The application of Machine and Deep Learning for technique and skill analysis in swing and team sport-specific movement: A systematic review

Abdel-Basset, M., Hawash, H., Chakrabortty, R. K., Ryan, M., Elhoseny, M., & Song, H. (2021). STDeepHAR: Deep Learning Model for Human Activity Recognition in IoHT Applications. IEEE Internet of Things Journal, 8(6), 4969 4979. http://iacss.org/index.php?id=30 Search in Google Scholar

Ahmad, N., Ghazilla, R.A., Khairi, N.M., & Kasi, V. (2013). Reviews on Various Inertial Measurement Unit (IMU) Sensor Applications. International Journal of Signal Processing Systems, 1(2), 256-262. https://doi.org/10.12720/ijsps.1.2.256-262 Search in Google Scholar

Ahmadi, A., Mitchell, E., & Destelle, F., Gowing, M., O’Connor, N., Richter, C., & Moran, K. (2014). Automatic Activity Classification and Movement Assessment During a Sports Training Session Using Wearable Inertial Sensors. Proceedings. 11th International Conference on Wearable and Implantable Body Sensor Networks, 98-103. https://doi.org/10.1109/BSN.2014.29 Search in Google Scholar

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & B. F. Csaki (Eds.), Second International Symposium on Information Theory, 267 281. Academiai Kiado: Budapest. https://doi.org/10.1007/978-1-4612-0919-5_38 Search in Google Scholar

Alanen, A., Räisänen, A., Benson, L., & Pasanen, K. (2021). The use of inertial measurement units for analyzing change of direction movement in sports: A scoping review. International Journal of Sports Science & Coaching, 16(6), 1332 - 1353. https://doi.org/10.1177/17479541211003064 Search in Google Scholar

Al-jabery, K. K., Obafemi-Ajayi, T., Olbricht, G. R., & Wunsch II, D. C. (2020). Data analysis and machine learning tools in MATLAB and Python. In K. K. Al-jabery, T. Obafemi-Ajayi, G. R. Olbricht, & D. C. Wunsch II (Eds.), Computational Learning Approaches to Data Analytics in Biomedical Applications (pp. 231-290). Academic Press. ISBN 9780128144824. https://doi.org/10.1016/B978-0-12-814482-4.00009-7 Search in Google Scholar

Alzubi, J.A., Nayyar, A., & Kumar, A. (2018). Machine Learning from Theory to Algorithms: An Overview. Journal of Physics: Conference Series, 1142. https://doi.org/10.1088/1742-6596/1142/1/012012 Search in Google Scholar

Anand, A., Sharma, M., Srivastava, R., Kaligounder, L., & Prakash, D. (2017). Wearable Motion Sensor Based Analysis of Swing Sports. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017, 261-267. https://doi.org/10.1109/ICMLA.2017.0-149 Search in Google Scholar

Angerbauer, S., Palmanshofer, A., Selinger, S., & Kurz, M. (2021). Comparing Human Activity Recognition Models Based on Complexity and Resource Usage. Applied Sciences, 11(18), 8473. https://doi.org/10.3390/app11188473 Search in Google Scholar

Ann, O. C. & Theng, L.B. (2014). Human activity recognition: A review. IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), 389-393. https://doi.org/10.1109/ICCSCE.2014.7072750 Search in Google Scholar

Aresta, S., Bortone, I., Bottiglione, F., Di Noia, T., Di Sciascio, E., Lofù, D., Musci, M., Narducci, F., Pazienza, A., Sardone, R., & Sorino, P. (2022). Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers’ Levels for Training Support. Applied Sciences, 12(23), 12350. https://doi.org/10.3390/app122312350 Search in Google Scholar

Baca, A., Dabnichki, P., Hu, C. W., Kornfeind, P., & Exel, J. (2022). Ubiquitous Computing in Sports and Physical Activity-Recent Trends and Developments. Sensors, 22(21), 8370. https://doi.org/10.3390/s22218370 Search in Google Scholar

Banos, O., Galvez, J. M., Damas, M., Pomares, H., & Rojas, I. (2014). Window Size Impact in Human Activity Recognition. Sensors, 14(4):6474-6499. https://doi.org/10.3390/s140406474 Search in Google Scholar

Batool, M., Jalal, A., & Kim, K. (2020). Telemonitoring of Daily Activity Using Accelerometer and Gyroscope in Smart Home Environments. Journal of Electrical Engineering and Technology, 15, 2801 2809. https://doi.org/10.1007/s42835-020-00554-y Search in Google Scholar

Bo, Y. (2022). A reinforcement learning-based basketball player activity recognition method using multisensors. Mobile Information Systems, 2022, 1-9. https://doi.org/10.1155/2022/6820073 Search in Google Scholar

Bonidia, R.P., Rodrigues, L.A., Avila-Santos, A.P. Sanches, D.S., Brancher, J.D. & Mustapha, A. (2018). Computational Intelligence in Sport: A Systematic Literature Review. Advances in Human-Computer Interaction, 2018. https://doi.org/10.1155/2018/3426178 Search in Google Scholar

Bragança, H., Colonna, J. G., Oliveira, H. A. B. F., & Souto, E. (2022). How Validation Methodology Influences Human Activity Recognition Mobile Systems. Sensors (Basel, Switzerland), 22(6), 2360. https://doi.org/10.3390/s22062360 Search in Google Scholar

Brzostowski, K., & Szwach, P. (2018). Data fusion in ubiquitous sports training: Methodology and application, Wireless Communications and Mobile Computing, 2018, 1-14. https://doi.org/10.1155/2018/8180296 Search in Google Scholar

Bulling, A., Blanke, U., & Schiele, B. (2014). A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Surveys, 46(3), 1-33. https://doi.org/10.1145/2499621 Search in Google Scholar

Camomilla, V., Bergamini, E., Fantozzi, S., & Vannozzi, G. (2018). Trends supporting the in-field use of wearable inertial sensors for sport performance evaluation: A systematic review. Sensors, 18(3), 873. https://doi.org/10.3390/s18030873 Search in Google Scholar

Chai, J., Zeng, H., Li, A., & Ngai, E. (2021). Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, 6, 100134. https://doi.org/10.1016/j.mlwa.2021.100134 Search in Google Scholar

Chambers, R., Gabbett, T. J., Cole, M. H., & Beard, A. (2015). The Use of Wearable Microsensors to Quantify Sport-Specific Movements. Sports medicine (Auckland, N.Z.), 45(7), 1065 1081. https://doi.org/10.1007/s40279-015-0332-9 Search in Google Scholar

Chandrashekar, G. & Sahin, F. (2014) A Survey on Feature Selection Methods. Computers and Electrical Engineering, 40(1), 16-28. https://doi.org/10.1016/j.compeleceng.2013.11.024 Search in Google Scholar

Chau, A.L., Li, X., & Yu, W. (2014). Support vector machine classification for large datasets using decision tree and Fisher linear discriminant. Future Generation Computer Systems, 36, 57-65. Search in Google Scholar

Chaves, E., Barontini, A., Mendes, N., Compán, V., & Lourenço, P. B. (2023). Methodologies and challenges for optimal sensor placement in historical masonry buildings. Sensors, 23(23), 9304. https://doi.org/10.3390/s23239304 Search in Google Scholar

Chen, R.C., Dewi, C., Huang, S., & Caraka, R.E. (2020). Selecting critical features for data classification based on machine learning methods. Journal of Big Data, 7, 1-26. https://doi.org/10.1186/s40537-020-00327-4 Search in Google Scholar

Chmait, N., & Westerbeek, H. (2021). Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists. Frontiers in sports and active living, 3, 682287. https://doi.org/10.3389/fspor.2021.682287 Search in Google Scholar

Chong, J., Tjurin, P., Niemelä, M., Jämsä, T., & Farrahi, V. (2021). Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms. Gait & Posture, 89, 45-53. https://doi.org/10.1016/j.gaitpost.2021.06.017 Search in Google Scholar

Claudino, J. G., Capanema, D. O., de Souza, T. V., Serrão, J. C., Machado Pereira, A. C., & Nassis, G. P. (2019). Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review. Sports medicine - open, 5(1), 28. https://doi.org/10.1186/s40798-019-0202-3 Search in Google Scholar

Colyer, S. L., Evans, M., Cosker, D. P., & Salo, A. (2018). A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System. Sports medicine - open, 4(1), 24. https://doi.org/10.1186/s40798-018-0139-y Search in Google Scholar

Crenna, F., Rossi, G. B., & Berardengo, M. (2021). Filtering Biomechanical Signals in Movement Analysis. Sensors, 21(13), 4580. https://doi.org/10.3390/s21134580 Search in Google Scholar

Currell, K., & Jeukendrup, A. E. (2008). Validity, reliability and sensitivity of measures of sporting performance. Sports medicine (Auckland, N.Z.), 38(4), 297 316. https://doi.org/10.2165/00007256-200838040-00003 Search in Google Scholar

Cust, E. E., Sweeting, A. J., Ball, K., & Robertson, S. (2019). Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance. Journal of sports sciences, 37(5), 568 600. https://doi.org/10.1080/02640414.2018.1521769 Search in Google Scholar

Cust, E. E., Sweeting, A. J., Ball, K., & Robertson, S. (2021). Classification of Australian football kick types in-situation via ankle-mounted inertial measurement units. Journal of sports sciences, 39(12), 1330 1338. https://doi.org/10.1080/02640414.2020.1868678 Search in Google Scholar

Dargie, W. (2009). Analysis of Time and Frequency Domain Features of Accelerometer Measurements. In 2009 Proceedings of 18th International Conference on Computer Communications and Networks (pp. 1-6). San Francisco, CA, USA. https://doi.org/10.1109/ICCCN.2009.5235366 Search in Google Scholar

Das Antar, A., Ahmed, M., & Ahad, M. A. R. (2019). Challenges in Sensor-based Human Activity Recognition and a Comparative Analysis of Benchmark Datasets: A Review. In 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR) (pp. 134-139). Spokane, WA, USA. https://doi.org/10.1109/ICIEV.2019.8858508 Search in Google Scholar

Davila, J. C., Cretu, A. M., & Zaremba, M. (2017). Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework. Sensors, 17(6), 1287. https://doi.org/10.3390/s17061287 Search in Google Scholar

de Cheveigné, A., & Nelken, I. (2019). Filters: When, Why, and How (Not) to Use Them. Neuron, 102(2), 280-293. https://doi.org/10.1016/j.neuron.2019.02.039 Search in Google Scholar

Dehghani, A., Glatard, T., & Shihab, E. (2019). Subject Cross Validation in Human Activity Recognition. ArXiv, abs/1904.02666. https://doi.org/10.48550/arXiv.1904.02666 Search in Google Scholar

Dwyer, D. B., Kempe, M., & Knobbe, A. (2022). Editorial: Using Artificial Intelligence to Enhance Sport Performance. Frontiers in sports and active living, 4, 886730. https://doi.org/10.3389/fspor.2022.886730 Search in Google Scholar

Elgeldawi, E., Sayed, A., Galal, A. R., & Zaki, A. M. (2021). Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis. Informatics, 8(4), 79. https://doi.org/10.3390/informatics8040079 Search in Google Scholar

Erdaş, Ç. B., Atasoy, I., Açıcı, K., & Oğul, H. (2016). Integrating Features for Accelerometer-based Activity Recognition. Procedia Computer Science, 98, 522-527. https://doi.org/10.1016/j.procs.2016.09.070 Search in Google Scholar

Fan, J., Bi, S., Wang, G., Zhang, L., & Sun, S. (2021). Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN. Journal of Sensors, 1-16. https://doi.org/10.1155/2021/6664776 Search in Google Scholar

Ganser, A., Hollaus, B., & Stabinger, S. (2021). Classification of Tennis Shots with a Neural Network Approach. Sensors (Basel, Switzerland), 21(17), 5703. https://doi.org/10.3390/s21175703 Search in Google Scholar

Gençoğlu, C., & Gümüş, H. (2020). Standing Handball Throwing Velocity Estimation with a Single Wrist-Mounted Inertial Sensor. Annals of Applied Sport Science, 8, 0-0. https://doi.org/10.29252/AASSJOURNAL.893 Search in Google Scholar

Gómez-Carmona, C.D., Rojas-Valverde, D., Rico-González, M., Ibáñez, S.J., & Pino-Ortega, J. (2020). What is the most suitable sampling frequency to register accelerometry-based workload? A case study in soccer. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 235, 114 - 121. https://doi.org/10.1177/1754337120972516 Search in Google Scholar

Gjoreski, H., & Gams, M. (2011). Accelerometer data preparation for activity recognition. In Proceedings of the International Multiconference Information Society, Ljubljana, Slovenia, 1014. Search in Google Scholar

Gupta, N., Gupta, S. K., Pathak, R. K., Jain, V., Rashidi, P., & Suri, J. S. (2022). Human activity recognition in artificial intelligence framework: a narrative review. Artificial intelligence review, 55, 4755 4808. https://doi.org/10.1007/s10462-021-10116-x Search in Google Scholar

Habibi Aghdam, H., Jahani Heravi, E. (2017). Convolutional Neural Networks. In: Guide to Convolutional Neural Networks. 85-130. Springer, Cham. https://doi.org/10.1007/978-3-319-57550-6_3 Search in Google Scholar

Halilaj, E., Rajagopal, A., Fiterau, M., Hicks, J. L., Hastie, T. J., & Delp, S. L. (2018). Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities. Journal of biomechanics, 81, 1 11. https://doi.org/10.1016/j.jbiomech.2018.09.009 Search in Google Scholar

Hribernik, M., Umek, A., Tomažič, S., & Kos, A. (2022). Review of Real-Time Biomechanical Feedback Systems in Sport and Rehabilitation. Sensors, 22(8), 3006. https://doi.org/10.3390/s22083006 Search in Google Scholar

Host, K., & Ivašic-Kos, M. (2022). An overview of Human Action Recognition in sports based on Computer Vision. Heliyon, 8(6), e09633. https://doi.org/10.1016/j.heliyon.2022.e09633 Search in Google Scholar

Hossin, M., & Sulaiman, M. N. (2015). A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 01-11. https://doi.org/10.5121/ijdkp.2015.5201 Search in Google Scholar

Hollaus, B., Stabinger, S., Mehrle, A., & Raschner, C. (2020). Using Wearable Sensors and a Convolutional Neural Network for Catch Detection in American Football. Sensors (Basel, Switzerland), 20(23), 6722. https://doi.org/10.3390/s20236722 Search in Google Scholar

Islam, M. M., Nooruddin, S., Karray, F., & Muhammad, G. (2022). Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects. Computers in Biology and Medicine, 149. https://doi.org/10.1016/j.compbiomed.2022.106060 Search in Google Scholar

Jalal, A., Quaid, M. A. K., Tahir, S. B. u. d., & Kim, K. (2020). A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems. Sensors (Basel, Switzerland), 20(22), 6670. https://doi.org/10.3390/s20226670 Search in Google Scholar

Jeni, L. A., Cohn, J. F., & De La Torre, F. (2013). Facing Imbalanced Data Recommendations for the Use of Performance Metrics. International Conference on Affective Computing and Intelligent Interaction and workshops : [proceedings]. ACII (Conference), 2013, 245 251. https://doi.org/10.1109/ACII.2013.47 Search in Google Scholar

Jha, A., Dave, M., & Madan, S. (2019) Comparison of Binary Class and Multi-Class Classifier Using Different Data Mining Classification Techniques, Proceedings of International Conference on Advancements in Computing & Management (ICACM) 2019. http://doi.org/10.2139/ssrn.3464211 Search in Google Scholar

Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202 Search in Google Scholar

Jowitt, H. K., Durussel, J., Brandon, R., & King, M. (2020). Auto detecting deliveries in elite cricket fast bowlers using microsensors and machine learning. Journal of sports sciences, 38(7), 767 772. https://doi.org/10.1080/02640414.2020.1734308 Search in Google Scholar

Jung, A. (2022) Machine Learning: The Basics, (1^st ed) Springer, Singapore (Chapter 1). Search in Google Scholar

Kautz, T., Groh, B.H., Hannink, J., Jensen, U., Strubberg, H., & Eskofier, B.M. (2017). Activity recognition in beach volleyball using a Deep Convolutional Neural Network. Data Mining and Knowledge Discovery, 31, 1678 - 1705. https://doi.org/10.1007/s10618-017-0495-0 Search in Google Scholar

Khan, A., Nicholson, J., & Plötz, T. (2017). Activity Recognition for Quality Assessment of Batting Shots in Cricket using a Hierarchical Representation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(3), 1 - 31. https://doi.org/10.1145/3130927 Search in Google Scholar

Khan, A., Azal, M., Chaudhari, O., & Chandra, R. (2024). A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation. Expert Systems with Applications, 244, 122778. https://doi.org/10.1016/j.eswa.2023.122778 Search in Google Scholar

Kim, M., & Park, S. (2020). Golf Swing Segmentation from a Single IMU Using Machine Learning. Sensors, 20(16):4466. https://doi.org/10.3390/s20164466 Search in Google Scholar

Kim, W., & Kim, M. (2017). Sports motion analysis system using wearable sensors and video cameras. 2017 International Conference on Information and Communication Technology Convergence (ICTC), 1089-1091. https://doi.org/10.1109/ICTC.2017.8190863 Search in Google Scholar

Kok, M., Hol, J., & Schön, T. (2017). Using Inertial Sensors for Position and Orientation Estimation. Foundations and Trends in Signal Processing, 11(1-2),1-153. https://doi.org/10.1561/9781680833577 Search in Google Scholar

Komang, M.G., Surya, M.N., & Ratna, A.N. (2019). Human activity recognition using skeleton data and support vector machine. Journal of Physics: Conference Series, 1192. https://doi.org/10.1088/1742-6596/1192/1/012044 Search in Google Scholar

Kulsoom, F., Narejo, S., Mehmood, Z., Chaudhry, H. N., Butt, A., & Bashir, A. K. (2022). A review of machine learning-based human activity recognition for diverse applications. Neural Computing & Applications, 34, 18289 18324. https://doi.org/10.1007/s00521-022-07665-9 Search in Google Scholar

Lara, O. D., & Labrador, M. A. (2013). A Survey on Human Activity Recognition using Wearable Sensors. IEEE Communications Surveys & Tutorials, 15(3), 1192-1209. https://doi.org/10.1109/SURV.2012.110112.00192 Search in Google Scholar

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS medicine, 6(7), e1000100. https://doi.org/10.1371/journal.pmed.1000100 Search in Google Scholar

Macadam, P., Cronin, J., Neville, J., & Diewald, S. (2019). Quantification of the validity and reliability of sprint performance metrics computed using inertial sensors: A systematic review. Gait & posture, 73, 26 38. https://doi.org/10.1016/j.gaitpost.2019.07.123 Search in Google Scholar

Mathis, A., Schneider, S., Lauer, J., & Mathis, M. W. (2020). A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives. Neuron, 108(1), 44 65. https://doi.org/10.1016/j.neuron.2020.09.017 Search in Google Scholar

Matsumoto, K., Tsujiuchi, N., Ito, A., Kobayashi, H., Ueda, M., & Okazaki, K. (2020). Proposal of Golf Swing Analysis Method Using Singular Value Decomposition. Proceedings, 49(1), 91. https://doi.org/10.3390/proceedings2020049091 Search in Google Scholar

McGrath, J., Neville, J., Stewart, T., Clinning H. & Cronin, J. (2021). Can an inertial measurement unit (IMU) in combination with machine learning measure fast bowling speed and perceived intensity in cricket? Journal of Sports Sciences, 39(12), 1402-1409. https://doi.org/10.1080/02640414.2021.1876312 Search in Google Scholar

McGrath, J.W., Neville, J., Stewart, T. & Cronin., J. (2019). Cricket fast bowling detection in a training setting using an inertial measurement unit and machine learning, Journal of Sports Sciences, 37(11), 1220-1226. https://doi.org/10.1080/02640414.2018.1553270 Search in Google Scholar

Misra, S., & Li, H. (2020). Chapter 9 - Noninvasive fracture characterization based on the classification of sonic wave travel times. In S. Misra, H. Li, & J. He (Eds.), Machine Learning for Subsurface Characterization (pp. 243-287). Gulf Professional Publishing. ISBN 9780128177365. https://doi.org/10.1016/B978-0-12-817736-5.00009-0 Search in Google Scholar

Mitchell, E., Monaghan, D., & O’Connor, N. E. (2013). Classification of sporting activities using smartphone accelerometers. Sensors (Basel, Switzerland), 13(4), 5317 5337. https://doi.org/10.3390/s130405317 Search in Google Scholar

Moran, K., Ahmadi, A., Richter, C., Mitchell, E., Kavanagh, J., O’Connor, N. (2015). Automatic Detection, Extraction, and Analysis of Landing During a Training Session, Using a Wearable Sensor System. Procedia Engineering, 112, 184-189. https://doi.org/10.1016/j.proeng.2015.07.197 Search in Google Scholar

Müller, M. (2007). Dynamic Time Warping. In R. Baeza-Yates & B. Ribeiro-Neto (Eds.), Information Retrieval for Music and Motion (pp. 69 - 84). https://doi.org/10.1007/978-3-540-74048-3_4 Search in Google Scholar

O’Reilly, M.A., Johnston, W., Buckley, C., Whelan, D.F., & Caulfield, B.M. (2017). The influence of feature selection methods on exercise classification with inertial measurement units. 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 193-196. https://doi.org/10.1109/BSN.2017.7936039 Search in Google Scholar

Pardakhti, M., Mandal, N., Ma, A.W., & Yang, Q. (2021). Practical Active Learning with Model Selection for Small Data. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 1647-1653. https://doi.org/10.1109/ICMLA52953.2021.00263 Search in Google Scholar

Preatoni, E., Nodari, S., & Lopomo, N. F. (2020). Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout. Frontiers in bioengineering and biotechnology, 8, 664. https://doi.org/10.3389/fbioe.2020.00664 Search in Google Scholar

Preece, S. J., Goulermas, J. Y., Kenney, L. P., & Howard, D. (2009). A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE transactions on bio-medical engineering, 56(3), 871 879. https://doi.org/10.1109/TBME.2008.2006190 Search in Google Scholar

Raj, R., & Kos, A. (2023). An improved human activity recognition technique based on convolutional neural network. Scientific Reports, 13, 22581. https://doi.org/10.1038/s41598-023-49739-1 Search in Google Scholar

Ramamurthy, S.R., & Roy, N. (2018). Recent trends in machine learning for human activity recognition A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8. https://doi.org/10.1002/widm.1254 Search in Google Scholar

Rana, M. & Mittal, V (2021). Wearable Sensors for Real-Time Kinematics Analysis in Sports: A Review, in IEEE Sensors Journal, 21(2), 1187-1207. https://doi.org/10.1109/JSEN.2020.3019016 Search in Google Scholar

Ray, S., Alshouiliy, K., & Agrawal, D. P. (2021). Dimensionality Reduction for Human Activity Recognition Using Google Colab. Information, 12(1), 6. https://doi.org/10.3390/info12010006 Search in Google Scholar

Refaeilzadeh, P., Tang, L., Liu, H. (2009). Cross-Validation. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_565 Search in Google Scholar

Reilly, B., Morgan, O, Czanner, G. & Robinson, M.A. (2021). Automated Classification of Changes of Direction in Soccer Using Inertial Measurement Units. Sensors, 21(14), 4625. https://doi.org/10.3390/s21144625 Search in Google Scholar

Richter, C., O’Reilly, M., & Delahunt, E. (2021). Machine learning in sports science: challenges and opportunities. Sports biomechanics, 1 7. Advance online publication. https://doi.org/10.1080/14763141.2021.1910334 Search in Google Scholar

Roggio, F., Ravalli, S., Maugeri, G., Bianco, A., Palma, A., Di Rosa, M., & Musumeci, G. (2021). Technological advancements in the analysis of human motion and posture management through digital devices. World Journal of Orthopedics, 12(7), 467 484. https://doi.org/10.5312/wjo.v12.i7.467 Search in Google Scholar

Rosati, S., Balestra, G., & Knaflitz, M. (2018). Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors. Sensors (Basel, Switzerland), 18(12), 4189. https://doi.org/10.3390/s18124189 Search in Google Scholar

Salim, F. A., Haider, F., Postma, D. B. W., van Delden, R., Reidsma, D., Luz, S., & van Beijnum, B. J. F. (2020). Towards Automatic Modeling of Volleyball Players’ Behavior for Analysis, Feedback, and Hybrid Training. Journal for the Measurement of Physical Behaviour, 3(4), 323-330. https://doi.org/10.1123/jmpb.2020-0012 Search in Google Scholar

Salman, M., Qaisar, S.B., & Qamar, A.M. (2017). Classification and legality analysis of bowling action in the game of cricket. Data Mining and Knowledge Discovery, 31, 1706-1734. https://doi.org/10.1007/s10618-017-0511-4 Search in Google Scholar

Schwarz, G. (1978). Estimating the Dimensions of a Model. The Annals of Statistics, 6(2), 461-464. Search in Google Scholar

Serpush, F., Menhaj, M. B., Masoumi, B., & Karasfi, B. (2022). Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System. Computational intelligence and neuroscience, 2022, 1391906. https://doi.org/10.1155/2022/1391906 Search in Google Scholar

Shahar, N., Ghazali, N. F., As’ari, M. A., & Swee, T. T. (2020). Wearable inertial sensor for human activity recognition in field hockey: Influence of sensor combination and sensor location. In: 2nd Joint International Conference on Emerging Computing Technology and Sports, JICETS 2019, 25 - 27 November 2019, Bandung, Indonesia. https://doi.org/10.1088/1742-6596/1529/2/022015 Search in Google Scholar

Smith, P., & Bedford, A. (2020). Automatic Classification of Locomotion in Sport: A Case Study from Elite Netball. International Journal of Computer Science in Sport, 19(2), 1-20. https://doi.org/10.2478/ijcss-2020-0007 Search in Google Scholar

Steels, T., Van Herbruggen, B., Fontaine, J., De Pessemier, T., Plets, D., & De Poorter, E. (2020). Badminton Activity Recognition Using Accelerometer Data. Sensors, 20(17), 4685. https://doi.org/10.3390/s20174685 Search in Google Scholar

Szeghalmy, S., & Fazekas, A. (2023). A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning. Sensors, 23(4), 2333. https://doi.org/10.3390/s23042333 Search in Google Scholar

Tan, F., & Xie, X. (2021). Recognition Technology of Athlete’s Limb Movement Combined Based on the Integrated Learning Algorithm. Journal of Sensors, 2021, 1-9. https://doi.org/10.1155/2021/3057557 Search in Google Scholar

Tran, A., Guan, J., Pilantanakitti, T., & Cohen, P.R. (2014). Action Recognition in the Frequency Domain. ArXiv, abs/1409.0908. https://doi.org/10.48550/arXiv.1409.0908 Search in Google Scholar

Uçar, M. K., Nour, M., Sindi, H., & Polat, K. (2020). The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets. Mathematical Problems in Engineering, 2020, 2836236. https://doi.org/10.1155/2020/2836236 Search in Google Scholar

Vaibhaw, Sarraf, J., & Pattnaik, P.K. (2020). Chapter 2 - Brain computer interfaces and their applications. In V.E. Balas, V.K. Solanki, & R. Kumar (Eds.), An Industrial IoT Approach for Pharmaceutical Industry Growth (pp. 31-54). Academic Press. ISBN 9780128213261. https://doi.org/10.1016/B978-0-12-821326-1.00002-4 Search in Google Scholar

Vakili, M., Ghamsari, M.K., & Rezaei, M. (2020). Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification. ArXiv, abs/2001.09636. https://doi.org/10.48550/arXiv.2001.09636 Search in Google Scholar

van den Tillaar, R., Bhandurge, S., & Stewart, T. (2021). Can Machine Learning with IMUs Be Used to Detect Different Throws and Estimate Ball Velocity in Team Handball? Sensors, 21(7), 2288. https://doi.org/10.3390/s21072288 Search in Google Scholar

van der Kruk., E, & Reijne., M. M. (2018) Accuracy of human motion capture systems for sport applications; state-of-the-art review. European Journal of Sport Science, 18(6), 806-819. https://doi.org/10.1080/17461391.2018.1463397 Search in Google Scholar

Van Eetvelde, H., Mendonça, L. D., Ley, C., Seil, R., & Tischer, T. (2021). Machine learning methods in sport injury prediction and prevention: a systematic review. Journal of experimental orthopaedics, 8(1), 27. https://doi.org/10.1186/s40634-021-00346-x Search in Google Scholar

Vrigkas, M., Nikou, C., & Kakadiaris, I. A. (2015). A Review of Human Activity Recognition Methods. Frontiers in Robotics and AI, 2(28). https://doi.org/10.3389/frobt.2015.00028 Search in Google Scholar

Wang, Z., Guo, M., & Zhao, C. (2016). Badminton stroke recognition based on body sensor networks. IEEE Transactions on Human-Machine Systems, 46(5), 769-775. https://doi.org/10.1109/THMS.2016.2571265 Search in Google Scholar

Wang, Y., Zhao, Y., Chan, R.H., & Li, W.J. (2018). Volleyball Skill Assessment Using a Single Wearable Micro Inertial Measurement Unit at Wrist. IEEE Access, 6, 13758-13765. https://doi.org/10.1109/ACCESS.2018.2792220 Search in Google Scholar

Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019). Deep Learning for Sensor-based Activity Recognition: A Survey. ArXiv, abs/1707.03502. https://doi.org/10.1016/j.patrec.2018.02.010 Search in Google Scholar

Whiteside, D., Cant, O., Connolly, M., & Reid, M. (2017). Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning. International journal of sports physiology and performance, 12(9), 1212 1217. https://doi.org/10.1123/ijspp.2016-0683 Search in Google Scholar

Wickramasinghe, I. (2022). Applications of Machine Learning in cricket: A systematic review. Machine Learning with Applications, 10, 10043510. https://doi.org/10.1016/J.MLWA.2022.100435 Search in Google Scholar

Wohlin, C. (2014). Guidelines for Snowballing in Systematic Literature Studies and a Replication in Software Engineering. in Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering. London, England, United Kingdom: Association for Computing Machinery, 1 10. https://doi.org/10.1145/2601248.2601268 Search in Google Scholar

Wolpert, D. H., & Macready, W.G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82. https://doi.org/10.1109/4235.585893 Search in Google Scholar

Worsey, M. T., Espinosa, H. G., Shepherd, J. B., & Thiel, D. V. (2019). Inertial Sensors for Performance Analysis in Combat Sports: A Systematic Review. Sports (Basel, Switzerland), 7(1), 28. https://doi.org/10.3390/sports7010028 Search in Google Scholar

Yeo, S. S., & Park, G. Y. (2020). Accuracy Verification of Spatio-Temporal and Kinematic Parameters for Gait Using Inertial Measurement Unit System. Sensors (Basel, Switzerland), 20(5), 1343. https://doi.org/10.3390/s20051343 Search in Google Scholar

Yin, C., Chen, J., Miao, X., Jiang, H., & Chen, D. (2021). Device-Free Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Long Short-Term Memory Neural Network. Sensors, 21(10), 3551. https://doi.org/10.3390/s21103551 Search in Google Scholar

Zecha, D., Einfalt, M., Eggert C. & Lienhart, R. (2018) Kinematic Pose Rectification for Performance Analysis and Retrieval in Sports, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1872-18728. https://doi.org/10.1109/CVPRW.2018.00232 Search in Google Scholar

Zhang, B., Lyu, M., Zhang, L. & Yang. W. (2021). Artificial Intelligence-Based Joint Movement Estimation Method for Football Players in Sports Training. Mobile Information Systems, Vol. 2021. https://doi.org/10.1155/2021/9956482 Search in Google Scholar

Zhou, L., Fischer, E., Tunca, C., Brahms, C.M., Ersoy, C., Granacher, U., & Arnrich, B. (2020). How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications. Sensors, 20(15), 4090. https://doi.org/10.3390/s20154090 Search in Google Scholar

Zhu, J., San-Segundo, R. & Pardo, J. (2017). Feature extraction for robust physical activity recognition. Human-centric Computing and Information Sciences, 7(1). https://doi.org/10.1186/s13673-017-0097-2 Search in Google Scholar