{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T09:59:16Z","timestamp":1785319156697,"version":"3.55.0"},"reference-count":30,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In basketball motion recognition, there are problems such as difficulty in obtaining high-quality action data, difficulty in coping with poor discrimination accuracy due to scene factors such as movement of athletes, occlusion of equipment, and interference of spectators. Aiming at these problems, the study proposes to use density-based spatial clustering of applications with noise to construct a basketball motion recognition model. On the basis of this model, a joint visual-inertial sensor is introduced to collect the action data of basketball, and a hybrid Gaussian model and dynamic time warping algorithm are added to realize the recognition of single joints and multi-joints. The outcomes found that on the performance test, the proposed model of the study had the best overall performance with a loss rate of 1.02\u202f%, an accuracy rate of 97.02\u202f%, a recall rate of 95.26\u202f%, an F1 value of 95.17\u202f%, a discrimination time of 1.3\u202fs, and an error rate of 0.9\u202f%. In addition, in terms of motion trajectory quality, the joint vision-inertial sensor designed by the study had better quality of captured motion trajectory smoothness than the single type of sensor. In the example test, the proposed model could well recognize six basketball movements: dribbling ball, passing the ball, shooting, side step defense, crossing over, and dealing. The correct identification rate was no less than 90\u202f%, and the recognition time was no more than 2\u202fs. The clustering of features also showed clear clusters, and the model was able to correctly recognize the complex basketball scenarios. Taken together, the research model can be well applied to basketball motion recognition and promote the improvement of basketball players\u2019 action level.<\/jats:p>","DOI":"10.1515\/comp-2025-0059","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T09:03:54Z","timestamp":1785315834000},"source":"Crossref","is-referenced-by-count":0,"title":["Basketball motion recognition method based on\u00a0DBSCAN trajectory segmental clustering"],"prefix":"10.1515","volume":"16","author":[{"given":"Peng","family":"Guo","sequence":"first","affiliation":[{"name":"College of Physical Education, Jilin Normal University , Siping , 136000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxin","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Foreign Languages, Jilin Normal University , Siping , 136000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"2026072909034591426_j_comp-2025-0059_ref_001","doi-asserted-by":"crossref","unstructured":"J. Zhang, L. Lin, and J. Liu, \u201cHierarchical consistent contrastive learning for skeleton-based action recognition with growing augmentations,\u201d Proc. AAAI Conf. Artif. Intell., vol.\u00a037, no.\u00a03, pp.\u00a03427\u20133435, 2023, https:\/\/doi.org\/10.1609\/aaai.v37i3.25451.","DOI":"10.1609\/aaai.v37i3.25451"},{"key":"2026072909034591426_j_comp-2025-0059_ref_002","doi-asserted-by":"crossref","unstructured":"M. Batool et al.., \u201cDepth sensors-based action recognition using a modified K-ary entropy classifier,\u201d IEEE Access, vol.\u00a011, pp.\u00a058578\u201358595, 2023, https:\/\/doi.org\/10.1109\/access.2023.3260403.","DOI":"10.1109\/ACCESS.2023.3260403"},{"key":"2026072909034591426_j_comp-2025-0059_ref_003","doi-asserted-by":"crossref","unstructured":"L. Xiao, Y. Cao, Y. Gai, E. Khezri, J. Liu, and M. Yang, \u201cRecognizing sports activities from video frames using deformable convolution and adaptive multiscale features,\u201d J. Cloud Comput., vol.\u00a012, no.\u00a01, pp.\u00a0167\u2013168, 2023, https:\/\/doi.org\/10.1186\/s13677-023-00552-1.","DOI":"10.1186\/s13677-023-00552-1"},{"key":"2026072909034591426_j_comp-2025-0059_ref_004","doi-asserted-by":"crossref","unstructured":"A. Alavigharahbagh, V. Hajihashemi, J. J. M. Machado, and J. M. R. S. Tavares, \u201cDeep learning approach for human action recognition using a time saliency map based on motion features considering camera movement and shot in video image sequences,\u201d Information, vol.\u00a014, no.\u00a011, pp.\u00a0616\u2013617, 2023, https:\/\/doi.org\/10.3390\/info14110616.","DOI":"10.3390\/info14110616"},{"key":"2026072909034591426_j_comp-2025-0059_ref_005","doi-asserted-by":"crossref","unstructured":"S. B. Khobdeh, M. R. Yamaghani, and S. K. Sareshkeh, \u201cBasketball motion recognition based on the combination of YOLO and a deep fuzzy LSTM network,\u201d J. Supercomput., vol.\u00a080, no.\u00a03, pp.\u00a03528\u20133553, 2024, https:\/\/doi.org\/10.1007\/s11227-023-05611-7.","DOI":"10.1007\/s11227-023-05611-7"},{"key":"2026072909034591426_j_comp-2025-0059_ref_006","doi-asserted-by":"crossref","unstructured":"M. A. Khan et al.., \u201cHuman action recognition using fusion of multiview and deep features: An application to video surveillance,\u201d Multimed. Tools Appl., vol.\u00a083, no.\u00a05, pp.\u00a014885\u201314911, 2024, https:\/\/doi.org\/10.1007\/s11042-020-08806-9.","DOI":"10.1007\/s11042-020-08806-9"},{"key":"2026072909034591426_j_comp-2025-0059_ref_007","doi-asserted-by":"crossref","unstructured":"M. A. Raza, L. Chen, L. Nanbo, and R. B. Fisher, \u201cEatSense: Human centric, action recognition and localization dataset for understanding eating behaviors and quality of motion assessment,\u201d Image Vis. Comput., vol.\u00a0137, pp.\u00a0104762\u2013104763, 2023, https:\/\/doi.org\/10.1016\/j.imavis.2023.104762.","DOI":"10.1016\/j.imavis.2023.104762"},{"key":"2026072909034591426_j_comp-2025-0059_ref_008","doi-asserted-by":"crossref","unstructured":"R. M. Katz-Rosene and A. Pasek, \u201cSpiral-scaling climate action: Lessons from and for the academic flying less movement,\u201d Environ. Politics, vol.\u00a033, no.\u00a02, pp.\u00a0259\u2013280, 2024, https:\/\/doi.org\/10.1080\/09644016.2023.2193068.","DOI":"10.1080\/09644016.2023.2193068"},{"key":"2026072909034591426_j_comp-2025-0059_ref_009","doi-asserted-by":"crossref","unstructured":"G. Cai, Q. Zhang, B. Liu, Z. Jin, and J. Qian, \u201cDeep learning-based recognition and visualization of human motion behavior,\u201d Acad. J. Sci. Technol., vol.\u00a010, no.\u00a01, pp.\u00a050\u201355, 2024, https:\/\/doi.org\/10.54097\/bk1cd370.","DOI":"10.54097\/bk1cd370"},{"key":"2026072909034591426_j_comp-2025-0059_ref_010","doi-asserted-by":"crossref","unstructured":"S. Feng et al.., \u201cDual\u2010mode conversion of photodetector and neuromorphic vision sensor via bias voltage regulation on a single device,\u201d Adv. Mater., vol.\u00a035, no.\u00a049, pp.\u00a02308090\u20132308091, 2023, https:\/\/doi.org\/10.1002\/adma.202308090.","DOI":"10.1002\/adma.202308090"},{"key":"2026072909034591426_j_comp-2025-0059_ref_011","doi-asserted-by":"crossref","unstructured":"H. Jeon and D. Lee, \u201cBi-directional long short-term memory-based gait phase recognition method robust to directional variations in subject\u2019s gait progression using wearable inertial sensor,\u201d Sensors, vol.\u00a024, no.\u00a04, pp.\u00a01276\u20131278, 2024, https:\/\/doi.org\/10.3390\/s24041276.","DOI":"10.3390\/s24041276"},{"key":"2026072909034591426_j_comp-2025-0059_ref_012","doi-asserted-by":"crossref","unstructured":"X. Wang et al.., \u201cHardvs: Revisiting human activity recognition with dynamic vision sensors,\u201d Proc. AAAI Conf. Artif. Intell., vol.\u00a038, no.\u00a06, pp.\u00a05615\u20135623, 2024, https:\/\/doi.org\/10.1609\/aaai.v38i6.28372.","DOI":"10.1609\/aaai.v38i6.28372"},{"key":"2026072909034591426_j_comp-2025-0059_ref_013","doi-asserted-by":"crossref","unstructured":"T. H. Hsu et al.., \u201cA 0.8\u202fV intelligent vision sensor with tiny convolutional neural network and programmable weights using mixed-mode processing-in-sensor technique for image classification,\u201d IEEE J. Solid-State Circuits, vol.\u00a058, no.\u00a011, pp.\u00a03266\u20133274, 2023, https:\/\/doi.org\/10.1109\/jssc.2023.3285734.","DOI":"10.1109\/JSSC.2023.3285734"},{"key":"2026072909034591426_j_comp-2025-0059_ref_014","doi-asserted-by":"crossref","unstructured":"L. Xiang et al.., \u201cIntegrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors,\u201d Comput. Biol. Med., vol.\u00a0170, pp.\u00a0108016\u2013108017, 2024, https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108016.","DOI":"10.1016\/j.compbiomed.2024.108016"},{"key":"2026072909034591426_j_comp-2025-0059_ref_015","doi-asserted-by":"crossref","unstructured":"M. S. Al-Batah, E. R. Al-Kwaldeh, M. A. Wahed, M. Alzyoud, and N. Al-Shanableh, \u201cEnhancement over DBSCAN satellite spatial data clustering,\u201d J. Electr. Comput. Eng., vol.\u00a02024, no.\u00a01, pp.\u00a02330624\u20132330625, 2024, https:\/\/doi.org\/10.1155\/2024\/2330624.","DOI":"10.1155\/2024\/2330624"},{"key":"2026072909034591426_j_comp-2025-0059_ref_016","doi-asserted-by":"crossref","unstructured":"J. Qian, Y. Zhou, X. Han, and Y. Wang, \u201cMDBSCAN: A multi-density DBSCAN based on relative density,\u201d Neurocomputing, vol.\u00a0576, pp.\u00a0127329\u2013127330, 2024, https:\/\/doi.org\/10.1016\/j.neucom.2024.127329.","DOI":"10.1016\/j.neucom.2024.127329"},{"key":"2026072909034591426_j_comp-2025-0059_ref_017","doi-asserted-by":"crossref","unstructured":"M. Hajihosseinlou, A. Maghsoudi, and R. Ghezelbash, \u201cIntelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches,\u201d J. Geochem. Explor., vol.\u00a0258, pp.\u00a0107393\u2013108394, 2024, https:\/\/doi.org\/10.1016\/j.gexplo.2024.107393.","DOI":"10.1016\/j.gexplo.2024.107393"},{"key":"2026072909034591426_j_comp-2025-0059_ref_018","doi-asserted-by":"crossref","unstructured":"J. Kim, H. Lee, and Y. M. Ko, \u201cConstrained density-based spatial clustering of applications with noise (DBSCAN) using hyperparameter optimization,\u201d Knowl.-Based Syst., vol.\u00a0303, pp.\u00a0112436\u2013112437, 2024, https:\/\/doi.org\/10.1016\/j.knosys.2024.112436.","DOI":"10.1016\/j.knosys.2024.112436"},{"key":"2026072909034591426_j_comp-2025-0059_ref_019","doi-asserted-by":"crossref","unstructured":"A. Sasi Kumar and P. S. Aithal, \u201cDeepQ based heterogeneous clustering hybrid cloud prediction using K-means algorithm,\u201d Int. J. Manage. Technol. Soc. Sci. (IJMTS), vol.\u00a08, no.\u00a02, pp.\u00a0273\u2013283, 2023.","DOI":"10.47992\/IJMTS.2581.6012.0282"},{"key":"2026072909034591426_j_comp-2025-0059_ref_020","doi-asserted-by":"crossref","unstructured":"S. Chowdhury, N. Helian, and R. C. de Amorim, \u201cFeature weighting in DBSCAN using reverse nearest neighbours,\u201d Pattern Recognit., vol.\u00a0137, pp.\u00a0109314\u2013110935, 2023.","DOI":"10.1016\/j.patcog.2023.109314"},{"key":"2026072909034591426_j_comp-2025-0059_ref_021","doi-asserted-by":"crossref","unstructured":"Z. Wei, Y. Gao, X. Zhang, X. Li, and Z. Han, \u201cAdaptive marine traffic behaviour pattern recognition based on multidimensional dynamic time warping and DBSCAN algorithm,\u201d Expert Syst. Appl., vol.\u00a0238, pp.\u00a0122229\u2013122230, 2024, https:\/\/doi.org\/10.1016\/j.eswa.2023.122229.","DOI":"10.1016\/j.eswa.2023.122229"},{"key":"2026072909034591426_j_comp-2025-0059_ref_022","doi-asserted-by":"crossref","unstructured":"A. E. Chaleshtori and A. Aghaie, \u201cA novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis,\u201d Reliab. Eng. Syst. Saf., vol.\u00a0242, pp.\u00a0109720\u2013109721, 2024, https:\/\/doi.org\/10.1016\/j.ress.2023.109720.","DOI":"10.1016\/j.ress.2023.109720"},{"key":"2026072909034591426_j_comp-2025-0059_ref_023","doi-asserted-by":"crossref","unstructured":"T. Zhang, W. Chen, Y. Liu, and L. Wu, \u201cAn intrusion detection method based on stacked sparse autoencoder and improved Gaussian mixture model,\u201d Comput. Secur., vol.\u00a0128, pp.\u00a0103144\u2013103145, 2023, https:\/\/doi.org\/10.1016\/j.cose.2023.103144.","DOI":"10.1016\/j.cose.2023.103144"},{"key":"2026072909034591426_j_comp-2025-0059_ref_024","doi-asserted-by":"crossref","unstructured":"J. Wu, F. Yang, and W. Hu, \u201cUnsupervised anomalous sound detection for industrial monitoring based on ArcFace classifier and Gaussian mixture model,\u201d Appl. Acoust., vol.\u00a0203, pp.\u00a0109188\u2013109189, 2023, https:\/\/doi.org\/10.1016\/j.apacoust.2022.109188.","DOI":"10.1016\/j.apacoust.2022.109188"},{"key":"2026072909034591426_j_comp-2025-0059_ref_025","doi-asserted-by":"crossref","unstructured":"D. Avola et al.., \u201cSignal enhancement and efficient DTW-based comparison for wearable gait recognition,\u201d Comput. Secur., vol.\u00a0137, pp.\u00a0103643\u2013103644, 2024, https:\/\/doi.org\/10.1016\/j.cose.2023.103643.","DOI":"10.1016\/j.cose.2023.103643"},{"key":"2026072909034591426_j_comp-2025-0059_ref_026","doi-asserted-by":"crossref","unstructured":"C. U. I. Langfu et al.., \u201cA method for satellite time series anomaly detection based on fast-DTW and improved-KNN,\u201d Chin. J. Aeronaut., vol.\u00a036, no.\u00a02, pp.\u00a0149\u2013159, 2023.","DOI":"10.1016\/j.cja.2022.05.001"},{"key":"2026072909034591426_j_comp-2025-0059_ref_027","doi-asserted-by":"crossref","unstructured":"C. K. H. Lee and E. K. H. Leung, \u201cSpatiotemporal analysis of bike-share demand using DTW-based clustering and predictive analytics,\u201d Transp. Res. Part E: Logist. Transp. Rev., vol.\u00a0180, pp.\u00a0103361\u2013103362, 2023, https:\/\/doi.org\/10.1016\/j.tre.2023.103361.","DOI":"10.1016\/j.tre.2023.103361"},{"key":"2026072909034591426_j_comp-2025-0059_ref_028","doi-asserted-by":"crossref","unstructured":"G. Wu, C. Wen, and H. Jiang, \u201cWushu movement recognition system based on DTW attitude matching algorithm,\u201d Entertain. Comput., vol.\u00a052, pp.\u00a0100877\u2013100878, 2025, https:\/\/doi.org\/10.1016\/j.entcom.2024.100877.","DOI":"10.1016\/j.entcom.2024.100877"},{"key":"2026072909034591426_j_comp-2025-0059_ref_029","doi-asserted-by":"crossref","unstructured":"Y. Zhang and X. Hou, \u201cApplication of video image processing in sports action recognition based on particle swarm optimization algorithm,\u201d Prev. Med., vol.\u00a0173, p.\u00a0107592, 2023, https:\/\/doi.org\/10.1016\/j.ypmed.2023.107592.","DOI":"10.1016\/j.ypmed.2023.107592"},{"key":"2026072909034591426_j_comp-2025-0059_ref_030","doi-asserted-by":"crossref","unstructured":"M. Wu et al.., \u201cInvisible experience to real-time assessment in elite tennis athlete training: Sport-specific movement classification based on wearable MEMS sensor data,\u201d Proc. Inst. Mech. Eng. Part P: J. Sports Eng. Technol., vol.\u00a0237, no.\u00a04, pp.\u00a0271\u2013282, 2023, https:\/\/doi.org\/10.1177\/17543371211050312.","DOI":"10.1177\/17543371211050312"}],"container-title":["Open Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/comp-2025-0059\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/comp-2025-0059\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T09:03:58Z","timestamp":1785315838000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/comp-2025-0059\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,1]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,7,30]]},"published-print":{"date-parts":[[2026,1,23]]}},"alternative-id":["10.1515\/comp-2025-0059"],"URL":"https:\/\/doi.org\/10.1515\/comp-2025-0059","relation":{},"ISSN":["2299-1093"],"issn-type":[{"value":"2299-1093","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,1]]},"article-number":"20250059"}}