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Latest Developments of Gesture Recognition for Human–Robot Collaboration

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Advanced Human-Robot Collaboration in Manufacturing

Abstract

Recently, the concept of human–robot collaboration has raised many research interests. Instead of robots replacing human operators in workplaces, human–robot collaboration is the direction that allows human operators and robots to work together. Although the communication channels between human operators and robots are still limited, gesture recognition has been effectively applied as the interface between humans and computers for a long time. Covering some of the most important technologies and algorithms of gesture recognition, this chapter is intended to provide an overview of the gesture recognition research and explore the possibility to apply gesture recognition in human–robot collaboration. In this chapter, an overall model of gesture recognition for human–robot collaboration is also proposed. There are four essential technical components in the model of gesture recognition for human–robot collaboration: sensor technologies, gesture identification, gesture tracking and gesture classification. Reviewed approaches are classified according to the four essential technical components. After the reviewed technical components, an example of gesture recognition for human–robot collaboration is provided. In the last part of the chapter, future research trends are outlined.

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References

  1. J. Krüger, T.K. Lien, A. Verl, Cooperation of human and machines in assembly lines, CIRP Ann. Manuf. Technol.58(2), 628–646 (2009)

    Google Scholar 

  2. S. Green, X. Chen, M. Billinnghurst, J.G. Chase, Human Robot collaboration: an augmented reality approach a literature review and analysis. Mechatronics 5(1), 1–10 (2007)

    Google Scholar 

  3. P.R. Cohen, H.J. Levesque, Teamwork, in Nous (1991) pp. 487–512

    Google Scholar 

  4. L.S. Vygotsky, Mind in Society: The Development of Higher Psychological Processes (Harvard university press, 1980)

    Google Scholar 

  5. P.R. Cohen, H.J. Levesque, Persistence, intention, and commitment, in Reason. About Actions Plans (1990), pp. 297–340

    Google Scholar 

  6. C. Breazeal, A. Brooks, J. Gray, G. Hoffman, C. Kidd, H. Lee, J. Lieberman, A. Lockerd, D. Mulanda, Humanoid robots as cooperative partners for people. Int. J. Humanoid Robot. 1(2), 1–34 (2004)

    Article  Google Scholar 

  7. A. Bauer, D. Wollherr, M. Buss, Human–robot collaboration: a survey. Int. J. Humanoid Robot. 5(01), 47–66 (2008)

    Article  Google Scholar 

  8. S. Mitra, T. Acharya, Gesture recognition: a survey. IEEE Trans. Syst. Man, Cybern. Part C Appl. Rev. 37(3), 311–324 (2007)

    Google Scholar 

  9. R. Parasuraman, T.B. Sheridan, C.D. Wickens, A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. Part A Syst. Humans30(3), 286–297 (2000)

    Google Scholar 

  10. T.E. Starner, Visual recognition of american sign language using hidden markov models (1995)

    Google Scholar 

  11. T. Starner, J. Weaver, A. Pentland, Real-time american sign language recognition using desk and wearable computer based video. IEEE Trans Pattern Anal Mach Intell 20(12), 1371–1375 (1998)

    Article  Google Scholar 

  12. N.R. Howe, M.E. Leventon, W.T. Freeman, Bayesian reconstruction of 3d human motion from single-camera video. NIPS 99, 820–826 (1999)

    Google Scholar 

  13. Y. Katsuki, Y. Yamakawa, M. Ishikawa, High-speed human/Robot hand interaction system, in Proceedings of the Tenth Annual ACM/IEEE International Conference on Human–Robot Interaction Extended Abstracts (ACM, 2015), pp. 117–118

    Google Scholar 

  14. M. Elmezain, A. Al-Hamadi, J. Appenrodt, B. Michaelis, A hidden markov model-based continuous gesture recognition system for hand motion trajectory, in 19th International Conference on Pattern Recognition, 2008. ICPR 2008 (IEEE, 2008), pp. 1–4

    Google Scholar 

  15. Y. Matsumoto, A. Zelinsky, An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement, in Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000. Proceedings (IEEE, 2000), pp. 499–504

    Google Scholar 

  16. J.P. Wachs, M. Kölsch, H. Stern, Y. Edan, Vision-based hand-gesture applications. Commun. ACM 54(2), 60–71 (2011)

    Article  Google Scholar 

  17. J. Suarez, R.R. Murphy, Hand gesture recognition with depth images: a review, in RO-MAN, 2012 IEEE (IEEE, 2012), pp. 411–417

    Google Scholar 

  18. P. Doliotis, A. Stefan, C. McMurrough, D. Eckhard, V. Athitsos, Comparing gesture recognition accuracy using color and depth information, in Proceedings of the 4th International Conference on Pervasive Technologies Related to Assistive Environments (ACM, 2011), p. 20

    Google Scholar 

  19. M. Hansard, S. Lee, O. Choi, R.P. Horaud, Time-of-Flight Cameras: Principles, Methods and Applications (Springer Science & Business Media, 2012)

    Google Scholar 

  20. T. Kapuściński, M. Oszust, M. Wysocki, Hand gesture recognition using time-of-flight camera and viewpoint feature histogram, in Intelligent Systems in Technical and Medical Diagnostics (Springer, 2014), pp. 403–414

    Google Scholar 

  21. S.B. Gokturk, H. Yalcin, C. Bamji, A time-of-flight depth sensor-system description, issues and solutions, in Conference on Computer Vision and Pattern Recognition Workshop, 2004. CVPRW’04 (IEEE, 2004), p. 35

    Google Scholar 

  22. J.D. Arango Paredes, B. Munoz, W. Agredo, Y. Ariza-Araujo, J.L. Orozco, A. Navarro, A reliability assessment software using Kinect to complement the clinical evaluation of Parkinson’s disease, in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2015), pp. 6860–6863

    Google Scholar 

  23. C. Wang, Z. Liu, S.-C. Chan, Superpixel-based hand gesture recognition with Kinect depth camera. IEEE Trans Multimed 17(1), 29–39 (2015)

    Article  Google Scholar 

  24. H. Liu, Y. Wang, W. Ji, L. Wang, A context-aware safety system for human–robot collaboration. Procedia Manuf. 17, 238–245 (2018)

    Article  Google Scholar 

  25. A. Erol, G. Bebis, M. Nicolescu, R.D. Boyle, X. Twombly, Vision-based hand pose estimation: a review. Comput. Vis. Image Underst. 108(1), 52–73 (2007)

    Article  Google Scholar 

  26. T. Labs, Myo (2015). https://www.myo.com/

  27. Y. Zhang, C. Harrison, Tomo: wearable, low-cost electrical impedance tomography for hand gesture recognition, in Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (ACM, 2015), pp. 167–173

    Google Scholar 

  28. N. Haroon, A.N. Malik, Multiple hand gesture recognition using surface EMG signals. J. Biomed. Eng. Med. Imaging 3(1), 1 (2016)

    Article  Google Scholar 

  29. S. Roy, S. Ghosh, A. Barat, M. Chattopadhyay, and D. Chowdhury, Real-time implementation of electromyography for hand gesture detection using micro accelerometer, in Artificial Intelligence and Evolutionary Computations in Engineering Systems (Springer, 2016), pp. 357–364

    Google Scholar 

  30. Google, Project soli (2015). https://www.google.com/atap/project-soli/

  31. J. Smith, T. White, C. Dodge, J. Paradiso, N. Gershenfeld, D. Allport, Electric field sensing for graphical interfaces. IEEE Comput. Graph. Appl. 18(3), 54–60 (1998)

    Article  Google Scholar 

  32. F. Adib, C.-Y. Hsu, H. Mao, D. Katabi, F. Durand, Capturing the human figure through a wall. ACM Trans. Graph. 34(6), 219 (2015)

    Article  Google Scholar 

  33. F. Adib, D. Katabi, See Through Walls with WIFI!, vol. 43, no. 4 (ACM, 2013)

    Google Scholar 

  34. F. Adib, Z. Kabelac, D. Katabi, R.C. Miller, 3d tracking via body radio reflections, in Usenix NSDI, vol. 14 (2014)

    Google Scholar 

  35. J. Letessier, F. Bérard, Visual tracking of bare fingers for interactive surfaces, in Proceedings of the 17th Annual ACM Symposium on User Interface Software and Technology (ACM, 2004), pp. 119–122

    Google Scholar 

  36. D. Weinland, R. Ronfard, E. Boyer, A survey of vision-based methods for action representation, segmentation and recognition. Comput. Vis. Image Underst. 115(2), 224–241 (2011)

    Article  Google Scholar 

  37. D.G. Lowe, Object recognition from local scale-invariant features, in The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol. 2 (IEEE, 1999), pp. 1150–1157

    Google Scholar 

  38. H. Bay, T. Tuytelaars, L. Van Gool, Surf: speeded up robust features, in Computer Vision–ECCV 2006 (Springer, 2006), pp. 404–417

    Google Scholar 

  39. E. Rublee, V. Rabaud, K. Konolige, G. Bradski, ORB: an efficient alternative to SIFT or SURF, in 2011 IEEE International Conference on Computer Vision (ICCV) (IEEE, 2011), pp. 2564–2571

    Google Scholar 

  40. S. Belongie, J. Malik, J. Puzicha, Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)

    Article  Google Scholar 

  41. B. Allen, B. Curless, Z. Popović, Articulated body deformation from range scan data. ACM Trans. Graph. (TOG)21(3), 612–619 (ACM, 2002)

    Google Scholar 

  42. R. Cutler, M. Turk, View-based interpretation of real-time optical flow for gesture recognition, fg (IEEE, 1998), p. 416

    Google Scholar 

  43. J.L. Barron, D.J. Fleet, S.S. Beauchemin, Performance of optical flow techniques. Int. J. Comput. Vis. 12(1), 43–77 (1994)

    Article  Google Scholar 

  44. C. Thurau, V. Hlaváč, Pose primitive based human action recognition in videos or still images, in IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008 (IEEE, 2008), pp. 1–8

    Google Scholar 

  45. Q. Pu, S. Gupta, S. Gollakota, S. Patel, Whole-home gesture recognition using wireless signals, in Proceedings of the 19th annual international conference on Mobile computing & networking (ACM, 2013), pp. 27–38

    Google Scholar 

  46. R. Ronfard, C. Schmid, B. Triggs, Learning to parse pictures of people, in Computer Vision—ECCV 2002 (Springer, 2002), pp. 700–714

    Google Scholar 

  47. S.-J. Lee, C.-S. Ouyang, S.-H. Du, A neuro-fuzzy approach for segmentation of human objects in image sequences. IEEE Trans. Syst. Man, Cybern. Part B Cybern33(3), 420–437 (2003)

    Google Scholar 

  48. D. Tang, H.J. Chang, A. Tejani, T.-K. Kim, Latent regression forest: structured estimation of 3D articulated hand posture, in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2014), pp. 3786–3793

    Google Scholar 

  49. J. Han, L. Shao, D. Xu, J. Shotton, Enhanced computer vision with microsoft kinect sensor: a review. IEEE Trans Cybern. 43(5), 1318–1334 (2013)

    Article  Google Scholar 

  50. I. Oikonomidis, N. Kyriazis, A.A. Argyros, Efficient model-based 3D tracking of hand articulations using Kinect. BMVC 1(2), 3 (2011)

    Google Scholar 

  51. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, A. Blake, Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)

    Article  Google Scholar 

  52. M. Ye, X. Wang, R. Yang, L. Ren, M. Pollefeys, Accurate 3D pose estimation from a single depth image, in IEEE International Conference on Computer Vision (ICCV), 2011 (IEEE, 2011), pp. 731–738

    Google Scholar 

  53. Y. Li, Hand gesture recognition using Kinect, in 2012 IEEE 3rd International Conference on Software Engineering and Service Science (ICSESS) (IEEE, 2012), pp. 196–199

    Google Scholar 

  54. H. Liu, T. Fang, T. Zhou, Y. Wang, L. Wang, Deep learning-based multimodal control interface for human–robot collaboration. Procedia CIRP 72, 3–8 (2018)

    Article  Google Scholar 

  55. D. Comaniciu, V. Ramesh, P. Meer, Real-time tracking of non-rigid objects using mean shift, in IEEE Conference on Computer Vision and Pattern Recognition, 2000. Proceedings, vol 2 (IEEE, 2000), pp. 142–149

    Google Scholar 

  56. R.E. Kalman, A new approach to linear filtering and prediction problems. J. Fluids Eng. 82(1), 35–45 (1960)

    MathSciNet  Google Scholar 

  57. S. Haykin, Kalman Filtering and Neural Networks, vol. 47 (Wiley, 2004)

    Google Scholar 

  58. R. Kandepu, B. Foss, L. Imsland, Applying the unscented Kalman filter for nonlinear state estimation. J. Process Control 18(7), 753–768 (2008)

    Article  Google Scholar 

  59. R. Gao, L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, M. Helu, Cloud-enabled prognosis for manufacturing. CIRP Ann. Technol. 64(2), 749–772 (2015)

    Article  Google Scholar 

  60. J. Krüger, T.K. Lien, A. Verl, Cooperation of human and machines in assembly lines. CIRP Ann. Technol. 58(2), 628–646 (2009)

    Article  Google Scholar 

  61. K. Okuma, A. Taleghani, N. De Freitas, J.J. Little, D.G. Lowe, A boosted particle filter: multitarget detection and tracking, in Computer Vision-ECCV 2004 (Springer, 2004), pp. 28–39

    Google Scholar 

  62. S. Oron, A. Bar-Hillel, D. Levi, S. Avidan, Locally orderless tracking, in 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2012), pp. 1940–1947

    Google Scholar 

  63. J. Kwon, K.M. Lee, Tracking by sampling trackers, in 2011 IEEE International Conference on Computer Vision (ICCV) (IEEE, 2011), pp. 1195–1202

    Google Scholar 

  64. T. Li, S. Sun, T.P. Sattar, J.M. Corchado, Fight sample degeneracy and impoverishment in particle filters: a review of intelligent approaches. Expert Syst. Appl. 41(8), 3944–3954 (2014)

    Article  Google Scholar 

  65. T. Li, T.P. Sattar, S. Sun, Deterministic resampling: unbiased sampling to avoid sample impoverishment in particle filters. Signal Process 92(7), 1637–1645 (2012)

    Article  Google Scholar 

  66. J.M. Del Rincón, D. Makris, C.O. Uruňuela, J.-C. Nebel, Tracking human position and lower body parts using Kalman and particle filters constrained by human biomechanics. IEEE Trans. Syst. Man, Cybern. Part B Cybern.41(1), 26–37 (2011)

    Google Scholar 

  67. D.A. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)

    Article  Google Scholar 

  68. J. Kwon, K.M. Lee, F.C. Park, Visual tracking via geometric particle filtering on the affine group with optimal importance functions, in IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009 (IEEE, 2009), pp. 991–998

    Google Scholar 

  69. Z. Kalal, J. Matas, K. Mikolajczyk, P-N learning: bootstrapping binary classifiers by structural constraints, in 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2010), pp. 49–56

    Google Scholar 

  70. B. Babenko, M.-H. Yang, S. Belongie, Visual tracking with online multiple instance learning, in IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009 (IEEE, 2009), pp. 983–990

    Google Scholar 

  71. A.W.M. Smeulders, D.M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, M. Shah, Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1442–1468 (2014)

    Article  Google Scholar 

  72. A.D. Wilson, A.F. Bobick, Parametric hidden markov models for gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 884–900 (1999)

    Article  Google Scholar 

  73. S. Lu, J. Picone, S. Kong, Fingerspelling alphabet recognition using a two-level hidden markov model, in Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2013), p. 1

    Google Scholar 

  74. J. McCormick, K. Vincs, S. Nahavandi, D. Creighton, S. Hutchison, Teaching a digital performing agent: artificial neural network and hidden Markov model for recognising and performing dance movement, in Proceedings of the 2014 International Workshop on Movement and Computing (ACM, 2014), p. 70

    Google Scholar 

  75. S.-Z. Yu, Hidden semi-Markov models. Artif. Intell. 174(2), 215–243 (2010)

    Article  MathSciNet  Google Scholar 

  76. H. Liu, L. Wang, Human motion prediction for human–robot collaboration. J. Manuf. Syst. 44, 287–294 (2017)

    Article  Google Scholar 

  77. L.R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition. IEEE Proc. 77(2), 257–286 (1989)

    Article  Google Scholar 

  78. M.A. Hearst, S.T. Dumais, E. Osman, J. Platt, B. Scholkopf, Support vector machines. IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998)

    Article  Google Scholar 

  79. M.E. Tipping, Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211–244 (2001)

    MathSciNet  MATH  Google Scholar 

  80. B. Schiilkopf, The kernel trick for distances, in Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference, vol. 13 (MIT Press, 2001), p. 301

    Google Scholar 

  81. A. Cenedese, G.A. Susto, G. Belgioioso, G.I. Cirillo, F. Fraccaroli, Home automation oriented gesture classification from inertial measurements. IEEE Trans. Autom. Sci. Eng. 12(4), 1200–1210 (2015)

    Article  Google Scholar 

  82. K. Feng, F. Yuan, Static hand gesture recognition based on HOG characters and support vector machines, in 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA) (IEEE, 2013), pp. 936–938

    Google Scholar 

  83. D. Ghimire, J. Lee, Geometric feature-based facial expression recognition in image sequences using multi-class adaboost and support vector machines. Sensors 13(6), 7714–7734 (2013)

    Article  Google Scholar 

  84. O. Patsadu, C. Nukoolkit, B. Watanapa, Human gesture recognition using Kinect camera, in 2012 International Joint Conference on Computer Science and Software Engineering (JCSSE) (IEEE, 2012), pp. 28–32

    Google Scholar 

  85. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems (2012), pp. 1097–1105

    Google Scholar 

  86. Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. IEEE Proc. 86(11), 2278–2323 (1998)

    Article  Google Scholar 

  87. H. Liu, L. Wang, Remote human–robot collaboration: a cyber–physical system application for hazard manufacturing environment. J. Manuf. Syst. 54, 24–34 (2020)

    Article  Google Scholar 

  88. H. Liu, T. Fang, T. Zhou, L. Wang, Towards robust human–robot collaborative manufacturing: multimodal fusion. IEEE Access 6, 74762–74771 (2018)

    Article  Google Scholar 

  89. P. Wang, H. Liu, L. Wang, R.X. Gao, Deep learning-based human motion recognition for predictive context-aware human–robot collaboration. CIRP Ann. (2018)

    Google Scholar 

  90. S. Hochreiter, J. Urgen Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Google Scholar 

  91. L. Wang, R.X. Gao, J. Váncza, J. Krüger, X.V. Wang, S. Makris, G. Chryssolouris, Symbiotic human–robot collaborative assembly. CIRP Ann. Manuf. Technol.68(2), 701–726 (2019)

    Google Scholar 

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Authors and Affiliations

  1. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

    Hongyi Liu & Lihui Wang

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  1. Hongyi Liu
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  2. Lihui Wang
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Correspondence to Lihui Wang .

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Editors and Affiliations

  1. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

    Lihui Wang

  2. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

    Xi Vincent Wang

  3. Research Laboratory on Engineering and Management Intelligence, SZTAKI Institute for Computer Science and Control, Eötvös Loránd Research Network, Budapest, Hungary

    József Váncza

  4. Research Laboratory on Engineering and Management Intelligence, SZTAKI Institute for Computer Science and Control, Eötvös Loránd Research Network, Budapest, Hungary

    Zsolt Kemény

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Liu, H., Wang, L. (2021). Latest Developments of Gesture Recognition for Human–Robot Collaboration. In: Wang, L., Wang, X.V., Váncza, J., Kemény, Z. (eds) Advanced Human-Robot Collaboration in Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-030-69178-3_2

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