IoT 16(6): e1

Research Article

Analysis of Indoor Rowing Motion using Wearable Inertial Sensors

Download6407 downloads
  • @ARTICLE{10.4108/eai.28-9-2015.2261465,
        author={Stephan Bosch and Muhammad Shoaib and Stephen Geerlings and Lennart Buit and Nirvana Meratnia and Paul Havinga},
        title={Analysis of Indoor Rowing Motion using Wearable Inertial Sensors},
        journal={EAI Endorsed Transactions on Internet of Things},
        keywords={rowing, inertial motion capture, body sensor network, machine learning},
  • Stephan Bosch
    Muhammad Shoaib
    Stephen Geerlings
    Lennart Buit
    Nirvana Meratnia
    Paul Havinga
    Year: 2015
    Analysis of Indoor Rowing Motion using Wearable Inertial Sensors
    DOI: 10.4108/eai.28-9-2015.2261465
Stephan Bosch1,*, Muhammad Shoaib1, Stephen Geerlings2, Lennart Buit2, Nirvana Meratnia1, Paul Havinga1
  • 1: University of Twente
  • 2: University of Twente, Student
*Contact email:


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.