Research on Adversarial Examples in Human Physical Rehabilitation Exercises Based on GPREGAN Framework

Kangjie Zheng; Shan Jin; ChengWei Zhang

doi:10.3788/LOP202158.2428004

[1] Maciejasz P, Eschweiler J, Gerlach-Hahn K et al. A survey on robotic devices for upper limb rehabilitation[J]. Journal of Neuroengineering and Rehabilitation, 11, 3(2014).

[2] Gauthier L V, Kane C, Borstad A et al. Video Game Rehabilitation for Outpatient Stroke (VIGoROUS): protocol for a multi-center comparative effectiveness trial of in-home gamified constraint-induced movement therapy for rehabilitation of chronic upper extremity hemiparesis[J]. BMC Neurology, 17, 109(2017).

[3] Vemulapalli R, Arrate F, Chellappa R. Human action recognition by representing 3D skeletons as points in a lie group[C]. //2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA., 588-595(2014).

[4] Wang J, Liu Z C, Wu Y et al. Mining actionlet ensemble for action recognition with depth cameras[C]. //2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA., 1290-1297(2012).

[5] Lv F, Nevatia R. Recognition and segmentation of 3-D human action using HMM and multi-class AdaBoost[M]. //Leonardis A, Bischof H, Pinz A. Computer vision-ECCV 2006. Lecture notes in computer science, 3954, 359-372(2006).

[6] Wu D, Shao L. Leveraging hierarchical parametric networks for skeletal joints based action segmentation and recognition[C]. //2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA., 724-731(2014).

[7] Xia L, Chen C C, Aggarwal J K. View invariant human action recognition using histograms of 3D joints[C]. //2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, June 16-21, 2012, Providence, RI, USA., 20-27(2012).

[8] Ji S W, Xu W, Yang M et al. 3D convolutional neural networks for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 221-231(2013).

[9] Du Y, Wang W, Wang L. Hierarchical recurrent neural network for skeleton based action recognition[C]. //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 1110-1118(2015).

[10] Ma Z F, Li J, Cao L X. Fall behavior detection and analysis using a Kinect sensor[J]. Laser & Optoelectronics Progress, 57, 210402(2020).

[11] Bao Z Q, Lü C G. Real-time gesture recognition based on Kinect[J]. Laser & Optoelectronics Progress, 55, 031008(2018).

[12] Guo L P, Chen X N, Liu B et al. 3D-object reconstruction based on fusion of depth images by Kinect sensor[J]. Journal of Applied Optics, 35, 811-816(2014).

[13] Badawi A A, Al-Kabbany A, Shaban H. Multimodal human activity recognition from wearable inertial sensors using machine learning[C]. //2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), December 3-6, 2018, Sarawak, Malaysia., 402-407(2018).

[14] Chen M Y, He X J, Yang J et al. 3-D convolutional recurrent neural networks with attention model for speech emotion recognition[J]. IEEE Signal Processing Letters, 25, 1440-1444(2018).

[15] Wu K L, Wei X M. Analysis of psychological and sleep status and exercise rehabilitation of front-line clinical staff in the fight against COVID-19 in China[J]. Medical Science Monitor Basic Research, 26, e924085(2020).

[16] Szegedy C, Zaremba W, Sutskever I et al. Intriguing properties of neural networks[EB/OL]. (2013-12-21)[2021-01-01]. https://arxiv.org/abs/1312.6199

[17] Goodfellow I J, Pouget-Abadie J, Mirza M et al. Generative adversarial nets[C]. //Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13, 2014, Montreal, Quebec, Canada. [S.l.: s.n.], 2672-2680(2014).

[18] Hu W W, Tan Y. Generating adversarial malware examples for black-box attacks based on GAN[EB/OL]. (2017-02-20)[2021-01-01]. https://arxiv.org/abs/1702.05983

[19] Johansson G. Visual perception of biological motion and a model for its analysis[J]. Perception & Psychophysics, 14, 201-211(1973).

[20] Luvizon D C, Tabia H, Picard D. Learning features combination for human action recognition from skeleton sequences[J]. Pattern Recognition Letters, 99, 13-20(2017).

[21] Luo J J, Wang W, Qi H R. Group sparsity and geometry constrained dictionary learning for action recognition from depth maps[C]. //2013 IEEE International Conference on Computer Vision, December 1-8, 2013, Sydney, NSW, Australia., 1809-1816(2013).

[22] Mahasseni B, Todorovic S. Regularizing long short term memory with 3D human-skeleton sequences for action recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 3054-3062(2016).

[23] Yacoob Y, Black M J. Parameterized modeling and recognition of activities[J]. Computer Vision and Image Understanding, 73, 232-247(1999).

[24] Zhu W T, Lan C L, Xing J L et al. Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks[C]. //Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA., 3697-3704(2016).

[25] Goodfellow I J, Shlens J, Szegedy C. Explaining and harnessing adversarial examples[EB/OL]. (2014-12-20)[2021-01-01]. https://arxiv.org/abs/1412.6572

[26] Wu Y X, He K M. Group normalization[J]. International Journal of Computer Vision, 128, 742-755(2020).

[27] Vakanski A, Jun H P, Paul D et al. A data set of human body movements for physical rehabilitation exercises[J]. Data, 3, 2(2018).