[1] 蔡建平, 淮湛欣. 太极拳运动考评软件的设计与实 现[ J] . 软件, 2012, 33(3) : 60-63. CAI J P, HUAI Z X. Design & implementation of Taiji motion appraisal system [ J ] . Software, 2012, 33 (3) : 60-63.
[2] HASHIMOTO H, NAKAJIMA M, KAWATA S, et al. Skill level evaluation of Taijiquan based on 3D body motion analysis[C] / / 2014 IEEE International Conference on Industrial Technology. Piscataway: IEEE, 2014: 712-717.
[3] 漆才杰, 戴国斌. 太极( 定步) 推手动作识别系统 的设计与研制[ J] . 武汉体育学院学报, 2015, 49 (8) : 52-56. QI C J, DAI G B. Design and development of Taichi pushing hands ( with fixed-foot stance ) movements recognition system[ J] . Journal of Wuhan institute of physical education, 2015, 49(8) : 52-56.
[4] 薛智宏, 张利英, 程振华, 等. 基于 Kinect 的原地 太极拳辅 助 训 练 系 统 [ J ] . 河 北 科 技 大 学 学 报, 2017, 38(2) : 183-189.
XUE Z H, ZHANG L Y, CHENG Z H, et al. Research of Tai-Chi-Chuan auxiliary training system based on Kinect [ J] . Journal of Hebei university of science and technology, 2017, 38(2) : 183-189.
[5] TOMPSON J J, JAIN A, LECUN Y, et al. Joint training of a convolutional network and a graphical model for human pose estimation[EB / OL] . (2014- 09- 17) [2021-11-08] . https: / / arxiv. org / abs/ 1406. 2984.
[6] NING G H, ZHANG Z, HE Z Q. Knowledge-guided deep fractal neural networks for human pose estimation [ J ] . IEEE transactions on multimedia, 2018, 20 (5) : 1246-1259.
[7] 陈梦婷, 王兴刚, 刘文予. 基于密集深度插值的 3D 人体姿态估计方法[ J] . 郑州大学学报( 工学版) , 2021, 42(3) : 26-32.
CHEN M T, WANG X G, LIU W Y. Dense depth interpolation for 3D human pose estimation[ J] . Journal of Zhengzhou university ( engineering science) , 2021, 42(3) : 26-32.
[8] CAO Z, HIDALGO G, SIMON T, et al. OpenPose: realtime multi-person 2D pose estimation using part affinity fields[J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 43(1): 172-186.
[9] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [ EB / OL] . (2015-04 - 10) [ 2021 - 11 - 08] . https: / / arxiv. org / abs/ 1409. 1556.
[10] KHAN A, HARIS M, NADEEM S S, et al. Virtual self defense trainer-analyzing and scoring user pose [ C] / / 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications. Piscataway: IEEE, 2020: 1-5.
[11] YADAV S K, SINGH A, GUPTA A, et al. Real-time Yoga recognition using deep learning[ J] . Neural computing and applications, 2019, 31(12) : 9349-9361.
[12] THAR M C, WINN K Z N, FUNABIKI N. A proposal of Yoga pose assessment method using pose detection for self-learning [ C ] / / 2019 International Conference on Advanced Information Technologies ( ICAIT) . Piscataway: IEEE, 2019: 137-142.
[13] 唐心宇, 宋爱国. 人体姿态估计及在康复训练情景 交互中的应用[ J] . 仪器仪表学报, 2018, 39( 11) : 195-203.
TANG X Y, SONG A G. Human pose estimation and its implementation in scenario interaction system of rehabilitation training [ J] . Chinese journal of scientific instrument, 2018, 39(11) : 195-203.
[14] TAKEDA I, YAMADA A, ONODERA H. Artificial Intelligence-assisted motion capture for medical applications: a comparative study between markerless and passive marker motion capture[ J] . Computer methods in biomechanics and biomedical engineering, 2021, 24 (8) : 864-873.
[15] GIORGINO T. Computing and visualizing dynamic time warping alignments in R: the dtw package [ J] . Journal of statistical software, 2009, 31(7) : 1-24.
[16] YU X Q, XIONG S P. A dynamic time warping based algorithm to evaluate Kinect-enabled home-based physical rehabilitation exercises for older people[ J] . Sensors, 2019, 19(13) : 2882.
[17] 张勇, 党兰学. 线性判别分析特征提取稀疏表示人 脸识别方法[ J] . 郑州大学学报 ( 工学版) , 2015, 36(2) : 94-98.
ZHANG Y, DANG L X. Sparse representation-based face recognition method by LDA feature extraction[ J] . Journal of Zhengzhou university ( engineering science) , 2015, 36(2) : 94-98