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Human pose plays an important role in extracted features; then these features are used as the weight input with classifier. We use machine learning during two steps of training and testing images of standard dataset that can be used during benchmarking the system. Unlike previous methods that need size or length of shapes mainly to represent the cues when machine learning is used to recognize human activities, accurate experimental results coming from appropriate segments of the human body proved the worthiness of proposed method. Twelve activities are used in challenging of availability comparison with dataset to demonstrate our method. The results show that we achieved 87.3% in training set, while in testing set, we achieved 94% in terms of precision.<\/jats:p>","DOI":"10.1155\/2021\/5593916","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T20:43:04Z","timestamp":1618346584000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Weighted Classification of Machine Learning to Recognize Human Activities"],"prefix":"10.1155","volume":"2021","author":[{"given":"Guorong","family":"Wu","sequence":"first","affiliation":[]},{"given":"Zichen","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8900-3598","authenticated-orcid":false,"given":"Xuhui","family":"Chen","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107140"},{"key":"e_1_2_9_2_2","article-title":"Image processing device, image processing method, and program","volume":"16","author":"Kinoshita M.","year":"2020","journal-title":"U.S. Patent Application"},{"key":"e_1_2_9_3_2","article-title":"Recognition of human activities from still image using NOVEL CLASSIFIER","volume":"71","author":"Sulong G.","year":"2015","journal-title":"Journal of Theoretical & Applied Information Technology"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21020405"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10020111"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.10.054"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"YanJ. 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