Erg Stroke Analysis

Project Overview:

For our QEA 3 final project, we chose to analyze the motion of people using rowing machines, also known as ergometers or ergs. By comparing the data we receive from two sensors, one connected to the handle of the erg and one connected to the user’s shoulder, we analyze the different stages of the user’s stroke and provide tips on how to improve stroke timing.

Stages of a Rowing Stroke

There are four stages of a rowing stroke: catch, drive, finish, and recovery. These stages can be seen in the diagrams at left. The oars are placed in the water during the catch at the very beginning of the stroke. The drive is the process of pulling the oars through the water. The finish is your body's position at the end of the drive after pulling the oars out of the water. Finally, the recovery is the reverse of the drive where the body and blades return to the catch position, with the blades out of the water.

For our model, we focus on analyzing the drive and the recovery. The drive is broken up into three ordered steps -- extending your legs, leaning backward, and then pulling with your arms. Similarly, the recovery includes the same three steps, but their order is reversed. As shown in the diagram above, the steps to the recovery are extending your arms, leaning forward, and re-bending your legs.

The most common mistakes people make when learning to row is bending their arms too early in the drive, and most commonly, beginning to bending their legs before straightening their arms and leaning forward during the recovery (7 Ways To Improve Your Rowing Stroke). Our project aims to help beginner rowers correct their form by completing the stages of the recovery in the correct order.

Project Goals:

Using two sensors, one connected to the handle of the erg and one secured to the seat, we hope to analyze the relative motion of the legs and the arms and back.

MVP

Be able to plot the velocities of the handle (full stroke) and seat (legs) from acceleration data collected from sensors
Use DFT to find and compare the frequencies and amplitudes of the velocities of the handle and seat data
Be able to see the differences between rowing correctly and bending your legs too early in the velocity plots and the DFT plots

Reach Goals

Gather insights from data – tell the user if their arms are moving correctly
Determine which other frequencies exist in the motion and figure out where they are coming form
Collect raw data from the erg and compare it to our results

Motion Model

Data Analysis

Page updated

Google Sites

Report abuse