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DOI: 10.1007\/978-3-540-88693-8_60. 10.1007\/978-3-540-88693-8_60","DOI":"10.1007\/978-3-540-88693-8_60"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] S.S. Beauchemin and J.L. Barron, \u201cThe computation of optical flow,\u201d ACM Computing Surveys, vol.27, no.3, pp.433-466, 1995. DOI: 10.1145\/212094.212141. 10.1145\/212094.212141","DOI":"10.1145\/212094.212141"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] K. Yang, Y. Dou, S. Lv, F. Zhang, and Q. Lv, \u201cRelative distance features for gait recognition with Kinect,\u201d Journal of Visual Communication and Image Representation, vol.39, pp.209-217, 2016. DOI: 10.1016\/j.jvcir.2016.05.020. 10.1016\/j.jvcir.2016.05.020","DOI":"10.1016\/j.jvcir.2016.05.020"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] P. Turaga, R. Chellappa, V.S. Subrahmanian, and O. Udrea, \u201cMachine recognition of human activities: A survey,\u201d IEEE Trans. Circuits Syst. Video Technol., vol.18, no.11, pp.1473-1488, 2008. DOI: 10.1109\/TCSVT.2008.2005594. 10.1109\/TCSVT.2008.2005594","DOI":"10.1109\/TCSVT.2008.2005594"},{"key":"8","unstructured":"[8] L. Torresani, A. Hertzmann, and C. Bregler, \u201cLearning non-rigid 3D shape from 2D motion,\u201d Proc. NIPS&apos;03, pp.1555-1562, 2003."},{"key":"9","unstructured":"[9] D. Kim, W. Yun, H. Yoon, and J. Kim, \u201cAction recognition with depth maps using HOG descriptors of multi-view motion appearance and history,\u201d Proc. UBICOMM 2014, pp.126-130, 2014."},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] H.H. Ali, H.M. Moftah, and A.A.A. Youssif, \u201cReal-time framework for human action recognition,\u201d Proc. ICBET&apos;18, pp.55-60, 2018. DOI: 10.1145\/3208955.3208972. 10.1145\/3208955.3208972","DOI":"10.1145\/3208955.3208972"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] B. Lange, S. Koenig, E. McConnell, C.-Y. Chang, R. Juang, E. Suma, M. Bolas, and A. Rizzo, \u201cInteractive game-based rehabilitation using the Microsoft Kinect,\u201d Proc. VR&apos;12, pp.171-172, 2012. DOI: 10.1109\/VR.2012.6180935. 10.1109\/VR.2012.6180935","DOI":"10.1109\/VR.2012.6180935"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] H.-T. Chang, Y.-W. Li, H.-T. Chen, S.-Y. Feng, and T.-T. Chien, \u201cA dynamic fitting room based on Microsoft Kinect and augmented reality technologies,\u201d Proc. HCI&apos;13, vol.8007, pp.177-185, 2013. DOI: 10.1007\/978-3-642-39330-3_19. 10.1007\/978-3-642-39330-3_19","DOI":"10.1007\/978-3-642-39330-3_19"},{"key":"13","unstructured":"[13] H. Benko and A. Wilson, \u201cDepth-Touch: Using depth-sensing camera to enable freehand interactions on and above the interactive surface,\u201d IEEE Workshop on Tabletops and Interactive Surfaces, vol.8, pp.1-7, 2009."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] A.L. Cardo, V.M.R. Penichet, M.D. Lozano, and J.E. Garrido, \u201cFalls and fainting detection at home through movement-based interaction,\u201d Proc. Interacci\u00f3n 2018, pp.1-2, 2018. DOI: 10.1145\/3233824.3233857. 10.1145\/3233824.3233857","DOI":"10.1145\/3233824.3233857"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] F. Tan, X. Feng, and Z. Xia, \u201cAn efficient algorithm for human body matting with RGB-D data,\u201d Proc. ICVR 2018, pp.40-43, 2018. DOI: 10.1145\/3198910.3198912. 10.1145\/3198910.3198912","DOI":"10.1145\/3198910.3198912"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] J.L. Raheja, A. Chaudhary, and K. Singal, \u201cTracking of fingertips and centers of palm using Kinect,\u201d Proc. CIMSim&apos;11, pp.248-252, 2011. DOI: 10.1109\/CIMSim.2011.51. 10.1109\/CIMSim.2011.51","DOI":"10.1109\/CIMSim.2011.51"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] K.F. Li, K. Lothrop, E. Gill, and S. Lau, \u201cA web-based sign language translator using 3D video processing,\u201d Proc. NBiS&apos;11, pp.356-361, 2011. DOI: 10.1109\/NBiS.2011.60. 10.1109\/NBiS.2011.60","DOI":"10.1109\/NBiS.2011.60"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] L. Xia, C.-C. Chen, and J. Aggarwal, \u201cHuman detection using depth information by Kinect,\u201d Proc. CVPR 2011, pp.15-22, 2011. DOI: 10.1109\/CVPRW.2011.5981811. 10.1109\/CVPRW.2011.5981811","DOI":"10.1109\/CVPRW.2011.5981811"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake, \u201cReal-time human pose recognition in parts from single depth images,\u201d Proc. CVPR 2011, pp.1297-1304, 2011. DOI: 10.1109\/CVPR.2011.5995316. 10.1109\/CVPR.2011.5995316","DOI":"10.1109\/CVPR.2011.5995316"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] Z. Gao, J. Li, H. Wang, and G. Feng, \u201cDigiClay: An interactive installation for virtual pottery using motion sensing technology,\u201d Proc. ICVR 2018, pp.126-132, 2018. DOI: 10.1145\/3198910.3234659. 10.1145\/3198910.3234659","DOI":"10.1145\/3198910.3234659"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] R. Polana and R. Nelson, \u201cRecognition of motion from tem-poral texture,\u201d Proc. CVPR 1992, pp.129-134, 1992. DOI: 10.1109\/CVPR.1992.223216. 10.1109\/CVPR.1992.223216","DOI":"10.1109\/CVPR.1992.223216"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] Z. Zhang, Z. Kuang, P. Luo, L. Feng, and W. Zhang, \u201cTemporal sequence distillation: Towards few-frame action recognition in videos,\u201d Proc. MM&apos;18, pp.257-264, 2018. DOI: 10.1145\/3240508.3240534 10.1145\/3240508.3240534","DOI":"10.1145\/3240508.3240534"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] Y. Tian, Q. Ruan, G. An, and Y. Fu, \u201cAction recognition using local consistent group sparse coding with spatio-temporal structure,\u201d Proc. MM&apos;16, pp.317-321, 2016. DOI: 10.1145\/2964284.2967234. 10.1145\/2964284.2967234","DOI":"10.1145\/2964284.2967234"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] G. Zhu, M. Yang, K. Yu, W. Xu, and Y. Gong, \u201cDetecting video events based on action recognition in complex scenes using spatio-temporal descriptor,\u201d Proc. MM&apos;09, pp.165-174, 2009. DOI: 10.1145\/1631272.1631297. 10.1145\/1631272.1631297","DOI":"10.1145\/1631272.1631297"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] W. Sun, Z. Zhou, and H. Li, \u201cSitting posture recognition in real-time combined with index map and BLS,\u201d Proc. ICIAI 2019, pp.101-105, 2019. DOI: 10.1145\/3319921.3319955. 10.1145\/3319921.3319955","DOI":"10.1145\/3319921.3319955"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] K. Jansen, B. Tarren, and M. Slingerland, \u201cDisposable, stretchable on-skin sensors for posture monitoring,\u201d Proc. WearSys&apos;18, pp.1-4, 2018. DOI: 10.1145\/3211960.3211969. 10.1145\/3211960.3211969","DOI":"10.1145\/3211960.3211969"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] W. Wang, F. Zhang, and L. Geng, \u201cPosture recognition in CT scanning based on HOG feature and mixture-of-parts model,\u201d Proc. IMIP&apos;19, pp.62-66, 2019. DOI: 10.1145\/3332340.3332351. 10.1145\/3332340.3332351","DOI":"10.1145\/3332340.3332351"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] E.M. Tapia, S.S. Intille, W. Haskell, K. Larson, J. Wright, A. King, and R. Friedman, \u201cReal-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor,\u201d Proc. ISWC&apos;07, pp.37-40, 2007. DOI: 10.1109\/ISWC.2007.4373774. 10.1109\/ISWC.2007.4373774","DOI":"10.1109\/ISWC.2007.4373774"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] A.K. Bourke and G.M. Lyons, \u201cA threshold-based fall-detection algorithm using a bi-axial gyroscope sensor,\u201d Medical Engineering &amp; Physics, vol.30, no.1, pp.84-90, 2008. DOI: 10.1016\/j.medengphy.2006.12.001. 10.1016\/j.medengphy.2006.12.001","DOI":"10.1016\/j.medengphy.2006.12.001"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] J. Yao and J.R. Cooperstock, \u201cArm gesture detection in a classroom environment,\u201d Proc. WACV&apos;02, pp.153-157, 2002. DOI: 10.1109\/ACV.2002.1182174. 10.1109\/ACV.2002.1182174","DOI":"10.1109\/ACV.2002.1182174"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] M. Jiang, K. Jin, and J. Kong, \u201cAction recognition using multi-temporal DMMs based on adaptive vague division,\u201d Proc. ICIGP 2018, pp.8-13, 2018. DOI: 10.1145\/3191442.3191462. 10.1145\/3191442.3191462","DOI":"10.1145\/3191442.3191462"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] L. Qi, X. Lu, and X. Li, \u201cAction recognition by jointly using video proposal and trajectory,\u201d Proc. ICVISP 2018, pp.1-7, 2018. DOI: 10.1145\/3271553.3271563. 10.1145\/3271553.3271563","DOI":"10.1145\/3271553.3271563"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] E. Gianaria, N. Balossino, M. Grangetto, and M. Lucenteforte, \u201cGait characterization using dynamic skeleton acquisition,\u201d Proc. MMSP&apos;13, pp.440-445, 2013. DOI: 10.1109\/MMSP.2013.6659329. 10.1109\/MMSP.2013.6659329","DOI":"10.1109\/MMSP.2013.6659329"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] J. Wang, Z. Liu, J. Chorowski, Z. Chen, and Y. Wu, \u201cRobust 3D action recognition with random occupancy patterns,\u201d Proc. ECCV 2012, pp.872-885, 2012. DOI: 10.1007\/978-3-642-33709-3_62. 10.1007\/978-3-642-33709-3_62","DOI":"10.1007\/978-3-642-33709-3_62"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] C. Chen, Z. Hou, B. Zhang, J. Jiang, and Y. Yang, \u201cGradient local auto-correlations and extreme learning machine for depth-based activity recognition,\u201d Proc. ISVC 2015, pp.613-623, 2015. DOI: 10.1007\/978-3-319-27857-5_55. 10.1007\/978-3-319-27857-5_55","DOI":"10.1007\/978-3-319-27857-5_55"},{"key":"36","doi-asserted-by":"publisher","unstructured":"[36] C. Chen, K. Liu, and N. Kehtarnavaz, \u201cReal-time human action recognition based on depth motion maps,\u201d Journal of Real-Time Image Processing, vol.12, no.1, pp.155-163, 2016. DOI: 10.1007\/s11554-013-0370-1. 10.1007\/s11554-013-0370-1","DOI":"10.1007\/s11554-013-0370-1"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] C. Chen, R. Jafari, and N. Kehtarnavaz, \u201cAction recognition from depth sequences using depth motion maps-based local binary patterns,\u201d Proc. WACV 2015, pp.1092-1099, 2015. DOI: 10.1109\/WACV.2015.150. 10.1109\/WACV.2015.150","DOI":"10.1109\/WACV.2015.150"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] X. Yang and Y. Tian, \u201cPolynormal Fisher vector for activity recognition from depth sequences,\u201d Proc. SA&apos;14, pp.1-4, 2014. DOI: 10.1145\/2668956.2668962. 10.1145\/2668956.2668962","DOI":"10.1145\/2668956.2668962"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[39] A.S.M.H. Bari and M.L. Gavrilova, \u201cMulti-layer perceptron architecture for Kinect-based gait recognition,\u201d Proc. CGI 2019, pp.356-363, 2019. DOI: 10.1007\/978-3-030-22514-8_31. 10.1007\/978-3-030-22514-8_31","DOI":"10.1007\/978-3-030-22514-8_31"},{"key":"40","doi-asserted-by":"publisher","unstructured":"[40] C. Sun, T. Zhang, and C. Xu, \u201cLatent support vector machine modeling for sign language recognition with Kinect,\u201d ACM Transactions on Intelligent Systems and Technology, vol.6, no.2, pp.1-20, 2015. DOI: 10.1145\/2629481. 10.1145\/2629481","DOI":"10.1145\/2629481"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] F. Ord\u00f3\u00f1ez and D. Roggen, \u201cDeep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition,\u201d Sensors, vol.16, no.1, pp.115-140, 2016. DOI: 10.3390\/s16010115. 10.3390\/s16010115","DOI":"10.3390\/s16010115"},{"key":"42","doi-asserted-by":"publisher","unstructured":"[42] T.T. Thanh, F. Chen, K. Kotani, and B. Le, \u201cExtraction of discriminative patterns from skeleton sequences for accurate action recognition,\u201d Fundamenta Informaticae, vol.130, no.2, pp.247-261, 2014. DOI: 10.3233\/FI-2014-991. 10.3233\/FI-2014-991","DOI":"10.3233\/FI-2014-991"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] S. Sempena, N.U. Maulidevi, and P.R. Aryan, \u201cHuman action recognition using Dynamic Time Warping,\u201d Proc. ICEEI 2011, pp.1-5, 2011. DOI: 10.1109\/ICEEI.2011.6021605. 10.1109\/ICEEI.2011.6021605","DOI":"10.1109\/ICEEI.2011.6021605"},{"key":"44","doi-asserted-by":"publisher","unstructured":"[44] H. Sakoe and S. Chiba, \u201cDynamic programming algorithm optimization for spoken word recognition,\u201d IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.26, no.1, pp.43-49, 1978. DOI: 10.1109\/TASSP.1978.1163055. 10.1109\/TASSP.1978.1163055","DOI":"10.1109\/TASSP.1978.1163055"},{"key":"45","unstructured":"[45] C. 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