BadminSense: Enabling Fine-Grained Badminton Stroke Evaluation on a Single Smartwatch
Authors: Taizhou Chen, Kai Chen, Xingyu Liu, Pingchuan Ke, Zhida Sun
Organizations: Department of Computer Science, Shantou University, Shantou, China · CSSE, Shenzhen University, Shenzhen, China · Department of Sociology, Hong Kong Shue Yan University, Hong Kong, China
Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players. We present BadminSense, a smartwatch-based system for fine-grained badminton performance analysis using wearable sensing. Through interviews with experienced badminton players, we identified four system design requirements with three implementation insights that guide the development of BadminSense. We then collected a badminton strokes dataset on 12 experienced badminton amateurs and annotated it with fine-grained labels, including stroke type, expert-assessed stroke rating, and shuttle impact location. Built on this dataset, BadminSense segments and classifies strokes, predicts stroke quality, and estimates shuttle impact location using vibration signal from an off-the-shelf smartwatch. Our evaluations show that BadminSense achieves a stroke classification accuracy of 91.43%, an average quality rating error of 0.438, and an average impact location estimation error of 12.9%. A real-world usability study further demonstrates BadminSense's potential to provide reliable and meaningful support for daily badminton practice.
Figures & tables
Figure 1. Illustration of the BadminSense’s concept. The system leverages a single smartwatch to detect and provide fine-grained badminton stroke analysis, including stroke classification, stroke quality rating, and impact location estimation. Three screenshots on the right illustrate the front-end interface of BadminSense. Left: Session overview. This interface presents the stroke count in a game session with a performance summary. Middle: Session timeline view ( DR4 ). This interface shows the sequence of strokes executed throughout the session period ( DR4 ). Right: Stroke view. This interface displays the stroke quality rating ( DR1 ), the impact location ( DR2 ), and the improvement advice ( DR3 ) from top to bottom.
Figure 2. Stroke-specific impact location that most users prefer. The light green area indicate s the optimal impact location.
Figure 3. (a) Configuration of the badminton court for data collection and the data collection procedure. 1. Facilitator 1 serves a shuttle sprayed with ink, 2. Subject returns the racket after stroking, 3. Facilitator delivers a cleaned racket for the next stroke, 4. Facilitator 1 records the ink mark and cleans up the racket, 5. Facilitator 2 prepares a clean racket for the next stroke. The red dot in the top left corner denoted the smartphone camera recording position for ground truth labeling. (b)(c) Shuttle with fountain pen ink on the head before serving. (d) An ink mark on the racket face by the shuttle impact after stroking, highlighted in a red circle.
Figure 4. Illustration of the four stroke types that we focused on in this study. The corresponding bar chart below visualizes the distribution of the number of ratings, with the digit below indicating the total sample size in the category.
Figure 5. BadminSense system pipeline overview. The pipeline consists of four components: stroke segmentation, stroke classification, stroke quality rating, and impact location estimation.
Figure 6. (a) Illustration of the smartwatch coordinates. (b) Gyro data sample of each stroke from one user.
Figure 7. Pair-wise illustration of five sequential phases on a forehand overhead clear stroke sample. The five-phase segments in (a) are produced with a corresponding phase motion as illustrated in (b).
Figure 8. The confusion matrix of the SVM results. BOC, FOC, FOS, and FOD denote backhand overhead clear , forehand overhead clear , forehand overhead smash , and forehand overhead drop , respectively.
General
User-Independent
Accuracy
Percision
Recall
F1 score
Accuracy
Percision
Recall
F1 score
SVM
95.05%
95.39%
95.28%
95.23%
91.43%
92.03%
91.99%
92.00%
Linear Regression
54.95%
47.84%
39.69%
41.88%
41.90%
40.53%
28.66%
31.74%
Random Forest
79.72%
81.19%
80.47%
80.29%
66.19%
75.43%
67.62%
66.63%
MLP
92.57%
93.03%
92.91%
92.88%
80.95%
82.47%
81.86%
82.00%
Table 1. Stroke classification performance across models and training strategies
General
User-Independent
BOC
FOC
FOS
FOD
AVG
BOC
FOC
FOS
FOD
AVG
SVR
0.420
0.404
0.413
0.456
0.423
0.411
0.426
0.432
0.483
0.438
LinearReg
1.269
0.542
0.576
0.605
0.748
0.757
0.771
0.553
0.703
0.696
CNN-LSTM
0.574
0.534
0.651
0.513
0.568
0.700
0.727
0.595
0.474
0.624
MS-TCN
0.773
0.574
0.661
0.668
0.669
0.578
0.484
0.516
0.683
0.565
Transformer
0.658
0.540
0.601
0.560
0.590
0.802
0.571
0.669
0.763
0.701
Table 2. MAE of stroke rating performance across models and training strategies. A lower MAE indicates better results. BOC, FOC, FOS, and FOD denote backhand overhead clear , forehand overhead clear , forehand overhead smash , and forehand overhead drop , respectively.
Figure 9. The multi-model signal processing pipeline for evaluating impact location estimation performance. We evaluate this pipeline by testing across multiple machine learning regressors and deep learning encoder models. The processing flow with a thick green line yields the base performance on our task, with an SVR model.
Figure 10. Visualization of the impact location estimation results.
Feature
General
User-Independent
X
Y
AVG
X
Y
AVG
SVR
IMU
0.136
0.129
0.132
0.133
0.124
0.129
Acoustic+IMU
0.135
0.130
0.132
0.135
0.133
0.134
LinearReg
IMU
0.346
0.320
0.333
0.336
0.263
0.300
Acoustic+IMU
0.168
0.159
0.164
0.162
0.173
0.168
CNN
IMU
0.136
0.134
0.135
0.134
0.124
0.129
Table 3. The normalized MAE of the impact location estimate performance on X and Y axis across models, feature selection strategies, and training strategies. A lower MAE indicates better results.
Figure 11. The detailed questionnaire results distribution from the user evaluation. The digit on the bar indicates the number of ratings.
Institute of Communications Engineering National Tsing Hua University, Hsinchu City, Taiwan · Department of Computer Science National Tsing Hua University, Hsinchu City, Taiwan