Analysis of Indoor Rowing Motion using Wearable Inertial Sensors

S. Bosch, M. Shoaib, Stephen Geerlings, Lennart Buit, Nirvana Meratnia, Paul J.M. Havinga

  • 1 Citations

Abstract

In this exploratory work the motion of rowers is analyzed while rowing on a rowing machine. This is performed using inertial sensors that measure the orientation at several positions on the body. Using these measurements, this work provides a preliminary analysis of the differences between experienced and novice rowers, or between a good and a bad technique. The analysis shows that the measured postural angles show no clear trend that would set apart experienced and novice rowers or a bad and a good technique. However, there are clear differences in absolute postural angle’s consistency and timing consistency of strokes between novice and experienced rowers. We also applied a machine learning technique to the data to find the similarities between different rowers and an experienced reference rower. The results can be used to compare the quality of the rowing technique with respect to a reference. In this paper, we present our initial results as well as the challenges that need to be further explored.
Original languageEnglish
Article numbere1
Number of pages7
JournalEAI Endorsed Transactions on Internet of Things
Volume2
Issue number6
DOIs
StatePublished - 14 Dec 2015
Event10th EAI International Conference on Body Area Networks 2015 - Sydney, Australia

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Exercise equipment
Sensors
Learning systems

Keywords

  • EWI-26606
  • METIS-315112
  • IR-99021

Cite this

Bosch, S.; Shoaib, M.; Geerlings, Stephen; Buit, Lennart; Meratnia, Nirvana; Havinga, Paul J.M. / Analysis of Indoor Rowing Motion using Wearable Inertial Sensors.

In: EAI Endorsed Transactions on Internet of Things, Vol. 2, No. 6, e1, 14.12.2015.

Research output: Scientific - peer-reviewArticle

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Analysis of Indoor Rowing Motion using Wearable Inertial Sensors. / Bosch, S.; Shoaib, M.; Geerlings, Stephen; Buit, Lennart; Meratnia, Nirvana; Havinga, Paul J.M.

In: EAI Endorsed Transactions on Internet of Things, Vol. 2, No. 6, e1, 14.12.2015.

Research output: Scientific - peer-reviewArticle

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