Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method

Posen Lee, Tai Been Chen, Chin Hsuan Liu*, Chi Yuan Wang, Guan Hua Huang, Nan Han Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Many neurological and musculoskeletal disorders are associated with problems related to postural movement. Noninvasive tracking devices are used to record, analyze, measure, and detect the postural control of the body, which may indicate health problems in real time. A total of 35 young adults without any health problems were recruited for this study to participate in a walking experiment. An iso-block postural identity method was used to quantitatively analyze posture control and walking behavior. The participants who exhibited straightforward walking and skewed walking were defined as the control and experimental groups, respectively. Fusion deep learning was applied to generate dynamic joint node plots by using OpenPose-based methods, and skewness was qualitatively analyzed using convolutional neural networks. The maximum specificity and sensitivity achieved using a combination of ResNet101 and the naïve Bayes classifier were 0.84 and 0.87, respectively. The proposed approach successfully combines cell phone camera recordings, cloud storage, and fusion deep learning for posture estimation and classification.

Original languageEnglish
Article number295
Issue number5
StatePublished - May 2022


  • fusion deep learning
  • iso-block postural identity
  • OpenPose


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