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Syst."],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Automated mental retardation (MR) assessment is potential for improving the diagnostic efficiency and objectivity in clinical practice. Based on the researches on abnormal behavior characteristics of patients with MR, we propose an extension and supplement shift multi-scale G3D (ESS MS-G3D) network for video-based assessment of MR. Specifically, all videos are collected from clinical diagnostic scenarios and the skeleton sequence of human body is extracted from videos through an advanced pose estimation model. To solve the shortcomings of existing behavior characteristic learning methods, we present: (1) three G3D styles, enable the network to have different input forms; (2) two G3D graphs and two extension graphs, redefine and extend the graph structure of spatial\u2013temporal nodes; (3) two learnable parameters, realize adaptive adjustment of graph structure; (4) a shift layer, enable the network to learn global features. Finally, we construct a three-branch model ESS MS-STGC, which can capture the discriminative spatial\u2013temporal features and explore the co-occurrence relationship between spatial and temporal domains. Experiments in clinical video data set show that our proposed model has good performance in MR assessment and is superior to the existing vision-based methods. In two-classification task, our model with joint stream achieves the highest accuracy of <jats:inline-formula><jats:alternatives><jats:tex-math>$$94.63\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>94.63<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> in validation set and <jats:inline-formula><jats:alternatives><jats:tex-math>$$89.13\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>89.13<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> in test set. The results are further improved to <jats:inline-formula><jats:alternatives><jats:tex-math>$$96.52\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>96.52<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and <jats:inline-formula><jats:alternatives><jats:tex-math>$$93.22\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>93.22<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, respectively, by utilizing multi-stream fusion strategy. In four-classification task, our model obtains Top1 accuracy of <jats:inline-formula><jats:alternatives><jats:tex-math>$$78.84\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>78.84<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and Top2 accuracy of <jats:inline-formula><jats:alternatives><jats:tex-math>$$91.34\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>91.34<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> in test set. The proposed method provides a new idea for clinical mental retardation assessment.<\/jats:p>","DOI":"10.1007\/s40747-023-01275-1","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T10:04:17Z","timestamp":1700561057000},"page":"2401-2419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ESS MS-G3D: extension and supplement shift MS-G3D network for the assessment of severe mental retardation"],"prefix":"10.1007","volume":"10","author":[{"given":"Quan","family":"Liu","sequence":"first","affiliation":[]},{"given":"Mincheng","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Dujuan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Simeng","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Qianhong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Xiang","sequence":"additional","affiliation":[]},{"given":"Lihua","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Zhongchun","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1921-7915","authenticated-orcid":false,"given":"Jun","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"1275_CR1","unstructured":"Luckasson R et al (2002) Mental retardation: definition, classification, and systems of supports. 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All guardians have consented to this research on the behalf of all the participants, including patients and normal controls.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}