Golf Swing Segmentation from a Single IMU Using Machine Learning
Abstract
:1. Introduction
2. Methods
2.1. Golf Swing Phases
2.2. Experiment
2.3. Data Processing
2.4. Swing-Phase Segmentation Algorithms
2.4.1. Heuristic-Based Segmentation
2.4.2. Bidirectional Long Short-Term Memory-Based Method
2.4.3. Convolutional Neural Network-Based Method
2.5. Model Training and Validation
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Camomilla, V.; Bergamini, E.; Fantozzi, S.; Vannozzi, G. Trends supporting the in-field use of wearable inertial sensors for sport performance evaluation: A systematic review. Sensors 2018, 18, 873. [Google Scholar] [CrossRef] [Green Version]
- Ahmad, N.; Ariffin, R.; Ghazilla, R.; Khairi, N.M. Reviews on Various Inertial Measurement Unit (IMU) Sensor Applications. Int. J. Signal Process. Syst. 2013, 1, 256–262. [Google Scholar] [CrossRef] [Green Version]
- Nam, C.N.K.; Kang, H.J.; Suh, Y.S. Golf swing motion tracking using inertial sensors and a stereo camera. IEEE Trans. Instrum. Meas. 2014, 63, 943–952. [Google Scholar] [CrossRef]
- Lai, D.T.H.; Hetchl, M.; Wei, X.C.; Ball, K.; McLaughlin, P. On the difference in swing arm kinematics between low handicap golfers and non-golfers using wireless inertial sensors. Procedia Eng. 2011, 13, 219–225. [Google Scholar] [CrossRef]
- Lückemann, P.; Haid, D.M.; Brömel, P.; Schwanitz, S.; Maiwald, C. Validation of an Inertial Sensor System for Swing Analysis in Golf. Proceedings 2018, 2, 246. [Google Scholar] [CrossRef] [Green Version]
- Jacobson, B.H.; Stemm, J.D.; Redus, B.S.; Goldstein, D.F.; Kolb, T. Center of Vertical Force and Swing Tempo in Selected Groups of Elite Collegiate Golfers. Sport J. 2005, 8, 1–4. [Google Scholar]
- McHardy, A.; Pollard, H. Muscle activity during the golf swing. Br. J. Sports Med. 2005, 39, 799–804. [Google Scholar] [CrossRef] [Green Version]
- Glazebrook, M.A.; Curwin, S.; Islam, M.N.; Kozey, J.; Stanish, W.D. Medial Epicondylitis: An Electromyographic Analysis and an Investigation of Intervention Strategies. Am. J. Sports Med. 1994, 22, 674–679. [Google Scholar] [CrossRef]
- Zheng, N.; Barrentine, S.W.; Fleisig, C.S.; Andrews, J.R. Kinematic analysis of swing in pro and amateur golfers. Int. J. Sports Med. 2008, 29, 487–493. [Google Scholar] [CrossRef]
- Hsu, Y.; Chen, Y.; Chou, P.; Kou, Y.; Chen, Y.; Su, H. Golf Swing Motion Detection Using an Inertial-Sensor-Based Portable Instrument. In Proceedings of the International conference on consumer electronics-Taiwan, Nantou County, Taiwan, 27–29 May 2016; pp. 1–2. [Google Scholar]
- Jensen, U.; Kugler, P.F.; Dassler, F.A.; Eskofier, B.M. Sensor-based Instant Golf Putt Feedback. In Proceedings of the 8th International Symposium on Computer Science in Sport, Shanghai, China, 21–24 September 2011; pp. 3–6. [Google Scholar]
- Kooyman, D.J.; James, D.A.; Rowlands, D.D. A feedback system for the motor learning of skills in golf. Procedia Eng. 2013, 60, 226–231. [Google Scholar] [CrossRef] [Green Version]
- Tu, Y.; Liu, L.; Li, M.; Chen, P.; Mao, Y. A review of human motion monitoring methods using wearable sensors. Int. J. Online Eng. 2018, 14, 168–179. [Google Scholar] [CrossRef]
- Wang, W.; Adamczyk, P.G. Analyzing Gait in the Real World Using Wearable Movement Sensors and Frequently Repeated Movement Paths. Sensors 2019, 19, 1925. [Google Scholar] [CrossRef] [Green Version]
- Cust, E.E.; Sweeting, A.J.; Ball, K.; Robertson, S. Machine and deep learning for sport-specific movement recognition: A systematic review of model development and performance. J. Sports Sci. 2019, 37, 568–600. [Google Scholar] [CrossRef]
- LeCun, Y.; Boser, B.; Denker, J.S.; Henderson, D.; Howard, R.E.; Hubbard, W.; Jackel, L.D. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Comput. 1989, 1, 541–551. [Google Scholar] [CrossRef]
- Rav, D.; Wong, C.; Lo, B.; Yang, G. A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices. IEEE J. Biomed. Health Inform. 2017, 12, 106–137. [Google Scholar] [CrossRef] [Green Version]
- Yang, J.B.; Nguyen, M.N.; San, P.P.; Li, X.L.; Krishnaswamy, S. Deep convolutional neural networks on multichannel time series for human activity recognition. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, 25–31 July 2015. [Google Scholar]
- Zebin, T.; Scully, P.J.; Ozanyan, K.B. Human activity recognition with inertial sensors using a deep learning approach. In Proceedings of the IEEE SENSORS, Orlando, FL, USA, 30 October–3 November 2016; pp. 10–12. [Google Scholar]
- Zeng, M.; Nguyen, L.T.; Yu, B.; Mengshoel, O.J.; Zhu, J.; Wu, P.; Zhang, J. Convolutional Neural Networks for human activity recognition using mobile sensors. In Proceedings of the 6th International Conference on Mobile Computing, Applications and Services, Austin, TX, USA, 6–7 November 2014; pp. 197–205. [Google Scholar]
- Zheng, Y.; Liu, Q.; Chen, E.; Ge, Y.; Zhao, J.L. Time series classification using multi-channels deep convolutional neural networks. In Proceedings of the International Conference on Web-Age Information Management, Macau, China, 16–18 June 2014; pp. 298–310. [Google Scholar]
- Anand, A.; Sharma, M.; Srivastava, R.; Kaligounder, L.; Prakash, D. Wearable motion sensor based analysis of swing sports. In Proceedings of the 16th International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico, 18–21 December 2017; pp. 261–267. [Google Scholar]
- Jiao, L.; Wu, H.; Bie, R.; Umek, A.; Kos, A. Multi-sensor Golf Swing Classification Using Deep CNN. Procedia Comput. Sci. 2018, 129, 59–65. [Google Scholar] [CrossRef]
- Kautz, T.; Groh, B.H.; Hannink, J.; Jensen, U.; Strubberg, H.; Eskofier, B.M. Activity recognition in beach volleyball using a Deep Convolutional Neural Network: Leveraging the potential of Deep Learning in sports. Data Min. Knowl. Discov. 2017, 31, 1678–1705. [Google Scholar] [CrossRef]
- Rassem, A.; El-Beltagy, M.; Saleh, M. Cross-Country Skiing Gears Classification Using Deep Learning. Available online: https://arxiv.org/abs/1706.08924 (accessed on 7 August 2020).
- Hochreiter, S.; Schmidhuber, J. Long Short-Term Memory. Neural Comput. 1997, 9, 1735–1780. [Google Scholar] [CrossRef]
- Graves, A.; Schmidhuber, J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. In Proceedings of the Neural Networks, Pergamon, Turkey, 10–15 September 2005; Volume 18, pp. 602–610. [Google Scholar]
- Jensen, U.; Schmidt, M.; Hennig, M.; Dassler, F.A.; Jaitner, T.; Eskofier, B.M. An IMU-based mobile system for golf putt analysis. Sport. Eng. 2015, 18, 123–133. [Google Scholar] [CrossRef]
- Meister, D.W.; Ladd, A.L.; Butler, E.E.; Zhao, B.; Rogers, A.P.; Ray, C.J.; Rose, J. Rotational biomechanics of the elite golf swing: Benchmarks for amateurs. J. Appl. Biomech. 2011, 27, 242–251. [Google Scholar] [CrossRef] [Green Version]
- Cooper, J.M.; Bates, B.T.; Bedi, J.; Scheuchenzuber, J. Kinematic and kinetic analysis of the golf swing. Biomech. IV 1974, 298–305. [Google Scholar]
- Neal, R.J.; Wilson, B.D. 3D Kinematics and Kinetics of the Golf Swing. Int. J. Sport Biomech. 1985, 1, 221–232. [Google Scholar] [CrossRef]
- Burden, A.M.; Grimshaw, P.N.; Wallace, E.S. Hip and shoulder rotations during the golf swing of sub-10 handicap players. J. Sports Sci. 1998, 16, 165–176. [Google Scholar] [CrossRef]
- Sprigings, E.J.; Mackenzie, S.J. Examining the delayed release in the golf swing using computer simulation. Sports Eng. 2002, 5, 23–32. [Google Scholar] [CrossRef]
- Kenny, I.C.; McCloy, A.J.; Wallace, E.S.; Otto, S.R. Segmental sequencing of kinetic energy in a computer-simulated golf swing. Sports Eng. 2008, 11, 37–45. [Google Scholar] [CrossRef]
- Tinmark, F.; Hellström, J.; Halvorsen, K.; Thorstensson, A. Elite golfers’ kinematic sequence in full-swing and partial-swing shots. Sports Biomech. 2010, 9, 236–244. [Google Scholar] [CrossRef]
- Verikas, A.; Vaiciukynas, E.; Gelzinis, A.; Parker, J.; Charlotte Olsson, M. Electromyographic patterns during golf swing: Activation sequence profiling and prediction of shot effectiveness. Sensors 2016, 16, 592. [Google Scholar] [CrossRef] [Green Version]
- Horan, S.A.; Kavanagh, J.J. The control of upper body segment speed and velocity during the golf swing. Sports Biomech. 2012, 11, 165–174. [Google Scholar] [CrossRef] [Green Version]
- McHardy, A.J.; Pollard, H.P. Golf and upper limb injuries: A summary and review of the literature. Chiropr. Osteopat. 2005, 13, 1–7. [Google Scholar] [CrossRef] [Green Version]
- Wood, G.A. Data smoothing and differentiation procedures in biomechanics. Exerc. Sport Sci. Rev. 1982, 10, 308–362. [Google Scholar] [CrossRef]
- Bridle, J.S. Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition. In Neurocomputing; Springer: Berlin/Heidelberg, Germany, 1990; pp. 227–236. [Google Scholar]
- Kingma, D.P.; Ba, J.L. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations, ICLR, San Diego, CA, USA, 7–9 May 2015. [Google Scholar]
- Huang, Y.C.; Chen, T.L.; Chiu, B.C.; Yi, C.W.; Lin, C.W.; Yeh, Y.J.; Kuo, L.C. Calculate golf swing trajectories from IMU sensing data. In Proceedings of the IEEE International Conference on Parallel Processing Workshops, Pittsburgh, PA, USA, 10–13 September 2012; pp. 505–513. [Google Scholar]
- Kumada, K.; Usui, Y.; Kondo, K. Golf swing tracking and evaluation using Kinect sensor and particle filter. In Proceedings of the ISPACS 2013 International Symposium on Intelligent Signal Processing and Communication Systems, Okinawa, Japan, 12–15 November 2013; pp. 698–703. [Google Scholar]
- Chotimanus, P.; Cooharojananone, N.; Phimoltares, S. Real swing extraction for video indexing in golf practice video. In Proceedings of the 2012 Computing, Communications and Applications Conference, Hong Kong, China, 11–13 January 2012; pp. 420–425. [Google Scholar]
- Neal, R.; Lumsden, R.; Holland, M.; Mason, B. Body Segment Sequencing and Timing in Golf. Int. J. Sports Sci. Coach. 2007, 2, 25–36. [Google Scholar] [CrossRef]
- Lee, C.; Park, S. Estimation of Unmeasured Golf Swing of Arm Based on the Swing Dynamics. Int. J. Precis. Eng. Manuf. 2018, 19, 745–751. [Google Scholar] [CrossRef]
- Mesaros, A.; Heittola, T.; Eronen, A.; Virtanen, T. Acoustic event detection in real life recordings. In Proceedings of the European Signal Processing Conference, Aalborg, Denmark, 23–27 August 2010; pp. 1267–1271. [Google Scholar]
- Waibel, A.; Hanazawa, T.; Hinton, G.; Shikano, K.; Lang, K.J. Phoneme Recognition Using Time-Delay Neural Networks. IEEE Trans. Acoust. 1989, 37, 328–339. [Google Scholar] [CrossRef]
- Bottou, L.; Fogelman Soulié, F.; Blanchet, P.; Liénard, J.S. Speaker-independent isolated digit recognition: Multilayer perceptrons vs. Dynamic time warping. Neural Netw. 1990, 3, 453–465. [Google Scholar] [CrossRef]
- Kalchbrenner, N.; Grefenstette, E.; Blunsom, P. A convolutional neural network for modelling sentences. arXiv 2014, arXiv:1404.2188. [Google Scholar]
- Kim, Y. Convolutional neural networks for sentence classification. arXiv 2014, arXiv:1408.5882. [Google Scholar]
- van den Oord, A.; Dieleman, S.; Zen, H.; Simonyan, K.; Vinyals, O.; Graves, A.; Kalchbrenner, N.; Senior, A.; Kavukcuoglu, K. WaveNet: A Generative Model for Raw Audio. 2016. Available online: https://arxiv.org/abs/1609.03499 (accessed on 10 August 2020).
- Graves, A. Generating Sequences with Recurrent Neural Networks. 2013. Available online: https://arxiv.org/abs/1308.0850 (accessed on 10 August 2020).
- Hermans, M.; Schrauwen, B. Training and analyzing deep recurrent neural networks. In Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA, 5–10 December 2013. [Google Scholar]
- Bahdanau, D.; Cho, K.H.; Bengio, Y. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations, ICLR, San Diego, CA, USA, 7–9 May 2015. [Google Scholar]
- Bai, S.; Kolter, J.Z.; Koltun, V. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. 2018. Available online: https://arxiv.org/abs/1803.01271 (accessed on 10 August 2020).
- Seneviratne, S.; Hu, Y.; Nguyen, T.; Lan, G.; Khalifa, S.; Thilakarathna, K.; Hassan, M.; Seneviratne, A. A Survey of Wearable Devices and Challenges. IEEE Commun. Surv. Tutorials 2017, 19, 2573–2620. [Google Scholar] [CrossRef]
- Ghasemzadeh, H.; Panuccio, P.; Trovato, S.; Fortino, G.; Jafari, R. Power-aware activity monitoring using distributed wearable sensors. IEEE Trans. Hum. Mach. Syst. 2014, 44, 537–544. [Google Scholar] [CrossRef]
- Mukhopadhyay, S.C. Wearable sensors for human activity monitoring: A review. IEEE Sens. J. 2015, 15, 1321–1330. [Google Scholar] [CrossRef]
- Maltby, R. Golf club design, fitting, alteration and repair: The principles and procedures. 1982; ISBN 0927956039. [Google Scholar]
- Lindsay, D.M.; Mantrop, S.; Vandervoort, A.A. A Review of Biomechanical Differences between Golfers of Varied Skill Levels. Int. J. Sports Sci. Coach. 2008, 3, 187–197. [Google Scholar] [CrossRef] [Green Version]
- Zheng, N.; Barrentine, S.W.; Fleisig, C.S.; Andrews, J.R. Swing kinematics for male and female pro golfers. Int. J. Sports Med. 2008, 29, 965–970. [Google Scholar] [CrossRef] [PubMed]
- Parker, J.; Hellström, J.; Olsson, M.C. Differences in kinematics and driver performance in elite female and male golfers. Sports Biomech. 2019. Available online: https://doi.org/10.1080/14763141.2019.1683221 (accessed on 10 August 2020).
- Horan, S.A.; Evans, K.; Morris, N.R.; Kavanagh, J.J. Thorax and pelvis kinematics during the downswing of male and female skilled golfers. J. Biomech. 2010, 43, 1456–1462. [Google Scholar] [CrossRef] [PubMed]
- Jagacinski, R.J.; Greenberg, N.; Liao, M.J. Tempo, rhythm, and aging in golf. J. Mot. Behav. 1997, 29, 159–173. [Google Scholar] [CrossRef] [PubMed]
IMU Placement. | ADD 1 | BST 1 | IMP 1 | FIN 1 |
---|---|---|---|---|
Wrist | min.2 of ω 3 in y-axis | z.c.2 of ω in z-axis | min. of ω in y-axis | min. of ω in norm |
Head | min. of ω in norm | min. of a 3 in x-axis | min. of a in norm | min. of ω in norm |
Waist | min. of ω in y-axis | z.c. of ω in y-axis | z.c. of a in x-axis | min. of ω in norm |
Backswing | Downswing | Follow-through | Full-Swing |
---|---|---|---|
1.163 ± 0.232 s | 0.317 ± 0.050 s | 0.670 ± 0.119 s | 2.151 ± 0.294 s |
Error (%) | Backswing | Downswing | Follow-through | |||||||
---|---|---|---|---|---|---|---|---|---|---|
a 1 | g 1 | a + g 1 | a | g | a + g | a | g | a + g | ||
H3 | 28.5 ± 7.5 | 6.7 ± 4.4 | 6.7 ± 4.4 | 24.3 ± 11.9 | 6.9 ± 2.3 | 6.9 ± 2.3 | 35.8 ± 17.5 | 10.9 ± 7.8 | 10.9 ± 7.8 | |
Wr2 | B3 | 4.9 ± 3.0 | 3.6 ± 2.8 | 3.1 ± 2.6 | 4.8 ± 2.7 | 3.2 ± 2.0 | 2.9 ± 1.9 | 9.1 ± 6.5 | 6.7 ± 6.0 | 7.1 ± 6.6 |
C3 | 4.9 ± 3.6 | 4.9 ± 3.3 | 4.5 ± 2.9 | 3.7 ± 2.1 | 3.2 ± 2.4 | 2.7 ± 1.8 | 6.8 ± 5.7 | 7.2 ± 6.0 | 6.9 ± 5.5 | |
H | N/A | N/A | 34.3 ± 20.7 | N/A | N/A | 79.1 ± 33.4 | N/A | N/A | 20.5 ± 15.5 | |
Hd2 | B | 12.2 ± 5.7 | 8.9 ± 6.3 | 6.6 ± 4.3 | 8.4 ± 4.7 | 4.6 ± 3.6 | 4.6 ± 2.9 | 10.6 ± 8.0 | 8.7 ± 8.4 | 8.1 ± 7.8 |
C | 7.1 ± 5.4 | 5.9 ± 4.0 | 5.8 ± 3.9 | 5.8 ± 4.2 | 5.0 ± 3.5 | 3.3 ± 2.3 | 7.8 ± 6.0 | 7.4 ± 6.1 | 6.3 ± 5.5 | |
H | N/A | N/A | 11.5 ± 5.9 | N/A | N/A | 26.3 ± 20.9 | N/A | N/A | 33.4 ± 18.2 | |
Wa2 | B | 12.5 ± 5.6 | 6.4 ± 3.7 | 6.7 ± 3.8 | 8.3 ± 4.9 | 4.6 ± 3.0 | 5.2 ± 3.6 | 13.2 ± 9.1 | 8.1 ± 6.7 | 9.0 ± 6.6 |
C | 6.6 ± 3.7 | 5.1 ± 3.3 | 4.2 ± 2.6 | 4.0 ± 3.0 | 3.4 ± 2.7 | 2.7 ± 2.1 | 8.5 ± 6.6 | 7.0 ± 5.8 | 6.4 ± 5.6 |
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Kim, M.; Park, S. Golf Swing Segmentation from a Single IMU Using Machine Learning. Sensors 2020, 20, 4466. https://doi.org/10.3390/s20164466
Kim M, Park S. Golf Swing Segmentation from a Single IMU Using Machine Learning. Sensors. 2020; 20(16):4466. https://doi.org/10.3390/s20164466
Chicago/Turabian StyleKim, Myeongsub, and Sukyung Park. 2020. "Golf Swing Segmentation from a Single IMU Using Machine Learning" Sensors 20, no. 16: 4466. https://doi.org/10.3390/s20164466
APA StyleKim, M., & Park, S. (2020). Golf Swing Segmentation from a Single IMU Using Machine Learning. Sensors, 20(16), 4466. https://doi.org/10.3390/s20164466