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Wearable Multi-Sensor Positioning Prototype for Rowing Technique Evaluation.

Luis Rodriguez Mendoza1, Kyle O'Keefe1

  • 1Geomatics Department, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study validates a wearable sensor for rowing training, assessing its accuracy in tracking a rower's wrist motion. The prototype effectively captures position, velocity, and attitude for technique analysis.

Keywords:
GNSSinertial navigation system (INS)inertial sensors (IMU)rowingultra-wideband (UWB)wearable technology

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Area of Science:

  • Sports Science
  • Biomechanics
  • Wearable Technology

Background:

  • Accurate measurement of rowing technique is crucial for athlete performance and injury prevention.
  • Existing methods for evaluating rowing technique may lack precision or real-time feedback capabilities.
  • Wearable sensors offer a potential solution for objective and detailed analysis of athletic movements.

Purpose of the Study:

  • To assess the feasibility of a wearable multi-sensor prototype for evaluating rowing technique.
  • To determine the positioning accuracy of the wearable device using various mathematical models and estimation methods.
  • To explore the potential of the device as a training tool for rowers.

Main Methods:

  • Development of a wearable device integrating an inertial measurement unit (IMU), ultra-wideband (UWB) transceiver, and global navigation satellite system (GNSS) receiver.
  • Conducting an experiment on a rowing shell to collect data from the device on a rower's wrist.
  • Comparing the device's trajectory data against a centimeter-level GNSS reference trajectory.
  • Analyzing rowing motion using multiple navigation frames and diverse positioning algorithms.

Main Results:

  • The wearable device prototype demonstrated viability for rowing technique analysis.
  • The system successfully provided real-time position, velocity, and attitude data of the rower's wrist.
  • Positioning accuracy varied between ±0.185 m and ±1.656 m, contingent upon the specific estimation method employed.

Conclusions:

  • The developed wearable multi-sensor system is a feasible tool for enhancing rowing technique evaluation and training.
  • The integration of IMU, UWB, and GNSS technologies provides valuable biomechanical insights for athletes.
  • Further refinement of estimation methods can optimize the positioning accuracy for advanced rowing analysis.