Limb movement detection and analysis based on visual recognition of human posture
Abstract Current motion detection and evaluation technologies face challenges such as limited scalability, imprecise feedback, and lack of personalized guidance. To address these challenges, this research integrated efficient BlazePose technology with pioneering DW_KNN* algorithm, resulting in the r...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-03-01
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| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-025-00246-4 |
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| _version_ | 1849389991288897536 |
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| author | Zhiguo Xiao Chunxiang Wang Tianjiao Ding Xiangfeng Shen Xinyuan Li Dongni Li |
| author_facet | Zhiguo Xiao Chunxiang Wang Tianjiao Ding Xiangfeng Shen Xinyuan Li Dongni Li |
| author_sort | Zhiguo Xiao |
| collection | DOAJ |
| description | Abstract Current motion detection and evaluation technologies face challenges such as limited scalability, imprecise feedback, and lack of personalized guidance. To address these challenges, this research integrated efficient BlazePose technology with pioneering DW_KNN* algorithm, resulting in the remarkable accuracy of 98.2% in action recognition and showcasing outstanding scalability. Furthermore, the established ACLstm time series prediction model could comprehensively analyze historical sports data and associated factors of users. In Rehab dataset, MAE(Mean Absolute Error, MAE) loss was 1.383 for motion count and 0.508 for motion time. This innovative framework delivered precise feedback and tailored guidance for physical exercise and medical rehabilitation. |
| format | Article |
| id | doaj-art-c417447ddfc3449eb5a8e955b546ffbc |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-c417447ddfc3449eb5a8e955b546ffbc2025-08-20T03:41:47ZengSpringerDiscover Artificial Intelligence2731-08092025-03-015111210.1007/s44163-025-00246-4Limb movement detection and analysis based on visual recognition of human postureZhiguo Xiao0Chunxiang Wang1Tianjiao Ding2Xiangfeng Shen3Xinyuan Li4Dongni Li5School of Computer Science & Technology, Beijing Institute of TechnologySchool of Computer Science Technology, Changchun UniversitySchool of Computer Science Technology, Changchun UniversitySchool of Computer Science Technology, Changchun UniversitySchool of Computer Science Technology, Changchun UniversitySchool of Computer Science & Technology, Beijing Institute of TechnologyAbstract Current motion detection and evaluation technologies face challenges such as limited scalability, imprecise feedback, and lack of personalized guidance. To address these challenges, this research integrated efficient BlazePose technology with pioneering DW_KNN* algorithm, resulting in the remarkable accuracy of 98.2% in action recognition and showcasing outstanding scalability. Furthermore, the established ACLstm time series prediction model could comprehensively analyze historical sports data and associated factors of users. In Rehab dataset, MAE(Mean Absolute Error, MAE) loss was 1.383 for motion count and 0.508 for motion time. This innovative framework delivered precise feedback and tailored guidance for physical exercise and medical rehabilitation.https://doi.org/10.1007/s44163-025-00246-4Motion detectionHuman pose recognitionAction classificationTime-series predictionPosture assessment |
| spellingShingle | Zhiguo Xiao Chunxiang Wang Tianjiao Ding Xiangfeng Shen Xinyuan Li Dongni Li Limb movement detection and analysis based on visual recognition of human posture Discover Artificial Intelligence Motion detection Human pose recognition Action classification Time-series prediction Posture assessment |
| title | Limb movement detection and analysis based on visual recognition of human posture |
| title_full | Limb movement detection and analysis based on visual recognition of human posture |
| title_fullStr | Limb movement detection and analysis based on visual recognition of human posture |
| title_full_unstemmed | Limb movement detection and analysis based on visual recognition of human posture |
| title_short | Limb movement detection and analysis based on visual recognition of human posture |
| title_sort | limb movement detection and analysis based on visual recognition of human posture |
| topic | Motion detection Human pose recognition Action classification Time-series prediction Posture assessment |
| url | https://doi.org/10.1007/s44163-025-00246-4 |
| work_keys_str_mv | AT zhiguoxiao limbmovementdetectionandanalysisbasedonvisualrecognitionofhumanposture AT chunxiangwang limbmovementdetectionandanalysisbasedonvisualrecognitionofhumanposture AT tianjiaoding limbmovementdetectionandanalysisbasedonvisualrecognitionofhumanposture AT xiangfengshen limbmovementdetectionandanalysisbasedonvisualrecognitionofhumanposture AT xinyuanli limbmovementdetectionandanalysisbasedonvisualrecognitionofhumanposture AT dongnili limbmovementdetectionandanalysisbasedonvisualrecognitionofhumanposture |