A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors
Abstract
:1. Introduction
- Proposal of a novel ensemble method (i.e., the super-bagging method) and its demonstration for volleyball action modelling,
- Evaluation of the super-bagging method against undersampling (i.e, balanced learning), full sampling (i.e., imbalanced learning) and ensemble (i.e., tree bagger) methods for volleyball action modelling,
- Demonstration of the role of dominant and non-dominant hand for volleyball action modelling using super-bagging method,
- Evaluation of all four IMU sensors separately and in combination for volleyball action modelling using different learning methods (i.e., balanced learning, imbalanced learning and super-bagging methods).
2. Related Work
3. Our Approach
3.1. Data Annotation
3.2. Auto-Tagging System Prototype
4. Super-Bagging Method
5. Experimentation
5.1. The Data Set
5.2. Feature Extraction
5.3. Classification Methods
5.4. Experiments
- Experiment 1 (): training is performed on the imbalanced data sets (i.e., ) in terms of action and non-actions and validation is performed on the imbalanced data set (i.e., ) in leave-one-subject out settings. The prior-probabilities of classifiers are set according to the classes distribution.
- Experiment 2 (): training is performed on the balanced data sets (i.e., ) in terms of actions and non-actions, where same number of non-actions events (selected randomly) and action events for each player are used. The validation is performed on the imbalanced data set (i.e., ) in leave-one-subject out settings. The prior-probabilities of classifiers are set to be equal for both classes as in this setting the distribution of classes is same.
- Experiment 3 (): training is performed using the super-bagging method and validation is performed on the imbalanced data set in leave-one-subject out settings.
6. Results and Discussions
6.1. Experiment 1 (): Imbalanced Learning Method
6.2. Experiment 2 (): Balanced Learning Method
6.3. Experiment 3 (): Super-Bagging Method
6.4. Discussion
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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ID | DH | Action(sec) | Non-Action(sec) | # Actions | Forearm Pass | One hand Pass | Overhead Pass | Serve | Smash | Underhand Serve | Block |
---|---|---|---|---|---|---|---|---|---|---|---|
R | 198 | 3055.25 | 120 | 40 | 3 | 16 | 0 | 29 | 28 | 4 | |
L | 193.75 | 3061 | 125 | 36 | 2 | 14 | 32 | 15 | 0 | 6 | |
R | 191 | 3030 | 116 | 50 | 3 | 3 | 34 | 25 | 0 | 1 | |
R | 176.75 | 3054.5 | 124 | 46 | 2 | 19 | 21 | 28 | 4 | 4 | |
R | 228.5 | 3009 | 150 | 30 | 1 | 70 | 0 | 12 | 30 | 7 | |
R | 135.5 | 3080.25 | 106 | 39 | 4 | 13 | 0 | 14 | 34 | 2 | |
R | 146.25 | 3077.5 | 105 | 34 | 4 | 16 | 34 | 17 | 0 | 0 | |
R | 183.25 | 3044.5 | 144 | 42 | 1 | 58 | 33 | 4 | 1 | 5 | |
total | – | 1453 | 24,412 | 990 | 317 | 20 | 209 | 154 | 144 | 97 | 49 |
Sensor | TB | DT | KNN | NB | SVM | LDA | Avg. | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | |
Acc. | 70.17 | 0.02 | 70.83 | 0.02 | 68.83 | 0.02 | 79.83 | 0.03 | 59.77 | 0.02 | 69.56 | 0.03 | 69.83 |
Mag. | 60.67 | 0.03 | 63.10 | 0.02 | 57.12 | 0.02 | 74.16 | 0.03 | 50.00 | 0 | 67.71 | 0.03 | 62.13 |
Gyr. | 61.55 | 0.03 | 64.07 | 0.03 | 60.78 | 0.02 | 74.58 | 0.03 | 53.35 | 0.02 | 64.86 | 0.03 | 63.20 |
Baro. | 58.43 | 0.03 | 59.22 | 0.05 | 56.53 | 0.04 | 57.24 | 0.06 | 53.01 | 0.01 | 56.78 | 0.03 | 56.87 |
Fusion | 70.37 | 0.07 | 70.75 | 0.10 | 68.77 | 0.08 | 80.30 | 0.02 | 60.14 | 0.02 | 74.53 | 0.03 | 70.81 |
Sensor | TB | DT | KNN | NB | SVM | LDA | Avg. | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | |
Acc. | 68.59 | 0.05 | 71.53 | 0.13 | 72.98 | 0.12 | 83.99 | 0.06 | 66.47 | 0.08 | 75.90 | 0.09 | 73.24 |
Mag. | 58.41 | 0.02 | 76.61 | 0.11 | 67.67 | 0.09 | 80.83 | 0.11 | 66.75 | 0.09 | 75.74 | 0.10 | 71.00 |
Gyr. | 60.37 | 0.03 | 61.42 | 0.05 | 58.85 | 0.03 | 75.71 | 0.07 | 50.00 | 0 | 64.70 | 0.04 | 61.84 |
Baro. | 52.16 | 0.02 | 40.86 | 0.21 | 38.56 | 0.22 | 31.53 | 0.21 | 50.00 | 0 | 50.53 | 0.00 | 43.94 |
Fusion | 71.64 | 0.06 | 71.85 | 0.24 | 66.93 | 0.25 | 79.58 | 0.08 | 73.59 | 0.10 | 82.93 | 0.09 | 74.42 |
Sensor | TB | DT | KNN | NB | SVM | LDA | Avg. | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | |
Acc. | 84.18 | 0.03 | 81.99 | 0.02 | 82.50 | 0.02 | 82.19 | 0.03 | 82.35 | 0.02 | 80.52 | 0.02 | 82.29 |
Mag. | 81.71 | 0.02 | 77.47 | 0.02 | 74.86 | 0.02 | 79.25 | 0.04 | 79.50 | 0.03 | 79.08 | 0.03 | 78.65 |
Gyr. | 77.91 | 0.05 | 73.72 | 0.03 | 75.48 | 0.04 | 75.94 | 0.04 | 74.17 | 0.04 | 72.78 | 0.03 | 75.00 |
Baro. | 58.51 | 0.09 | 57.19 | 0.06 | 56.80 | 0.08 | 59.30 | 0.08 | 61.45 | 0.03 | 61.01 | 0.03 | 59.04 |
Fusion | 83.10 | 0.03 | 79.46 | 0.03 | 80.69 | 0.03 | 80.83 | 0.04 | 81.57 | 0.03 | 80.32 | 0.02 | 81.00 |
Sensor | TB | DT | KNN | NB | SVM | LDA | Avg. | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | |
Acc. | 82.16 | 0.03 | 78.90 | 0.03 | 80.33 | 0.03 | 81.71 | 0.02 | 81.28 | 0.03 | 79.84 | 0.04 | 80.70 |
Mag. | 77.59 | 0.04 | 74.80 | 0.03 | 69.59 | 0.04 | 75.31 | 0.04 | 76.69 | 0.04 | 75.90 | 0.05 | 74.98 |
Gyr. | 76.79 | 0.03 | 72.84 | 0.02 | 73.42 | 0.03 | 74.74 | 0.04 | 75.35 | 0.03 | 75.10 | 0.04 | 74.71 |
Baro. | 53.07 | 0.04 | 51.57 | 0.03 | 50.22 | 0.04 | 49.46 | 0.06 | 55.88 | 0.02 | 56.07 | 0.02 | 52.72 |
Fusion | 79.59 | 0.03 | 76.70 | 0.03 | 76.18 | 0.03 | 78.25 | 0.03 | 79.60 | 0.04 | 79.24 | 0.04 | 78.26 |
Sensor | TB | DT | KNN | NB | SVM | LDA | Avg. | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | |
Acc. | 84.19 | 0.03 | 82.70 | 0.02 | 82.50 | 0.02 | 82.19 | 0.03 | 82.35 | 0.02 | 80.67 | 0.02 | 82.43 |
Mag. | 81.67 | 0.02 | 77.97 | 0.02 | 74.86 | 0.02 | 79.18 | 0.04 | 79.50 | 0.03 | 79.08 | 0.05 | 78.71 |
Gyr. | 77.91 | 0.05 | 74.34 | 0.03 | 75.48 | 0.04 | 75.95 | 0.04 | 74.17 | 0.04 | 72.80 | 0.03 | 75.11 |
Baro. | 58.51 | 0.09 | 57.25 | 0.06 | 56.80 | 0.08 | 59.32 | 0.08 | 61.45 | 0.03 | 61.04 | 0.03 | 59.06 |
Fusion | 82.87 | 0.03 | 80.22 | 0.03 | 80.59 | 0.03 | 81.24 | 0.03 | 81.58 | 0.03 | 80.66 | 0.02 | 81.19 |
Sensor | TB | DT | KNN | NB | SVM | LDA | Avg. | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | Std | UAR | |
Acc. | 82.40 | 0.03 | 79.38 | 0.06 | 80.30 | 0.05 | 82.93 | 0.04 | 80.50 | 0.04 | 79.93 | 0.05 | 80.91 |
Mag. | 77.59 | 0.04 | 78.95 | 0.04 | 71.36 | 0.06 | 77.89 | 0.06 | 77.21 | 0.04 | 77.82 | 0.05 | 76.80 |
Gyr. | 76.79 | 0.03 | 72.05 | 0.03 | 73.15 | 0.03 | 72.37 | 0.04 | 75.35 | 0.03 | 74.63 | 0.04 | 74.06 |
Baro. | 53.07 | 0.04 | 47.35 | 0.10 | 45.66 | 0.10 | 39.80 | 0.11 | 55.88 | 0.02 | 56.07 | 0.02 | 49.64 |
Fusion | 80.11 | 0.03 | 80.12 | 0.08 | 77.65 | 0.08 | 80.87 | 0.06 | 80.13 | 0.04 | 81.58 | 0.05 | 80.08 |
Sensor | Imbalanced | Balanced | Super-Bagging | |||
---|---|---|---|---|---|---|
DH | NDH | DH | NDH | DH | NDH | |
Acc. | 69.83 | 73.24 | 82.29 | 80.70 | 82.43 | 80.91 |
Mag. | 62.13 | 71.00 | 78.65 | 74.98 | 78.71 | 76.80 |
Gyr. | 63.20 | 61.84 | 75.00 | 74.71 | 75.11 | 74.06 |
Baro. | 56.87 | 43.94 | 59.04 | 52.72 | 59.06 | 49.64 |
Fusion | 70.81 | 74.42 | 81.00 | 78.26 | 81.19 | 80.08 |
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Share and Cite
Haider, F.; Salim, F.A.; Postma, D.B.W.; van Delden, R.; Reidsma, D.; van Beijnum, B.-J.; Luz, S. A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors. Multimodal Technol. Interact. 2020, 4, 33. https://doi.org/10.3390/mti4020033
Haider F, Salim FA, Postma DBW, van Delden R, Reidsma D, van Beijnum B-J, Luz S. A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors. Multimodal Technologies and Interaction. 2020; 4(2):33. https://doi.org/10.3390/mti4020033
Chicago/Turabian StyleHaider, Fasih, Fahim A. Salim, Dees B.W. Postma, Robby van Delden, Dennis Reidsma, Bert-Jan van Beijnum, and Saturnino Luz. 2020. "A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors" Multimodal Technologies and Interaction 4, no. 2: 33. https://doi.org/10.3390/mti4020033