An investigation of 3D human pose estimation for learning Tai Chi: A human factor perspective

Aouaidjia KAMEL, Bowen LIU, Ping LI, Bin SHENG

Research output: Contribution to journalArticle

2 Citations (Scopus)


In this article, we propose a Tai Chi training system based on pose estimation using Convolutional Neural Networks (CNNs) called iTai-Chi. Our system aims to overcome the disadvantages of insufficient accurate feedback in traditional teaching methods such as one-to-many tutorial and video watching. With the specially trained neural network, our iTai-Chi system can estimate learners' poses more accurately compared to Kinect V2. In our system, user's motion is evaluated through comparison with the template motion. The evaluated results are presented to the user to locate the error in their motions and help their correction. To verify the effectiveness of our system, we carried out a series of user studies. Results reflect that the iTai-Chi system successfully improve users' performance in movement accuracy. Also, our system assists elder Tai Chi practitioners and students without prior knowledge to overcome learning obstacles and improve their skills. The users agreed that our system is interesting and supportive for their Tai Chi learning. Copyright © 2018 Taylor & Francis Group, LLC.
Original languageEnglish
Pages (from-to)427-439
JournalInternational Journal of Human-Computer Interaction
Issue number4-5
Early online dateNov 2018
Publication statusPublished - 2019


Human engineering
Neural networks
neural network
teaching method


Kamel, A., Liu, B., Li, P., & Sheng, B. (2019). An investigation of 3D human pose estimation for learning Tai Chi: A human factor perspective. International Journal of Human-Computer Interaction, 35(4-5), 427-439. doi: 10.1080/10447318.2018.1543081


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