Paper Info
Title
Analyzing tennis game through sensor data with machine learning and multi-objective optimization.
Abstract
Wearable devices are heavily used in many sports. However, the existing sports wearables are either not tennis-specific, or are limited to information on shots. We therefore added tennis-specific information to a leading commercial device. Firstly, we developed a method for classifying shot types into forehand, backhand and serve. Secondly, we used multi-objective optimization to distinguish active play from the time in-between points. By combining both parts with the general movement information already provided by the device, we get comprehensive metrics for professional players and coaches to objectively measure a player's performance and enable in-depth tactical analysis.
Year
DOI
Venue
2017
10.1145/3123024.3123163
UbiComp '17: The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing Maui Hawaii September, 2017
Keywords
Field
DocType
Tennis, Wearable analytics, Shot detection, Optimization
Computer science,Wearable computer,Multi-objective optimization,Wearable technology,Multimedia,Backhand
Conference
ISBN
Citations 
PageRank 
978-1-4503-5190-4
1
0.35
References 
Authors
6
2
Name
Order
Citations
PageRank
Miha Mlakar1203.30
Mitja Luštrek241054.52