Design and implement a mobile badminton stroke classification system

Lin, Junyang; Chang, Chia-Wei; Wang, Chih-Hao; Chi, Hong-Chuan; Yi, Chih‐Wei; Tseng, Yu–Chee; Wang, Chih-Chuan

doi:10.1109/apnoms.2017.8094141

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…A previous study used an IMU accelerometer and a microphone placed on the racket to perform stroke classification with 6 classes [18], which led to an accuracy of 95.51% when classifying a single player, and 79.32% when training and testing on a same set of 10 players. However, the study does not consider the scenario of separating the players in the training and test set.…”

Section: Related Workmentioning

confidence: 99%

Badminton stroke classification based on accelerometer data: from individual to generalized models

Peralta

Herbruggen

Fontaine

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

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Activity recognition models based on wearable devices are becoming increasingly popular. However, models that are trained and tested on the same players show a large bias and are not generalizable to previously unseen players. In this paper, we tackle the badminton stroke recognition problem from this perspective, comparing the performance of individual and generalized models based on an accelerometer and a gyroscope, and identifying which components of the solution can maximize the performance of generalized models. First, we describe a simple convolutional neural network trained to classify 7 types of stroke. Second, the model is extended in a hybrid way to identify two additional classes (movement and rest). Third, data augmentation is applied on the training set. Fourth, transfer learning is applied to use data from the test player to fine-tune the generalized model and attempt to reach the performance of an individual model. These models are evaluated on a dataset collected from amateur players, both in a controlled environment and in a match simulation. The results showed a large difference between the performance of individual and generalized models; however, the latter could be improved by increasing the number of players in the training set, by data augmentation, and by transfer learning, highlighting the necessity of larger datasets in this field.

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Section: Related Workmentioning

confidence: 99%

Badminton stroke classification based on accelerometer data: from individual to generalized models

Peralta

Herbruggen

Fontaine

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

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“…For the experiment setup, they attached the inertial measurement units to the chest, upper arm, forearm, wrist, and waist. Lin et al [9,10] designed and implemented a mobile application a the badminton stroke classification using Random Forest. Egret et al [11] performed the kinematic analysis of the golf swing for experienced golfers.…”

Section: Related Workmentioning

confidence: 99%

“…For example, several researches and applications have been done for tennis such as sensor-based swing and stroke classification as a tool for personal feedback and coaching [5][6][7]. Another similar research has also been done with regards to the baseball pitching [8], badminton stroke classification [9,10], and the golf swing as well [11].…”

Section: Introduction 11 Backgroundmentioning

confidence: 99%

Toward the Perfect Stroke: A Multimodal Approach for Table Tennis Stroke Evaluation

Sri-iesaranusorn

Garcia

Tiausas

et al. 2021

2021 Thirteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)

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Developing a consistent stroke is a challenge and even more so for nonprofessional Table tennis players. To build consistent proper strokes for beginner players, there is a need to understand the stroke differences between standard and beginner players. In terms of table tennis applications, prior works used a video-based method, or accelerometer sensor embedded in a table tennis racket, or infrared (IR) depth sensor for evaluating the stroke. However, there are certain challenges in these methods such as having insufficient data to analyse a complete stroke, time-consuming and costly data collection, as well as using non-prevalent equipment. Hence, to improve the beginner player's performance, an ubiquitous way through readily accessible commercial devices for stroke evaluation is essential. In this study, to achieve such a goal, we (i) recorded videos and accelerator signals of standard and beginner players using consumer-grade products, and (ii) analysed the stroke consistency of both standard and beginner players. The results show the significant differences in the strokes between both kinds of players through the multimodal approach. Also, we found the significantly strong correlation between the stroke consistency and the hitting score for the forehand stroke. These findings motivate us to further examine the improvement of beginner players by instructing procedural knowledge of a standard player's stroke, and implement applications for the motor-skill instruction.

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Trajectory-based Badminton Shots Detection

İk

et al. 2020

2020 International Conference on Pervasive Artificial Intelligence (ICPAI)

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Design and implement a mobile badminton stroke classification system

Cited by 10 publications

References 9 publications

Badminton stroke classification based on accelerometer data: from individual to generalized models

Badminton stroke classification based on accelerometer data: from individual to generalized models

Toward the Perfect Stroke: A Multimodal Approach for Table Tennis Stroke Evaluation

Trajectory-based Badminton Shots Detection

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