ababou, N., Ababou, A., Roth, D., & von Mammen, S. (2021). Tracking rower motion without on-body sensors using an instrumented machine and an artificial neural network. Proceedings of the Institution of Mechanical Engineers Part P-Journal of Sports Engineering and Technology. https://dx.doi.org/10.1177/17543371211014108

MLA:

BenSiSaid, Karim, et al. "Tracking rower motion without on-body sensors using an instrumented machine and an artificial neural network." Proceedings of the Institution of Mechanical Engineers Part P-Journal of Sports Engineering and Technology (2021).

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