J. Aggarwal and L. Xia, Human activity recognition from 3D data: A review, Pattern Recognition Letters, vol.48, pp.70-80, 2014.
DOI : 10.1016/j.patrec.2014.04.011

J. K. Aggarwal and M. S. Ryoo, Human activity analysis, ACM Computing Surveys, vol.43, issue.3
DOI : 10.1145/1922649.1922653

, ACM Computing Surveys (CSUR), vol.43, issue.3, p.16, 2011.

B. B. Amor, J. Su, and A. Srivastava, Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.1, pp.1-13, 2016.
DOI : 10.1109/TPAMI.2015.2439257

J. Ben-arie, Z. Wang, P. Pandit, and S. Rajaram, Human activity recognition using multidimensional indexing, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.8, pp.1091-1104, 2002.
DOI : 10.1109/TPAMI.2002.1023805

A. F. Bobick and J. W. Davis, The recognition of human movement using temporal templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.3, pp.257-267, 2001.
DOI : 10.1109/34.910878

Z. Cao, T. Simon, S. E. Wei, and Y. Sheikh, Realtime multi-person 2d pose estimation using part affinity fields. arXiv preprint, 2016.

A. A. Chaaraoui, J. R. Padilla-lpez, P. Climent-prez, and F. Flrez-revuelta, Evolutionary joint selection to improve human action recognition with RGB-D devices, Expert Systems with Applications, vol.41, issue.3, pp.786-794, 2014.
DOI : 10.1016/j.eswa.2013.08.009

C. C. Chang and C. J. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, p.27, 2011.
DOI : 10.1145/1961189.1961199

C. Chen, R. Jafari, and N. Kehtarnavaz, UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor, 2015 IEEE International Conference on Image Processing (ICIP), pp.168-172
DOI : 10.1109/ICIP.2015.7350781

C. Chen, R. Jafari, and N. Kehtarnavaz, A Real-Time Human Action Recognition System Using Depth and Inertial Sensor Fusion, IEEE Sensors Journal, vol.16, issue.3, pp.773-781, 2016.
DOI : 10.1109/JSEN.2015.2487358

X. Chen and M. Koskela, Skeleton-based action recognition with extreme learning machines, Neurocomputing, vol.149, pp.387-396, 2015.
DOI : 10.1016/j.neucom.2013.10.046

G. Chron, I. Laptev, and C. Schmid, P-cnn: Pose-based cnn features for action recognition, Proceedings of the IEEE International Conference on Computer Vision, pp.3218-3226

C. Debes, A. Merentitis, S. Sukhanov, M. Niessen, N. Frangiadakis et al., Monitoring Activities of Daily Living in Smart Homes: Understanding human behavior, IEEE Signal Processing Magazine, vol.33, issue.2, pp.81-94, 2016.
DOI : 10.1109/MSP.2015.2503881

Y. Du, W. Wang, and L. Wang, Hierarchical recurrent neural network for skeleton based action recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1110-1118

A. A. Efros, A. C. Berg, G. Mori, and J. Malik, Recognizing action at a distance, Proceedings Ninth IEEE International Conference on Computer Vision, pp.726-733
DOI : 10.1109/ICCV.2003.1238420

URL : http://lear.inrialpes.fr/people/triggs/events/iccv03/cdrom/iccv03/0726_efros.pdf

A. Eweiwi, M. S. Cheema, C. Bauckhage, and J. Gall, Efficient Pose-Based Action Recognition, Asian Conference on Computer and Applications, pp.3479-3494, 2016.
DOI : 10.1007/978-3-319-16814-2_28

URL : http://www.iai.uni-bonn.de/%7Egall/download/jgall_action2d3d_accv14.pdf

J. Luo, W. Wang, and H. Qi, Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps, 2013 IEEE International Conference on Computer Vision, pp.1809-1816
DOI : 10.1109/ICCV.2013.227

URL : http://web.eecs.utk.edu/%7Ejluo9/DL-GSGC.pdf

D. C. Luvizon, H. Tabia, and D. Picard, Learning features combination for human action recognition from skeleton sequences, Pattern Recognition Letters, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01515376

R. Minhas, A. Baradarani, S. Seifzadeh, and Q. J. Wu, Human action recognition using extreme learning machine based on visual vocabularies, Neurocomputing, vol.73, issue.10-12, pp.1906-1917, 2010.
DOI : 10.1016/j.neucom.2010.01.020

F. Negin, C. B. Akgl, K. A. Yksel, and A. Eril, An rdf-based action recognition framework with feature selection capability, considering therapy exercises utilizing depth cameras, Journal of Theoretical and Applied Computer Science, vol.8, issue.3, pp.3-22, 2014.
DOI : 10.1007/978-3-642-39094-4_74

F. Negin, F. Zdemir, C. B. Akgl, K. A. Yksel, and A. Eril, A decision forest based feature selection framework for action recognition from rgb-depth cameras, International Conference Image Analysis and Recognition, pp.648-657
DOI : 10.1007/978-3-642-39094-4_74

U. M. Nunes, D. R. Faria, and P. Peixoto, A human activity recognition framework using max-min features and key poses with differential evolution random forests classifier, Pattern Recognition Letters, vol.99, 2017.
DOI : 10.1016/j.patrec.2017.05.004

URL : http://publications.aston.ac.uk/30712/1/Max_min_features_and_key_poses_with_differential_evolution_random_forests_classifier.pdf

G. I. Parisi, C. Weber, and S. Wermter, Self-organizing neural integration of pose-motion features for human action recognition, Frontiers in Neurorobotics, vol.32, issue.6, 2015.
DOI : 10.1016/j.imavis.2014.04.005

URL : http://journal.frontiersin.org/article/10.3389/fnbot.2015.00003/pdf

X. Peng, L. Wang, X. Wang, and Y. Qiao, Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice, Computer Vision and Image Understanding, vol.150, pp.109-125, 2016.
DOI : 10.1016/j.cviu.2016.03.013

URL : http://arxiv.org/pdf/1405.4506

R. Poppe, A survey on vision-based human action recognition, Image and Vision Computing, vol.28, issue.6, pp.976-990, 2010.
DOI : 10.1016/j.imavis.2009.11.014

L. L. Presti, L. Cascia, and M. , 3D skeleton-based human action classification: A survey, Pattern Recognition, vol.53, pp.130-147, 2016.
DOI : 10.1016/j.patcog.2015.11.019

R. Qiao, L. Liu, C. Shen, and A. Van-den-hengel, Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition, Pattern Recognition, vol.66, 2017.
DOI : 10.1016/j.patcog.2017.01.015

URL : http://arxiv.org/pdf/1504.04923

M. Ramanathan, W. Y. Yau, and E. K. Teoh, Human Action Recognition With Video Data: Research and Evaluation Challenges, IEEE Transactions on Human-Machine Systems, vol.44, issue.5, pp.650-663, 2014.
DOI : 10.1109/THMS.2014.2325871

S. Sadanand and J. J. Corso, Action bank: A high-level representation of activity in video, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.1234-1241
DOI : 10.1109/CVPR.2012.6247806

URL : http://www.cse.buffalo.edu/%7Ejcorso/pubs/jcorso_CVPR2012_actionbank.pdf

P. Scovanner, S. Ali, and M. Shah, A 3-dimensional sift descriptor and its application to action recognition, Proceedings of the 15th international conference on Multimedia , MULTIMEDIA '07, pp.357-360
DOI : 10.1145/1291233.1291311

, ACM

S. Sempena, N. U. Maulidevi, and P. R. Aryan, Human action recognition using Dynamic Time Warping, Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, pp.1-5
DOI : 10.1109/ICEEI.2011.6021605

, IEEE

J. Shan and S. Akella, 3D human action segmentation and recognition using pose kinetic energy, 2014 IEEE International Workshop on Advanced Robotics and its Social Impacts, pp.69-75, 2014.
DOI : 10.1109/ARSO.2014.7020983

J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio et al., Real-time human pose recognition in parts from single depth images, Communications of the ACM, vol.56, issue.1, pp.116-124, 2013.
DOI : 10.1145/2398356.2398381

J. Sung, C. Ponce, B. Selman, and A. Saxena, Unstructured human activity detection from rgbd images, Robotics and Automation (ICRA), 2012 IEEE International Conference on, pp.842-849

L. Tao and R. Vidal, Moving Poselets: A Discriminative and Interpretable Skeletal Motion Representation for Action Recognition, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), pp.61-69
DOI : 10.1109/ICCVW.2015.48

D. Tran and L. Torresani, EXMOVES: Mid-level Features for Efficient Action Recognition and Video Analysis, International Journal of Computer Vision, vol.64, issue.2???3, p.26
DOI : 10.1007/978-3-642-33712-3_50

S. Agahian,

, Computer Vision, vol.119, issue.3, pp.239-253, 2016.

G. Varol and A. A. Salah, Efficient large-scale action recognition in videos using extreme learning machines, Expert Systems with Applications, vol.42, issue.21, pp.8274-8282, 2015.
DOI : 10.1016/j.eswa.2015.06.013

V. Veeriah, N. Zhuang, and G. J. Qi, Differential Recurrent Neural Networks for Action Recognition, 2015 IEEE International Conference on Computer Vision (ICCV), pp.4041-4049
DOI : 10.1109/ICCV.2015.460

R. Vemulapalli, F. Arrate, and R. Chellappa, Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.588-595
DOI : 10.1109/CVPR.2014.82

R. Vemulapalli, F. Arrate, and R. Chellappa, R3DG features: Relative 3D geometry-based skeletal representations for human action recognition, Computer Vision and Image Understanding, vol.152, pp.155-166, 2016.
DOI : 10.1016/j.cviu.2016.04.005

S. Vishwakarma and A. Agrawal, A survey on activity recognition and behavior understanding in video surveillance, The Visual Computer, vol.114, issue.12, pp.983-1009, 2013.
DOI : 10.1016/j.cviu.2009.11.005

C. Wang, Y. Wang, and A. L. Yuille, An Approach to Pose-Based Action Recognition, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.915-922
DOI : 10.1109/CVPR.2013.123

C. Wang, Y. Wang, and A. L. Yuille, Mining 3D Key-Pose-Motifs for Action Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2639-2647
DOI : 10.1109/CVPR.2016.289

H. Wang, A. Klser, C. Schmid, and C. L. Liu, Action recognition by dense trajectories, CVPR 2011, pp.3169-3176
DOI : 10.1109/CVPR.2011.5995407

URL : https://hal.archives-ouvertes.fr/inria-00583818

H. Wang, A. Klser, C. Schmid, and C. L. Liu, Dense Trajectories and Motion Boundary Descriptors for Action Recognition, International Journal of Computer Vision, vol.73, issue.2, pp.60-79, 2013.
DOI : 10.1007/s11263-006-9794-4

URL : https://hal.archives-ouvertes.fr/hal-00725627

J. Wang, Z. Liu, and Y. Wu, Learning actionlet ensemble for 3D human action recognition, pp.11-40, 2014.

J. Wang, Z. Liu, Y. Wu, and J. Yuan, Mining actionlet ensemble for action recognition with depth cameras, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.1290-1297
DOI : 10.1109/CVPR.2012.6247813

P. Wang, Z. Li, Y. Hou, and W. Li, Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks, Proceedings of the 2016 ACM on Multimedia Conference, MM '16, pp.102-106
DOI : 10.1109/DICTA.2014.7008101

, ACM

L. Xia, C. C. Chen, and J. Aggarwal, View invariant human action recognition using histograms of 3D joints, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.20-27
DOI : 10.1109/CVPRW.2012.6239233

X. Yang, C. Zhang, and Y. Tian, Recognizing actions using depth motion maps-based histograms of oriented gradients, Proceedings of the 20th ACM international conference on Multimedia, MM '12, pp.1057-1060
DOI : 10.1145/2393347.2396382

Y. Yang, C. Deng, D. Tao, S. Zhang, W. Liu et al., Latent Max-Margin Multitask Learning With Skelets for 3-D Action Recognition, IEEE Transactions on Cybernetics, vol.47, issue.2, pp.439-448, 2017.
DOI : 10.1109/TCYB.2016.2519448

A. Yao, J. Gall, G. Fanelli, and L. Van-gool, Does Human Action Recognition Benefit from Pose Estimation?, Procedings of the British Machine Vision Conference 2011
DOI : 10.5244/C.25.67

C. Youssef, Spatiotemporal representation of 3d skeleton jointsbased action recognition using modified spherical harmonics, Pattern Recognition Letters, vol.83, pp.32-41, 2016.

M. Zanfir, M. Leordeanu, and C. Sminchisescu, The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection, 2013 IEEE International Conference on Computer Vision, pp.2752-2759
DOI : 10.1109/ICCV.2013.342

L. Zelnik-manor and M. Irani, Event-based analysis of video, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, p.II?II, 2001.
DOI : 10.1109/CVPR.2001.990935

J. Zhang, W. Li, P. O. Ogunbona, P. Wang, and C. Tang, RGB-D-based action recognition datasets: A survey, Pattern Recognition, vol.60, pp.86-105, 2016.
DOI : 10.1016/j.patcog.2016.05.019

, Improving Bag-of-poses with Semi-temporal Pose Descriptors for Skeleton-based Action Recognition 27

S. Zhang, X. Liu, and J. Xiao, On geometric features for skeletonbased action recognition using multilayer lstm networks, Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on, pp.148-157

F. Zhu, L. Shao, J. Xie, and Y. Fang, From handcrafted to learned representations for human action recognition: A??survey, Image and Vision Computing, vol.55, pp.42-52, 2016.
DOI : 10.1016/j.imavis.2016.06.007

G. Zhu, L. Zhang, P. Shen, and J. Song, Human action recognition using multi-layer codebooks of key poses and atomic motions, Signal Processing: Image Communication, vol.42, pp.19-30, 2016.
DOI : 10.1016/j.image.2016.01.003

W. Zhu, C. Lan, J. Xing, W. Zeng, Y. Li et al., Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks, 2016.

Y. Zhu, W. Chen, and G. Guo, Fusing multiple features for depthbased action recognition, ACM Transactions on Intelligent Systems and Technology (TIST), vol.6, issue.2, p.18, 2015.