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Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,4,30]]},"abstract":"<jats:p>\n            Parkinson\u2019s disease is a progressive nervous system disorder afflicting millions of patients. Among its motor symptoms, bradykinesia is one of the cardinal manifestations. Experienced doctors are required for the clinical diagnosis of bradykinesia, but sometimes they also miss subtle changes, especially in early stages of such disease. Therefore, developing auxiliary diagnostic methods that can automatically detect bradykinesia has received more and more attention. In this article, we employ a two-stage framework for bradykinesia recognition based on the video of patient movement. First, convolution neural networks are trained to localize keypoints in each video frame. These time-varying coordinates form motion trajectories that represent the whole movement. From the trajectory, we then propose novel measurements, namely\n            <jats:italic>stability<\/jats:italic>\n            ,\n            <jats:italic>completeness<\/jats:italic>\n            , and\n            <jats:italic>self-similarity<\/jats:italic>\n            , to quantify different motor behaviors. We also propose a periodic motion model called\n            <jats:italic>PMNet<\/jats:italic>\n            . An encoder--decoder structure is applied to learn a low dimensional representation of a motion process. The compressed motion process and quantified motor behaviors are combined as inputs to a fully-connected neural network. Different from the traditional means, our solution extends the application scenario outside the hospital and can be easily transplanted to conduct similar tasks. A commonly used clinical assessment is served as a case study. Experimental results based on real-world data validate the effectiveness of our approach for bradykinesia recognition.\n          <\/jats:p>","DOI":"10.1145\/3369438","type":"journal-article","created":{"date-parts":[[2020,2,10]],"date-time":"2020-02-10T06:49:13Z","timestamp":1581317353000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Bradykinesia Recognition in Parkinson\u2019s Disease via Single RGB Video"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5682-2140","authenticated-orcid":false,"given":"Bo","family":"Lin","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"given":"Wei","family":"Luo","sequence":"additional","affiliation":[{"name":"Zhejiang University School of Medicine, Hangzhou, China"}]},{"given":"Zhiling","family":"Luo","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University School of Medicine, Hangzhou, China"}]},{"given":"Shuiguang","family":"Deng","sequence":"additional","affiliation":[{"name":"Zhejiang University School of Medicine, Hangzhou, China"}]},{"given":"Jianwei","family":"Yin","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"given":"Mengchu","family":"Zhou","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology, Newark, NJ"}]}],"member":"320","published-online":{"date-parts":[[2020,2,9]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of USENIX Symposium on Operating Systems Design and Implementation","volume":"16","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , Manjunath Kudlur , Josh Levenberg , Rajat Monga , Sherry Moore , Derek G. Murray , Benoit Steiner , Paul Tucker , Vijay Vasudevan , Pete Warden , Martin Wicke , Yuan Yu , and Xiaoqiang Zheng . 2016 . TensorFlow: A system for large-scale machine learning . In Proceedings of USENIX Symposium on Operating Systems Design and Implementation , Vol. 16 . 265--283. Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A system for large-scale machine learning. 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Ji-Won Kim, Jae-Ho Lee, Yuri Kwon, Chul-Seung Kim, Gwang-Moon Eom, Seong-Beom Koh, Do-Young Kwon, and Kun-Woo Park. 2011. Quantification of bradykinesia during clinical finger taps using a gyrosensor in patients with Parkinson\u2019s disease. Medical 8 Biological Engineering 8 Computing 49, 3 (2011), 365--371."},{"key":"e_1_2_1_20_1","volume-title":"Modeling alzheimer\u2019s disease progression with fused laplacian sparse group lasso. ACM Transactions on Knowledge Discovery from Data 12, 6","author":"Liu Xiaoli","year":"2018","unstructured":"Xiaoli Liu , Peng Cao , Andr\u00e9 R. Gon\u00e7alves , Dazhe Zhao , and Arindam Banerjee . 2018. Modeling alzheimer\u2019s disease progression with fused laplacian sparse group lasso. ACM Transactions on Knowledge Discovery from Data 12, 6 ( 2018 ), 65. Xiaoli Liu, Peng Cao, Andr\u00e9 R. Gon\u00e7alves, Dazhe Zhao, and Arindam Banerjee. 2018. Modeling alzheimer\u2019s disease progression with fused laplacian sparse group lasso. 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