{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T03:03:23Z","timestamp":1779851003445,"version":"3.53.1"},"reference-count":124,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>There is an ever-present need to objectively measure and analyze sports motion for the determination of correct patterns of motion for skill execution. Developments in performance analysis technologies such as inertial measuring units (IMUs) have resulted in enormous data generation. However, these advances present challenges in analysis, interpretation, and transformation of data into useful information. Artificial intelligence (AI) systems can process and analyze large amounts of data quickly and efficiently through classification techniques. This study aimed to systematically review the literature on Machine Learning (ML) and Deep Learning (DL) methods applied to IMU data inputs for evaluating techniques or skills in individual swing and team sports. Electronic database searches (IEEE Xplore, PubMed, Scopus, and Google Scholar) were conducted and aligned with the PRISMA statement and guidelines. A total of 26 articles were included in the review. The Support Vector Machine (SVM) was identified as the most utilized model, as per 7 studies. A deep learning approach was reported in 6 studies, in the form of a Convolutional Neural Network (CNN) architecture. The in-depth analysis highlighted varying methodologies across all sports inclusive of device specifications, data preprocessing techniques and model performance evaluation. This review highlights that each step of the ML modeling process is iterative and should be based on the specific characteristics of the movement being analyzed.<\/jats:p>","DOI":"10.2478\/ijcss-2024-0007","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T06:00:54Z","timestamp":1717567254000},"page":"110-145","source":"Crossref","is-referenced-by-count":4,"title":["The application of Machine and Deep Learning for technique and skill analysis in swing and team sport-specific movement: A systematic review"],"prefix":"10.2478","volume":"23","author":[{"given":"Chloe","family":"Leddy","sequence":"first","affiliation":[{"name":"South East Technological University (Kilkenny Road Campus) , Department of Aerospace & Mechanical Engineering , Kilkenny Rd , Carlow"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Bolger","sequence":"additional","affiliation":[{"name":"South East Technological University (Cork Road Campus) , Department of Sport & Exercise Science , Waterford , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul J.","family":"Byrne","sequence":"additional","affiliation":[{"name":"South East Technological University (Kilkenny Road Campus) , Department of Health & Sport Sciences , Kilkenny Road, Carlow , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharon","family":"Kinsella","sequence":"additional","affiliation":[{"name":"South East Technological University (Kilkenny Road Campus) , Department of Health & Sport Sciences , Kilkenny Road, Carlow , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lilibeth","family":"Zambrano","sequence":"additional","affiliation":[{"name":"South East Technological University (Kilkenny Road Campus) , Department of Aerospace & Mechanical Engineering , Kilkenny Rd , Carlow"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"2026052702271348017_j_ijcss-2024-0007_ref_001","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/JIOT.2020.3033430"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_002","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.12720\/ijsps.1.2.256-262"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_003","doi-asserted-by":"crossref","unstructured":"Ahmadi, A., Mitchell, E., & Destelle, F., Gowing, M., O\u2019Connor, 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","DOI":"10.1109\/BSN.2014.29"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_004","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1007\/978-1-4612-0919-5_38"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_005","doi-asserted-by":"crossref","unstructured":"Alanen, A., R\u00e4is\u00e4nen, 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","DOI":"10.1177\/17479541211003064"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_006","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/B978-0-12-814482-4.00009-7"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_007","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1088\/1742-6596\/1142\/1\/012012"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_008","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ICMLA.2017.0-149"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_009","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/app11188473"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_010","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ICCSCE.2014.7072750"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_011","doi-asserted-by":"crossref","unstructured":"Aresta, S., Bortone, I., Bottiglione, F., Di Noia, T., Di Sciascio, E., Lof\u00f9, D., Musci, M., Narducci, F., Pazienza, A., Sardone, R., & Sorino, P. (2022). Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers\u2019 Levels for Training Support. Applied Sciences, 12(23), 12350. https:\/\/doi.org\/10.3390\/app122312350","DOI":"10.3390\/app122312350"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_012","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s22218370"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_013","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s140406474"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_014","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1007\/s42835-020-00554-y"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_015","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1155\/2022\/6820073"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_016","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1155\/2018\/3426178"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_017","doi-asserted-by":"crossref","unstructured":"Bragan\u00e7a, 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","DOI":"10.3390\/s22062360"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_018","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1155\/2018\/8180296"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_019","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1145\/2499621"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_020","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s18030873"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_021","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.mlwa.2021.100134"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_022","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1007\/s40279-015-0332-9"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_023","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.compeleceng.2013.11.024"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_024","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1016\/j.future.2013.06.021"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_025","doi-asserted-by":"crossref","unstructured":"Chaves, E., Barontini, A., Mendes, N., Comp\u00e1n, V., & Louren\u00e7o, P. B. (2023). Methodologies and challenges for optimal sensor placement in historical masonry buildings. Sensors, 23(23), 9304. https:\/\/doi.org\/10.3390\/s23239304","DOI":"10.3390\/s23239304"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_026","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1186\/s40537-020-00327-4"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_027","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3389\/fspor.2021.682287"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_028","doi-asserted-by":"crossref","unstructured":"Chong, J., Tjurin, P., Niemel\u00e4, M., J\u00e4ms\u00e4, 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","DOI":"10.1016\/j.gaitpost.2021.06.017"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_029","doi-asserted-by":"crossref","unstructured":"Claudino, J. G., Capanema, D. O., de Souza, T. V., Serr\u00e3o, 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","DOI":"10.1186\/s40798-019-0202-3"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_030","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1186\/s40798-018-0139-y"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_031","doi-asserted-by":"crossref","unstructured":"Crenna, F., Rossi, G. B., & Berardengo, M. (2021). Filtering Biomechanical Signals in Movement Analysis. Sensors, 21(13), 4580. https:\/\/doi.org\/10.3390\/s21134580","DOI":"10.3390\/s21134580"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_032","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.2165\/00007256-200838040-00003"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_033","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1080\/02640414.2018.1521769"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_034","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1080\/02640414.2020.1868678"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_035","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ICCCN.2009.5235366"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_036","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ICIEV.2019.8858508"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_037","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s17061287"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_038","doi-asserted-by":"crossref","unstructured":"de Cheveign\u00e9, 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","DOI":"10.1016\/j.neuron.2019.02.039"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_039","unstructured":"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"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_040","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3389\/fspor.2022.886730"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_041","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/informatics8040079"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_042","doi-asserted-by":"crossref","unstructured":"Erda\u015f, \u00c7. B., Atasoy, I., A\u00e7\u0131c\u0131, K., & O\u011ful, 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","DOI":"10.1016\/j.procs.2016.09.070"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_043","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1155\/2021\/6664776"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_044","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s21175703"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_045","doi-asserted-by":"crossref","unstructured":"Gen\u00e7o\u011flu, C., & G\u00fcm\u00fc\u015f, 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","DOI":"10.29252\/aassjournal.893"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_046","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Carmona, C.D., Rojas-Valverde, D., Rico-Gonz\u00e1lez, M., Ib\u00e1\u00f1ez, 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","DOI":"10.1177\/1754337120972516"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_047","unstructured":"Gjoreski, H., & Gams, M. (2011). Accelerometer data preparation for activity recognition. In Proceedings of the International Multiconference Information Society, Ljubljana, Slovenia, 1014."},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_048","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1007\/s10462-021-10116-x"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_049","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1007\/978-3-319-57550-6_3"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_050","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.jbiomech.2018.09.009"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_051","doi-asserted-by":"crossref","unstructured":"Hribernik, M., Umek, A., Toma\u017ei\u010d, 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","DOI":"10.3390\/s22083006"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_052","doi-asserted-by":"crossref","unstructured":"Host, K., & Iva\u0161ic-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","DOI":"10.1016\/j.heliyon.2022.e09633"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_053","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.5121\/ijdkp.2015.5201"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_054","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s20236722"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_055","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.compbiomed.2022.106060"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_056","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s20226670"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_057","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ACII.2013.47"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_058","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.2139\/ssrn.3464211"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_059","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1098\/rsta.2015.0202"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_060","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1080\/02640414.2020.1734308"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_061","doi-asserted-by":"crossref","unstructured":"Jung, A. (2022) Machine Learning: The Basics, (1st ed) Springer, Singapore (Chapter 1).","DOI":"10.1007\/978-981-16-8193-6_1"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_062","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1007\/s10618-017-0495-0"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_063","doi-asserted-by":"crossref","unstructured":"Khan, A., Nicholson, J., & Pl\u00f6tz, 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","DOI":"10.1145\/3130927"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_064","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.eswa.2023.122778"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_065","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s20164466"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_066","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ICTC.2017.8190863"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_067","doi-asserted-by":"crossref","unstructured":"Kok, M., Hol, J., & Sch\u00f6n, 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","DOI":"10.1561\/2000000094"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_068","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1088\/1742-6596\/1192\/1\/012044"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_069","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1007\/s00521-022-07665-9"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_070","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/SURV.2012.110112.00192"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_071","doi-asserted-by":"crossref","unstructured":"Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., G\u00f8tzsche, 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","DOI":"10.1371\/journal.pmed.1000100"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_072","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.gaitpost.2019.07.123"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_073","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.neuron.2020.09.017"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_074","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/proceedings2020049091"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_075","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1080\/02640414.2021.1876312"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_076","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1080\/02640414.2018.1553270"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_077","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/B978-0-12-817736-5.00009-0"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_078","doi-asserted-by":"crossref","unstructured":"Mitchell, E., Monaghan, D., & O\u2019Connor, N. E. (2013). Classification of sporting activities using smartphone accelerometers. Sensors (Basel, Switzerland), 13(4), 5317 5337. https:\/\/doi.org\/10.3390\/s130405317","DOI":"10.3390\/s130405317"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_079","doi-asserted-by":"crossref","unstructured":"Moran, K., Ahmadi, A., Richter, C., Mitchell, E., Kavanagh, J., O\u2019Connor, 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","DOI":"10.1016\/j.proeng.2015.07.197"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_080","doi-asserted-by":"crossref","unstructured":"M\u00fcller, 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","DOI":"10.1007\/978-3-540-74048-3_4"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_081","doi-asserted-by":"crossref","unstructured":"O\u2019Reilly, 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","DOI":"10.1109\/BSN.2017.7936039"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_082","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ICMLA52953.2021.00263"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_083","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3389\/fbioe.2020.00664"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_084","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/TBME.2008.2006190"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_085","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1038\/s41598-023-49739-1"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_086","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1002\/widm.1254"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_087","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/JSEN.2020.3019016"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_088","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/info12010006"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_089","doi-asserted-by":"crossref","unstructured":"Refaeilzadeh, P., Tang, L., Liu, H. (2009). Cross-Validation. In: LIU, L., \u00d6ZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https:\/\/doi.org\/10.1007\/978-0-387-39940-9_565","DOI":"10.1007\/978-0-387-39940-9_565"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_090","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s21144625"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_091","doi-asserted-by":"crossref","unstructured":"Richter, C., O\u2019Reilly, 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","DOI":"10.1080\/14763141.2021.1910334"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_092","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.5312\/wjo.v12.i7.467"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_093","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s18124189"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_094","doi-asserted-by":"crossref","unstructured":"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\u2019 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","DOI":"10.1123\/jmpb.2020-0012"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_095","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1007\/s10618-017-0511-4"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_096","doi-asserted-by":"crossref","unstructured":"Schwarz, G. (1978). Estimating the Dimensions of a Model. The Annals of Statistics, 6(2), 461-464.","DOI":"10.1214\/aos\/1176344136"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_097","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1155\/2022\/1391906"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_098","doi-asserted-by":"crossref","unstructured":"Shahar, N., Ghazali, N. F., As\u2019ari, 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","DOI":"10.1088\/1742-6596\/1529\/2\/022015"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_099","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.2478\/ijcss-2020-0007"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_100","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s20174685"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_101","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s23042333"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_102","doi-asserted-by":"crossref","unstructured":"Tan, F., & Xie, X. (2021). Recognition Technology of Athlete\u2019s Limb Movement Combined Based on the Integrated Learning Algorithm. Journal of Sensors, 2021, 1-9. https:\/\/doi.org\/10.1155\/2021\/3057557","DOI":"10.1155\/2021\/3057557"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_103","unstructured":"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"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_104","doi-asserted-by":"crossref","unstructured":"U\u00e7ar, 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","DOI":"10.1155\/2020\/2836236"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_105","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/B978-0-12-821326-1.00002-4"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_106","unstructured":"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"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_107","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s21072288"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_108","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1080\/17461391.2018.1463397"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_109","doi-asserted-by":"crossref","unstructured":"Van Eetvelde, H., Mendon\u00e7a, 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","DOI":"10.1186\/s40634-021-00346-x"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_110","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3389\/frobt.2015.00028"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_111","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/THMS.2016.2571265"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_112","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/ACCESS.2018.2792220"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_113","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.patrec.2018.02.010"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_114","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1123\/ijspp.2016-0683"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_115","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1016\/j.mlwa.2022.100435"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_116","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1145\/2601248.2601268"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_117","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/4235.585893"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_118","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/sports7010028"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_119","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s20051343"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_120","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s21103551"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_121","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/CVPRW.2018.00232"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_122","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1155\/2021\/9956482"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_123","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.3390\/s20154090"},{"key":"2026052702271348017_j_ijcss-2024-0007_ref_124","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1186\/s13673-017-0097-2"}],"container-title":["International Journal of Computer Science in Sport"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/reference-global.com\/pdf\/10.2478\/ijcss-2024-0007","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T02:27:53Z","timestamp":1779848873000},"score":1,"resource":{"primary":{"URL":"https:\/\/reference-global.com\/article\/10.2478\/ijcss-2024-0007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,1]]},"references-count":124,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,6,5]]},"published-print":{"date-parts":[[2024,2,1]]}},"alternative-id":["10.2478\/ijcss-2024-0007"],"URL":"https:\/\/doi.org\/10.2478\/ijcss-2024-0007","relation":{},"ISSN":["1684-4769"],"issn-type":[{"value":"1684-4769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,1]]}}}