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: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)427-439
Number of pages13
JournalInternational Journal of Human-Computer Interaction
Volume35
Issue number4-5
DOIs
Publication statusPublished - 16 Mar 2019
Externally publishedYes

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Human-Computer Interaction
  • Computer Science Applications

Cite this