{"id":93812,"date":"2024-08-14T18:12:44","date_gmt":"2024-08-14T11:12:44","guid":{"rendered":"https:\/\/www.its.ac.id\/news\/?p=93812"},"modified":"2025-03-15T12:35:34","modified_gmt":"2025-03-15T05:35:34","slug":"its-doctor-develops-human-activity-recognition-method-utilizing-ai","status":"publish","type":"post","link":"https:\/\/www.its.ac.id\/news\/en\/its-doctor-develops-human-activity-recognition-method-utilizing-ai\/","title":{"rendered":"ITS Doctor Develops Human Activity Recognition Method Utilizing AI"},"content":{"rendered":"
\"Dr
Dr Ir Endang Sri Rahayu MKom (standing) explaining her dissertation on the combination model of joint angle shift using Artificial Intelligence (AI)<\/figcaption><\/figure>\n
\n

ITS Campus, ITS News<\/strong> \u2013 Research on Human Motion Analysis (HMA) is now advancing in several fields, including healthcare. A new doctoral graduate from Institut Teknologi Sepuluh Nopember (ITS)<\/a>, Dr. Ir. Endang Sri Rahayu, M.Kom, has developed an innovative method to recognize human activities by observing joint points using Deep Convolutional Neural Networks (DCNN), a method within Artificial Intelligence (AI).<\/p>\n<\/blockquote>\n

During her open doctoral defense held at the ITS’ Department of Electrical Engineering<\/a>\u00a0on Monday (12\/8), Endang emphasized the importance of research in human activity recognition. This research supports medical rehabilitation processes, elderly activity monitoring, and the development of intelligent robot movements. “To address various human activities, the research needs to be developed using various methods to achieve high accuracy,” she explained.<\/p>\n

Endang further highlighted that recognizing human activity is critical in healthcare because it can detect abnormal movements. Such abnormal movements could serve as an indicator to monitor disease risks, especially in the elderly. \u201cTherefore, research on human activity observation becomes essential,\u201d said the lecturer from Jayabaya University\u2019s Department of Electrical Engineering, Jakarta.<\/p>\n

<\/div>\n
\"Dr
Dr Ir Endang Sri Rahayu MKom when presenting her research related to Human Motion Analysis (HMA) in a doctoral promotion hearing at the Department of Electrical Engineering ITS<\/figcaption><\/figure>\n

Driven by this problem, Endang\u2019s dissertation titled A Combination Model of Joint Angle Shifts with Deep Learning to Recognize Human Activities<\/em> aims to recognize human activities by extracting joint features. \u201cThis research analyzes joint positions using the DCNN model,\u201d explained the Jombang-born researcher, who was born on April 27, 1965.<\/p>\n

Using the Florence 3D Actions dataset, this research observed 15 human joint points, including the head, shoulders, and ankles, which serve as indicators of human movement. Joints were chosen as movement indicators because they connect the human skeleton and move following human activity patterns. \u201cThus, joints are the ideal indicators as joint points represent human activity patterns,\u201d explained the ITS Electrical Engineering<\/a> graduate.<\/p>\n

\"Dr
Dr Ir Endang Sri Rahayu MKom when explaining the joint points observed in one of her studies during a trial which was also broadcast via Youtube<\/figcaption><\/figure>\n

Endang added that the distance between joints is calculated frame by frame from a segment of observation video using the Euclidean distance technique. However, this method struggled to distinguish some human activities, such as sitting and standing. \u201cThis occurs because the joint distance-based method only considers absolute joint position changes without accounting for movement direction,\u201d she explained.<\/p>\n

Delving deeper, Endang noted the need for a method that could differentiate between activities with similar movement distances. She introduced a joint angle shift method that successfully distinguished activities like sitting and standing, which have similar joint position changes but differ in movement direction.<\/p>\n

\"Dr
Dr Ir Endang Sri Rahayu MKom (right) with his family after the doctoral promotion session held at the ITS Electrical Engineering Department<\/figcaption><\/figure>\n
\n
\n
\n
\n

Based on her research, Endang achieved impressive results with an accuracy of 97.44 percent and a loss rate of 0.0602. This evaluation demonstrated the model’s optimal performance and its significant potential for further development. “I hope that future research on HMA continues to progress, particularly in healthcare for vulnerable groups,” she concluded. (ITS Public Relations)<\/strong><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

\u00a0<\/strong><\/p>\n

Reporter: Mohammad Febryan Khamim<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

ITS Campus, ITS News \u2013 Research on Human Motion Analysis (HMA) is now advancing in several fields, including healthcare. A new doctoral graduate from Institut Teknologi Sepuluh Nopember (ITS), Dr. Ir. Endang Sri Rahayu, M.Kom, has developed an innovative method to recognize human activities by observing joint points using Deep Convolutional Neural Networks (DCNN), a […]<\/p>\n","protected":false},"author":172,"featured_media":92941,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[3663,120,3691,67,125],"tags":[],"class_list":["post-93812","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-good-health-well-being-en","category-headline-en","category-innovation-infrastructure-en","category-latest-en","category-research-and-industry"],"yoast_head":"\nITS Doctor Develops Human Activity Recognition Method Utilizing AI - ITS News<\/title>\n<meta name=\"description\" content=\"Dr. Ir. 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