NDLI: SwingPong: analysis and suggestion based on motion data from mobile sensors for table tennis strokes using decision tree

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SwingPong: analysis and suggestion based on motion data from mobile sensors for table tennis strokes using decision tree

Content Provider	ACM Digital Library
Author	Komonpetch, Abhirat Viyanon, Waraporn Chatchawal, Sittichai Kosasaeng, Vimvipa
Abstract	Table tennis is a sport that everyone can play, regardless of age or gender. It becomes a popular sport since it does not require much space and its equipment is affordable. For beginners, they may not have a chance to get some helpful advice to learn how to control a table tennis ball. Thus, we decided to analyze user's motion data and build a model to develop an application called SwingPong detecting basic table-tennis strokes in order to provide helpful suggestion to players. SwingPong is an android application on a smartphone analyzing user's forehand and backhand strokes and providing suggestion back to the user in order to improve his or her swing. There are two main modules in the application: Module #1: storing angular acceleration and angular velocity of strokes. When a table tennis ball is hit on the racket, the angular acceleration and angular velocity of user's swing are collected using accelerometer and gyroscope sensors in smartphones. We collected the training data set from table tennis national players, Thai youth national team table tennis athletes and coaches. J48 Decision Tree was used in order to build a model of hitting classification. Module #2: providing suggestions. We applied the model from Module#1 to evaluate user's basic strokes and provide alert sound when a ball is hit. After a user hits the ball in a given time, the application analyzes the user's strokes and shows suggestions. The experimental results show that SwingPong is able to recognize strokes whether the ball is good (or in), out, or hits the net. The accuracy of classification of forehand strokes is 77.21% and the accuracy of classification of backhand strokes is 69.63%. In addition, SwingPong can provide helpful suggestions so that users can use the feedbacks to improve their swings.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450347990
DOI	10.1145/3028842.3028860
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-12-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Mobile sensors Table tennis strokes Feedback Accelerometer Gyroscope Decision tree Motion analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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