Conference contribution
(Conference Contribution)


Movement prediction in rowing using a Dynamic Time Warping based stroke detection


Publication Details
Author(s): Groh B, Reinfelder S, Streicher M, Taraben A, Eskofier B
Title edited volumes: IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
Publisher: IEEE Computer Society
Publication year: 2014
Conference Proceedings Title: IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2014)
Pages range: 1-6

Event details
Event: IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2014)
Event location: Singapore
Start date of the event: 21/04/2014
End date of the event: 24/04/2014

Abstract

In professional rowing competitions, sensor data is transmitted from an on-board sensor unit on the boat to an external computer system. This system calculates the current position of each boat in real-time. However, incomplete localizations occur as a result of radio transmission outages. This paper introduces an algorithm to overcome transmission outages by predicting the rowing movement. The prediction algorithm is based on accelerometer and GPS data that is provided by the on-board unit before an outage occurs. It uses Subsequence Dynamic Time Warping (subDTW) to detect the rowing strokes in the acceleration signal. Knowing the previous strokes, the system predicts the upcoming strokes, as the rowing motion follows a periodic pattern. Thereby, the GPS measured velocity can be extrapolated and the position is predicted. A further outcome of the subDTW stroke detection is an accurate determination of the rowing stroke rate. In our experiment, we evaluate the rowing stroke detection and stroke rate determination based on subDTW as well as the prediction algorithm for simulated outages of professional race data. It shows a subDTW stroke signal detection of 100% after the start phase of the race. The prediction in case of a sensor outage of 5 seconds leads to a correlation between the predicted velocity and the actual velocity of 0.96 and a resulting position error (RMSE) of 0.3 m.



How to cite
APA: Groh, B., Reinfelder, S., Streicher, M., Taraben, A., & Eskofier, B. (2014). Movement prediction in rowing using a Dynamic Time Warping based stroke detection. In IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2014) (pp. 1-6). IEEE Computer Society.

MLA: Groh, Benjamin, et al. "Movement prediction in rowing using a Dynamic Time Warping based stroke detection." Proceedings of the IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2014), Singapore IEEE Computer Society, 2014. 1-6.

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