Analysis of Indoor Rowing Motion using Wearable Inertial Sensors

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.

A Systematic Review of Performance Analysis in Rowing Using Inertial Sensors

“…Presenting raw signal data to a coach or an athlete is sometimes inappropriate as distinct biomechanical events are not distinct; noise can be eradicated from a signal using correctly designed filters. A high volume of the reviewed papers used a windowing/filtering technique during their data analysis (15/36-41.7%) [14,18,22,26,27,30,34,[38][39][40][41][42][43][44]47].…”

“…Four of the reviewed papers (4/15-26.7%) used sensor fusion algorithms for different purposes [22,39,40,47]. Of these, three-quarters (3/4-75.0%) used sensor fusion computational algorithms to obtain the rower's orientation metrics (e.g., joint angles and oar angles) [22,40,47]. Using inertial sensors to obtain orientation data can produce insightful metrics.…”

“…By extracting relevant features in both the time and frequency domains, researchers can apply these algorithms to generate personalized athlete models to further understand their performance. Four of the reviewed records reported the use of these algorithms [17,22,25,42]. Atallah et al [17] used a KNN model to classify different activities, whereby rowing was one (76.39% success rate).…”

“…Atallah et al [17] used a KNN model to classify different activities, whereby rowing was one (76.39% success rate). Bosch et al [22] also used a KNN technique, however, to use inertial sensor signals to distinguish between novice and experienced rowers. The researchers did this by comparing the signals obtained by both experienced and novice rowers to a template generated by an experienced rower.…”

“…Nevertheless, recruiting participants of different levels can strengthen the robustness of machine learning algorithms, improving their accuracy for athletes and the general public. One record recruited novice rowers in order to create a machine learning algorithm which could decipher between experienced and unexperienced rowing techniques within the inertial sensor signals [22].…”