<abstract xmlns="http://www.w3.org/1999/xhtml">Artificial Intelligence (AI) invades fields where sophisticated analytics has not been applied before. Modality refers to how something happens or is experienced. Multimodal datasets are beneficial for solving complex research problems with AI methods. Kayaking technique optimization has been challenging, as there seems to be no gold standard for effective paddling techniques since there are outstanding athletes with profoundly different physical capabilities and kayaking styles.Multimodal analysis can help find the most effective paddling techniques for training and competition based on individuals’ abilities.We describe the characteristics of the output power of kayak athletes and Electromyogram (EMG) measurements collected from the most critical muscles, and the relationship between these modalities. We propose metrics (weighted arithmetic mean difference and variability of power output and stroke duration) suitable for discerning athletes based on how efficiently and correctly they perform particular training tasks. Additionally, the described methods (asymmetry, coactivation, muscle intensity-output power) help athletes and coaches in assessing their performance and compare it with others based on their EMG activities.As the next step, we will apply machine-learning approaches on the synchronized dataset we collect with the described methods to reveal desirable EMG and stroke patterns.</abstract>

Artificial Intelligence (AI) invades fields where sophisticated analytics has not been applied before. Modality refers to how something happens or is experienced. Multimodal datasets are beneficial for solving complex research problems with AI methods. Kayaking technique optimization has been challenging, as there seems to be no gold standard for effective paddling techniques since there are outstanding athletes with profoundly different physical capabilities and kayaking styles.Multimodal analysis can help find the most effective paddling techniques for training and competition based on individuals’ abilities.We describe the characteristics of the output power of kayak athletes and Electromyogram (EMG) measurements collected from the most critical muscles, and the relationship between these modalities. We propose metrics (weighted arithmetic mean difference and variability of power output and stroke duration) suitable for discerning athletes based on how efficiently and correctly they perform particular training tasks. Additionally, the described methods (asymmetry, coactivation, muscle intensity-output power) help athletes and coaches in assessing their performance and compare it with others based on their EMG activities.As the next step, we will apply machine-learning approaches on the synchronized dataset we collect with the described methods to reveal desirable EMG and stroke patterns.

Multimodal Approach for Kayaking Performance Analysis and Improvement

Faculty of Information Technology and Bionics

Department of Telecommunications and Media Informatics

Budapest University of Technology and Economics

Department of Health Sciences and Sport Medicine

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Multimodal Approach for Kayaking Performance Analysis and Improvement"}

Artificial Intelligence (AI) invades fields where sophisticated analytics has not been applied before. Modality refers to how something happens or is...

Multimodal Approach for Kayaking Performance Analysis and Improvement

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 51 - 76

DOI: https://doi.org/10.2478/ijcss-2020-0010

Schlüsselwörter
KAYAKING TECHNIQUE ANALYSIS, KAYAKING PERFORMANCE, SPORTS ANALYTICS, MAXIMAL INTENSITY EXERCISE TEST

© 2020 G. Nagy et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Multimodal Approach for Kayaking Performance Analysis and Improvement

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 51 - 76

DOI: https://doi.org/10.2478/ijcss-2020-0010

SchlüsselwörterKAYAKING TECHNIQUE ANALYSIS, KAYAKING PERFORMANCE, SPORTS ANALYTICS, MAXIMAL INTENSITY EXERCISE TEST

© 2020 G. Nagy et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
KAYAKING TECHNIQUE ANALYSIS, KAYAKING PERFORMANCE, SPORTS ANALYTICS, MAXIMAL INTENSITY EXERCISE TEST