{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T01:44:47Z","timestamp":1781487887833,"version":"3.54.1"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106117"],"award-info":[{"award-number":["62106117"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022M711741"],"award-info":[{"award-number":["2022M711741"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZR2021QF084"],"award-info":[{"award-number":["ZR2021QF084"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62106117"],"award-info":[{"award-number":["62106117"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M711741"],"award-info":[{"award-number":["2022M711741"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["ZR2021QF084"],"award-info":[{"award-number":["ZR2021QF084"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["62106117"],"award-info":[{"award-number":["62106117"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["2022M711741"],"award-info":[{"award-number":["2022M711741"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021QF084"],"award-info":[{"award-number":["ZR2021QF084"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In the past few years, the assessment of Parkinson\u2019s disease (PD) has mainly been based on the clinician\u2019s examination, the patient\u2019s medical history, and self-report. Parkinson\u2019s disease may be misdiagnosed due to a lack of clinical experience. Moreover, it is highly subjective and is not conducive to reflecting a true result. Due to the high incidence rate and increasing trend of PD, it is significant to use objective monitoring and diagnostic tools for accurate and timely diagnosis. In this paper, we designed a low-level feature extractor that uses convolutional layers to extract local information about an image and a high-level feature extractor that extracts global information about an image through the autofocus mechanism. PD is detected by fusing local and global information. The model is trained and evaluated on two publicly available datasets. Experiments have shown that our model has a strong advantage in diagnosing whether people have PD; gait-based analysis and recognition can also provide effective evidence for the early diagnosis of PD.<\/jats:p>","DOI":"10.3390\/info14020119","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T01:41:06Z","timestamp":1676338866000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["FuseLGNet: Fusion of Local and Global Information for Detection of Parkinson\u2019s Disease"],"prefix":"10.3390","volume":"14","author":[{"given":"Ming","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Institute of Ubiquitous Networks and Urban Computing, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Institute of Ubiquitous Networks and Urban Computing, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pihai","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Turing Innovation Team, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Turing Innovation Team, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Turing Innovation Team, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aite","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Institute of Ubiquitous Networks and Urban Computing, Qingdao University, Qingdao 266071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.knosys.2017.10.017","article-title":"Deep learning for freezing of gait detection in Parkinson\u2019s disease patients in their homes using a waist-worn inertial measurement unit","volume":"139","author":"Camps","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mileti, I., Germanotta, M., Alcaro, S., Pacilli, A., Imbimbo, I., Petracca, M., Erra, C., Di Sipio, E., Aprile, I., and Rossi, S. (2017, January 7\u201310). Gait partitioning methods in Parkinson\u2019s disease patients with motor fluctuations: A comparative analysis. Proceedings of the 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rochester, MN, USA.","DOI":"10.1109\/MeMeA.2017.7985910"},{"key":"ref_3","unstructured":"Chen, M., Huang, B., and Xu, Y. (2008, January 19\u201323). Intelligent shoes for abnormal gait detection. Proceedings of the 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nguyen, T.N., Huynh, H.H., and Meunier, J. (2016). Skeleton-based abnormal gait detection. Sensors, 16.","DOI":"10.3390\/s16111792"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1109\/TNSRE.2013.2291907","article-title":"Segmentation and classification of gait cycles","volume":"22","author":"Agostini","year":"2013","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.gaitpost.2012.12.011","article-title":"Clinical assessment of freezing of gait in Parkinson\u2019s disease from computer-generated animation","volume":"38","author":"Morris","year":"2013","journal-title":"Gait Posture"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101912","DOI":"10.1016\/j.datak.2021.101912","article-title":"Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index","volume":"135","author":"Lv","year":"2021","journal-title":"Data Knowl. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/MITS.2022.3162901","article-title":"A Novel Perspective on Travel Demand Prediction Considering Natural Environmental and Socioeconomic Factors","volume":"15","author":"Xu","year":"2023","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_9","first-page":"2628","article-title":"Integrating household travel survey and social media data to improve the quality of od matrix: A comparative case study","volume":"21","author":"Cheng","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","first-page":"126378","article-title":"DCACorrCapsNet: A deep channel-attention correlative capsule network for COVID-19 detection based on multi-source medical images","volume":"53","author":"Zhao","year":"2022","journal-title":"IET Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cai, N., Feng, S., Gui, Q., Zhao, L., Pan, H., Yin, J., and Lin, B. (2021, January 21\u201322). Hybrid silhouette-skeleton body representation for gait recognition. Proceedings of the 2021 13th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China.","DOI":"10.1109\/IHMSC52134.2021.00057"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_13","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917), Long Beach, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/TNSRE.2020.2969649","article-title":"Prediction of freezing of gait in patients with Parkinson\u2019s disease by identifying impaired gait patterns","volume":"28","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Borz\u00ec, L., Mazzetta, I., Zampogna, A., Suppa, A., Olmo, G., and Irrera, F. (2021). Prediction of Freezing of Gait in Parkinson\u2019s Disease Using Wearables and Machine Learning. Sensors, 21.","DOI":"10.3390\/s21020614"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2018.03.032","article-title":"A hybrid spatio-temporal model for detection and severity rating of Parkinson\u2019s disease from gait data","volume":"315","author":"Zhao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"57583","DOI":"10.1109\/ACCESS.2018.2874073","article-title":"Multi-View Gait Recognition Based on a Spatial-Temporal Deep Neural Network","volume":"6","author":"Tong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, H., Lv, Z., Li, J., Xu, Z., Yue, W., Sun, H., and Sheng, Z. (2022). Traffic Flow Forecasting in the COVID-19, A Deep Spatial-Temporal Model Based on Discrete Wavelet Transformation. ACM Trans. Knowl. Discov. Data.","DOI":"10.1145\/3564753"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101678","DOI":"10.1016\/j.aei.2022.101678","article-title":"A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic","volume":"53","author":"Wang","year":"2022","journal-title":"Adv. Eng. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lv, Z., Li, J., Dong, C., and Xu, Z. (2021). DeepSTF: A Deep Spatial\u2013Temporal Forecast Model of Taxi Flow. Comput. J., bxab178.","DOI":"10.1093\/comjnl\/bxab178"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103758","DOI":"10.1016\/j.trc.2022.103758","article-title":"A Spatio-Temporal autocorrelation model for designing a carshare system using historical heterogeneous Data: Policy suggestion","volume":"141","author":"Cheng","year":"2022","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Albuquerque, P., Verlekar, T.T., Correia, P.L., and Soares, L.D. (2021). A spatiotemporal deep learning approach for automatic pathological gait classification. Sensors, 21.","DOI":"10.3390\/s21186202"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9439","DOI":"10.1109\/TCYB.2021.3056104","article-title":"Multimodal Gait Recognition for Neurodegenerative Diseases","volume":"52","author":"Zhao","year":"2022","journal-title":"IEEE Trans Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1007\/s10489-022-03543-y","article-title":"TransGait: Multimodal-based gait recognition with set transformer","volume":"53","author":"Li","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11979","DOI":"10.1109\/ACCESS.2022.3143815","article-title":"Resource Competition in Blockchain Networks Under Cloud and Device Enabled Participation","volume":"10","author":"Liang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4293","DOI":"10.1007\/s11269-022-03255-5","article-title":"A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning","volume":"36","author":"Xu","year":"2022","journal-title":"Water Resour. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, A., Wang, Y., and Li, J. (2022). Transferable Self-Supervised Instance Learning for Sleep Recognition. IEEE Trans. Multimed.","DOI":"10.1109\/TMM.2022.3176751"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhao, A., Li, J., Lv, Z., Dong, C., and Li, H. (2022). Multi-attribute Graph Convolution Network for Regional Traffic Flow Prediction. Neural Process. Lett.","DOI":"10.1007\/s11063-022-11036-9"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"33851","DOI":"10.1007\/s11042-022-12659-9","article-title":"Two-channel lstm for severity rating of parkinson\u2019s disease using 3d trajectory of hand motion","volume":"81","author":"Zhao","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113075","DOI":"10.1016\/j.eswa.2019.113075","article-title":"Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait","volume":"143","author":"Bilodeau","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Albuquerque, P., Machado, J.P., Verlekar, T.T., Correia, P.L., and Soares, L.D. (2021). Remote Gait type classification system using markerless 2D video. Diagnostics, 11.","DOI":"10.3390\/diagnostics11101824"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5536386","DOI":"10.1155\/2021\/5536386","article-title":"Blind travel prediction based on obstacle avoidance in indoor scene","volume":"2021","author":"Lv","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_33","unstructured":"Lv, Z., Li, J., Dong, C., and Zhao, W. (2020). Lecture Notes in Computer Science, Proceedings of the International Conference on Wireless Algorithms, Systems, and Applications, Qingdao, China, 13\u201315 September 2020, Springer."},{"key":"ref_34","unstructured":"Lv, Z., Li, J., Xu, Z., Wang, Y., and Li, H. (2021). Lecture Notes in Computer Science, Proceedings of the International Conference on Wireless Algorithms, Systems, and Applications, Nanjing, China, 25\u201327 June 2021, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Loureiro, J., and Correia, P.L. (2020, January 16\u201320). Using a skeleton gait energy image for pathological gait classification. Proceedings of the 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), Buenos Aires, Argentina.","DOI":"10.1109\/FG47880.2020.00064"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/2\/119\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:33:41Z","timestamp":1760121221000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/2\/119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,13]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["info14020119"],"URL":"https:\/\/doi.org\/10.3390\/info14020119","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,13]]}}}