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Transfer learning for activity recognition: a survey

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Abstract

Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper, we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed.

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References

  1. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the international conference on data engineering, pp 3–14

  2. Alemdar H, Ersoy C (2010) Wireless sensor networks for healthcare: a survey. Comput Netw 54(15):2688–2710. http://www.sciencedirect.com/science/article/pii/S1389128610001398

    Google Scholar 

  3. Arnold A, Nallapati R, Cohen W (2007) A comparative study of methods for transductive transfer learning. In: Data mining workshops, 2007. ICDM workshops 2007. Seventh IEEE international conference on, pp 77–82

  4. Avci A, Bosch S, Marin-Perianu M, Marin-Perianu R, Havinga P (2010) Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: Architecture of computing systems (ARCS), 2010 23rd international conference on, pp 1–10

  5. Barnett S, Ceci S (2002) When and where do we apply what we learn? A taxonomy for far transfer. Psychol Bull 128(4):612–637

    Article  Google Scholar 

  6. Blanke U, Schiele B (2010) Remember and transfer what you have learned-recognizing composite activities based on activity spotting. In: Wearable computers (ISWC), 2010 international symposium on, IEEE, pp 1–8

  7. Byrnes J (1996) Cognitive development and learning in instructional contexts. Allyn and Bacon, Boston

    Google Scholar 

  8. Calatroni A, Roggen D, Tröster G (2011) Automatic transfer of activity recognition capabilities between body-worn motion sensors: training newcomers to recognize locomotion. In: Eighth international conference on networked sensing systems (INSS’11), Penghu, Taiwan

  9. Cao L, Liu Z, Huang T (2010) Cross-dataset action detection. In: Computer vision and pattern recognition (CVPR), 2010 IEEE conference on, pp 1998–2005

  10. Chan M, Estve D, Escriba C, Campo E (2008) A review of smart homes-present state and future challenges. Comput Methods Programs Biomed 91(1):55–81

    Article  Google Scholar 

  11. Chang C-C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27:1–27:27

    Article  Google Scholar 

  12. Chattopadhyay R, Krishnan N, Panchanathan S (2011) Topology preserving domain adaptation for addressing subject based variability in semg signal. In: 2011 AAAI Spring symposium series

  13. Chieu H, Lee W, Kaelbling L (2006) Activity recognition from physiological data using conditional random fields. Technical report, Singapore-MIT Alliance (SMA)

  14. Cook D (2010) Learning setting-generalized activity models for smart spaces. Intell Syst IEEE PP(99):1

    Google Scholar 

  15. Dai W, Yang Q, Xue G-R, Yu Y (2007) Boosting for transfer learning. In: Proceedings of the 24th international conference on machine learning, ICML ’07, ACM, New York, NY, USA, pp 193–200

  16. Dai W, Yang Q, Xue G-R, Yu Y (2008) Self-taught clustering. In: Proceedings of the 25th international conference on Machine learning, ICML ’08, ACM, New York, NY, USA, pp 200–207

  17. Davis J, Domingos P (2009) Deep transfer via second-order markov logic. In: Proceedings of the 26th annual international conference on machine learning, ICML ’09, ACM, New York, NY, USA, pp 217–224

  18. Duan L, Xu D, Tsang I, Luo J (2010) Visual event recognition in videos by learning from web data. In: Computer vision and pattern recognition (CVPR), 2010 IEEE conference on, pp 1959–1966

  19. Elkan C (2001) The foundations of cost-sensitive learning. In: Proceedings of the 17th international joint conference on Artificial intelligence, vol 2, IJCAI’01. Morgan Kaufmann Publishers, San Francisco, CA, USA, pp 973–978

  20. Farhadi A, Tabrizi M (2008) Learning to recognize activities from the wrong view point. In: Forsyth D, Torr P, Zisserman A (eds) Computer vision ECCV 2008, vol 5302 of Lecture notes in computer science. Springer, Berlin/Heidelberg, pp 154–166

  21. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    Article  MathSciNet  MATH  Google Scholar 

  22. Gu T, Chen S, Tao X, Lu J (2010) An unsupervised approach to activity recognition and segmentation based on object-use fingerprints. Data Knowl Eng 69(6):533–544

    Article  Google Scholar 

  23. Hachiya H, Sugiyama M, Ueda N (2012) Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition. Neurocomputing 80(0):93–101. Special issue on machine learning for, signal processing 2010

    Google Scholar 

  24. Haigh K, Yanco H (2002) Automation as caregiver: a survey of issues and technologies. In: AAAI-02 workshop on automation as caregiver: the role of intelligent technology in, elder care, pp 39–53

  25. Hu D, Yang Q (2011) Transfer learning for activity recognition via sensor mapping. In: Twenty-second international joint conference on artificial intelligence

  26. Hu D, Zheng V, Yang Q (2010) Cross-domain activity recognition via transfer learning. Pervasive Mobile Comput 7(3):344–358

    Article  Google Scholar 

  27. Kasteren TL, Englebienne G, Kröse BJ (2010) An activity monitoring system for elderly care using generative and discriminative models. Pers Ubiquitous Comput 14(6):489–498

    Article  Google Scholar 

  28. Kim E, Helal S, Cook D (2010) Human activity recognition and pattern discovery. Pervasive Comput IEEE 9(1):48–53

    Article  Google Scholar 

  29. Krishnan N (2010) A computational framework for wearable accelerometer-based, PhD thesis, Arizona State University

  30. Krishnan N, Lade P, Panchanathan S (2010) Activity gesture spotting using a threshold model based on adaptive boosting. In: Multimedia and Expo (ICME), 2010 IEEE international conference on, pp 155–160

  31. Krishnan N, Panchanathan S (2008) Analysis of low resolution accelerometer data for continuous human activity recognition. In: Acoustics, speech and signal processing, 2008. ICASSP 2008. IEEE international conference on, pp 3337–3340

  32. Kurz M, Hölzl G, Ferscha A, Calatroni A, Roggen D, Tröster G (2011) Real-time transfer and evaluation of activity recognition capabilities in an opportunistic system. In: ADAPTIVE 2011, The third international conference on adaptive and self-adaptive systems and applications, pp 73–78

  33. Kwapisz JR, Weiss GM, Moore SA (2010) Activity recognition using cell phone accelerometers. In: Proceedings of the fourth international workshop on knowledge discovery from sensor data, pp 10–18

  34. Lam A, Roy-Chowdhury A, Shelton C (2011) Interactive event search through transfer learning. In: Kimmel R, Klette R, Sugimoto A (eds) Computer vision, ACCV 2010, vol 6494 of Lecture notes in computer science, Springer, Berlin/Heidelberg, pp 157–170

  35. Lester J, Choudhury T, Kern N, Borriello G, Hannaford B (2005) A hybrid discriminative/generative approach for modeling human activities. In: Proceedings of the 19th international joint conference on artificial intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 766–772

  36. Liao L, Fox D, Kautz H (2005) Location-based activity recognition using relational Markov networks. In: Proceedings of the 19th international joint conference on artificial intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 773–778

  37. Liu J, Shah M, Kuipers B, Savarese S (2011) Cross-view action recognition via view knowledge transfer. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE conference on, pp 3209–3216

  38. Logan B, Healey J, Philipose M, Tapia EM, Intille S (2007) A long-term evaluation of sensing modalities for activity recognition. In: Proceedings of the 9th international conference on Ubiquitous computing. Springer, Berlin, Heidelberg, pp 483–500

  39. Mahmud MM, Ray S (2008) Transfer learning using kolmogorov complexity: basic theory and empirical evaluations. In: Platt J, Koller D, Singer Y, Roweis S (eds) Advances in neural information processing systems 20. MIT Press, Cambridge, MA, pp 985–992

    Google Scholar 

  40. Maurer U, Smailagic A, Siewiorek D, Deisher M (2006) Activity recognition and monitoring using multiple sensors on different body positions. In: International workshop on wearable and implantable body sensor networks

  41. Mihalkova L, Huynh T, Mooney R (2007) Mapping and revising markov logic networks for transfer learning. In: Proceedings of the national conference on artificial intelligence, vol 22. AAAI Press, MIT Press, Menlo Park, CA, Cambridge, MA, London 1999, p 608

  42. Mihalkova L, Mooney R (2008) Transfer learning by mapping with minimal target data. In: Proceedings of the AAAI-08 workshop on transfer learning for complex tasks

  43. Mihalkova L, Mooney RJ (2009) Transfer learning from minimal target data by mapping across relational domains. In: Proceedings of the 21st international joint conference on artificial intelligence, IJCAI’09, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 1163–1168

  44. Nater F, Tommasi T, Grabner H, van Gool L, Caputo B (2011) Transferring activities: updating human behavior analysis (both first authors contributed equally). In: ICCV WS on visual surveillance

  45. Palmes P, Pung HK, Gu T, Xue W, Chen S (2010) Object relevance weight pattern mining for activity recognition and segmentation. Pervasive Mob Comput 6(1):43–57

    Article  Google Scholar 

  46. Pan J, Yang Q, Chang H, Yeung D (2006) A manifold regularization approach to calibration reduction for sensor-network based tracking. In: Proceedings of the national conference on artificial intelligence, vol 21, p 988

  47. Pan SJ, Tsang IW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199–210

    Article  Google Scholar 

  48. Pan S, Kwok J, Yang Q, Pan J (2007) Adaptive localization in a dynamic wifi environment through multi-view learning. In: Proceedings of the national conference on artificial Intelligence, vol 22, p 1108

  49. Pan S, Shen D, Yang Q, Kwok J (2008) Transferring localization models across space. In: Proceedings of the 23rd national conference on artificial intelligence, vol 3, pp 1383–1388

  50. Pan S, Yang Q (2010) A survey on transfer learning. Knowl Data Eng IEEE Trans 22(10):1345–1359

    Article  Google Scholar 

  51. Pan S, Zheng V, Yang Q, Hu D (2008) Transfer learning for wifi-based indoor localization. In: Association for the advancement of artificial intelligence (AAAI) workshop, p 6

  52. Philipose M, Fishkin KP, Perkowitz M, Patterson DJ, Fox D, Kautz H, Hahnel D (2004) Inferring activities from interactions with objects. IEEE Pervasive Comput 3:50–57

    Google Scholar 

  53. Raina R, Battle A, Lee H, Packer B, Ng AY (2007) Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th international conference on machine learning, ICML ’07, ACM, New York, NY, USA, pp 759–766

  54. Rashidi P, Cook D (2009) Transferring learned activities in smart environments. In: 5th international conference on intelligent environments, vol 2, pp 185–192

  55. Rashidi P, Cook D (2010a) Activity recognition based on home to home transfer learning. In: AAAI workshop on plan, activity, and intent recognition

  56. Rashidi P, Cook D (2010b) Multi home transfer learning for resident activity discovery and recognition. In: KDD knowledge discovery from sensor data, pp 56–63

  57. Rashidi P, Cook D (2011) Activity knowledge transfer in smart environments. Pervasive Mob Comput 7(3):331–343

    Article  Google Scholar 

  58. Rashidi P, Cook D, Holder L, Schmitter-Edgecombe M (2011) Discovering activities to recognize and track in a smart environment. IEEE Trans Knowl Data Eng 23(4):527–539

    Article  Google Scholar 

  59. Roggen D, Frster K, Calatroni A, Trster G (2011) The adarc pattern analysis architecture for adaptive human activity recognition systems. J Ambient Intell Humaniz Comput. Online 1–18: doi:10.1007/s12652-011-0064-0

  60. Rosenstein MT, Marx Z, Kaelbling LP, Dietterich TG (2005) To transfer or not to transfer. In: In NIPS05 workshop, inductive transfer: 10 years later

  61. Taylor M, Stone P (2009) Transfer learning for reinforcement learning domains: a survey. J Mach Learn Res 10:1633–1685

    MathSciNet  MATH  Google Scholar 

  62. Tenenbaum JB, Vd Silva, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  63. Thorndike E, Woodworth R (1901) The influence of improvement in one mental function upon the efficiency of other functions. (i). Psychol Rev 8(3):247–261

    Article  Google Scholar 

  64. Thrun S (1996) Explanation-based neural network learning: a lifelong learning approach. Kluwer, Berlin

    Book  MATH  Google Scholar 

  65. Thrun S, Pratt L (1998) Learning to learn. Kluwer, Berlin

    Book  MATH  Google Scholar 

  66. van Kasteren T, Englebienne G, Kröse B (2008) Recognizing activities in multiple contexts using transfer learning. In: AAAI AI in eldercare symposium

  67. van Kasteren T, Englebienne G, Krse B (2010) Transferring knowledge of activity recognition across sensor networks. In: Floren P, Krger A, Spasojevic M (eds) Pervasive computing, vol 6030 of Lecture notes in computer science, Springer, Berlin/Heidelberg, pp 283–300

  68. Venkatesan A (2011) A study of boosting based transfer learning for activity and gesture recognition, PhD thesis, Arizona State University

  69. Venkatesan A, Krishnan N, Panchanathan S (2010) Cost-sensitive boosting for concept drift. In: International workshop on handling concept drift in adaptive information systems 2010, pp 41–47

  70. Vilalta R, Drissi Y (2002) A perspective view and survey of meta-learning. Artif Intell Rev 18:77–95

    Article  Google Scholar 

  71. Wang Z, Song Y, Zhang C (2008) Transferred dimensionality reduction. In: Daelemans W, Goethals B, Morik K (eds) Machine learning and knowledge discovery in databases, vol 5212 of Lecture notes in computer science. Springer, Berlin/Heidelberg, pp 550–565

  72. Wei B, Pal C (2011) Heterogeneous transfer learning with rbms. In: Twenty-fifth AAAI conference on artificial intelligence

  73. Wu C, Khalili AH, Aghajan H (2010) Multiview activity recognition in smart homes with spatio-temporal features. In: Proceedings of the fourth ACM/IEEE international conference on distributed smart cameras, ICDSC ’10, ACM, New York, NY, USA, pp 142–149

  74. Xian-ming L, Shao-zi L (2009) Transfer adaboost learning for action recognition. In: IT in medicine education, 2009. ITIME ’09. IEEE international symposium on, vol 1, pp 659–664

  75. Yang J, Yan R, Hauptmann AG (2007) Cross-domain video concept detection using adaptive svms. In: Proceedings of the 15th international conference on multimedia, MULTIMEDIA ’07. ACM, New York, NY, USA, pp 188–197

  76. Yang Q (2009) Activity recognition: linking low-level sensors to high-level intelligence. In: Proceedings of the 21st international joint conference on artificial intelligence. Morgan Kaufmann Publishers, pp 20–25

  77. Yang W, Wang Y, Mori G (2011) Learning transferable distance functions for human action recognition. In: Wang L, Zhao G, Cheng L, Pietikinen M (eds) Machine learning for vision-based motion analysis. Advances in pattern recognition. Springer, London, pp 349–370

    Chapter  Google Scholar 

  78. Zhao Z, Chen Y, Liu J, Liu M (2010) Cross-mobile elm based activity recognition. Int J Eng Ind 1(1):30–38

    Article  MathSciNet  Google Scholar 

  79. Zhao Z, Chen Y, Liu J, Shen Z, Liu M (2011) Cross-people mobile-phone based activity recognition. In: Twenty-second international joint conference on artificial intelligence

  80. Zheng V, Hu D, Yang Q (2009) Cross-domain activity recognition. In: Ubicomp, vol 9, pp 61–70

  81. Zheng V, Pan S, Yang Q, Pan J (2008) Transferring multi-device localization models using latent multi-task learning. In: Proceedings of the 23rd national conference on, Artificial intelligence, pp 1427–1432

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99163, USA

    Diane Cook, Kyle D. Feuz & Narayanan C. Krishnan

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  1. Diane Cook
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  2. Kyle D. Feuz
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  3. Narayanan C. Krishnan
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Correspondence to Diane Cook.

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Cook, D., Feuz, K.D. & Krishnan, N.C. Transfer learning for activity recognition: a survey. Knowl Inf Syst 36, 537–556 (2013). https://doi.org/10.1007/s10115-013-0665-3

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