Advertisement

State of the Art

  • Jorge Luis Reyes OrtizEmail author
Chapter
  • 1.1k Downloads
Part of the Springer Theses book series (Springer Theses)

Abstract

This chapter examines the current state of the art on the subject of HAR. It starts with a general introduction regarding the HAR pipeline and then focuses on various already implemented HAR systems relevant to our research. It also highlights particular aspects of these systems such as sensing technologies, types of activities, ML approaches and real-time computing.

Keywords

Feature Selection Hide Markov Model Gaussian Mixture Model Inertial Sensor Wearable Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. R.F. Allen, E. Ambikairajah, N.H. Lovell, B.G. Celler, Classification of a known sequence of motions and postures from accelerometry data using adapted gaussian mixture models. Physiol. Meas. 27, 935 (2006)CrossRefGoogle Scholar
  2. K. Altun, B. Barshan, in Human activity recognition using inertial/magnetic sensor units, Human Behavior Understanding, 2010Google Scholar
  3. O. Amft, C. Lombriser, T. Stiefmeier, G. Tröster, in Recognition of user activity sequences using distributed event detection, European Conference on Smart Sensing and Context, 2007Google Scholar
  4. L. Atallah, B. Lo, R. King, G.-Z. Yang, in Sensor placement for activity detection using wearable accelerometers, International Conference on Body Sensor Networks, 2010Google Scholar
  5. A. Avci, S. Bosch, M. Marin-Perianu, R. Marin-Perianu, P. Havinga, in Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey, International Conference on Architecture of Computing Systems, 2010Google Scholar
  6. M. Bachlin, M. Plotnik, D. Roggen, I. Maidan, J.M. Hausdorff, N. Giladi, G. Troster, Wearable assistant for parkinson’s disease patients with the freezing of gait symptom. IEEE Trans. Inf. Technol. Biomed. 14, 436–446 (2010)CrossRefGoogle Scholar
  7. G. Bahle, P. Lukowicz, K. Kunze, K. Kise, in I see you: how to improve wearable activity recognition by leveraging information from environmental cameras, IEEE International Conference on Pervasive Computing and Communications Workshops, 2013Google Scholar
  8. L. Bao, S.S. Intille, in Activity recognition from user-annotated acceleration data. Pervasive Comput. (2004)Google Scholar
  9. M. Berchtold, M. Budde, D. Gordon, H.R. Schmidtke, M. Beigl, in Activity recognition service for mobile phones, International Symposium on Wearable Computers, 2010Google Scholar
  10. C.M. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2006)Google Scholar
  11. T. Brezmes, J.L. Gorricho, J. Cotrina, in Activity recognition from accelerometer data on a mobile phone, Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, 2009Google Scholar
  12. B. Bruno, F. Mastrogiovanni, A. Sgorbissa, T. Vernazza, R. Zaccaria, in Analysis of human behavior recognition algorithms based on acceleration data, IEEE International Conference on Robotics and Automation, 2013Google Scholar
  13. B. Bruno, F. Mastrogiovanni, A. Sgorbissa, T. Vernazza, R. Zaccaria, in Human motion modelling and recognition: a computational approach, IEEE International Conference on Automation Science and Engineering, 2012Google Scholar
  14. C. Cedras, M. Shah, Motion-based recognition a survey. Image Vis. Comput. 13, 129–155 (1995)CrossRefGoogle Scholar
  15. C.-C. Chang, C.-J. Lin, LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27–54 (2011)CrossRefGoogle Scholar
  16. L. Chen, C.D. Nugent, H. Wang, A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. Data Eng. 24, 961–974 (2012a).Google Scholar
  17. L. Chen, J. Hoey, C.D. Nugent, D.J. Cook, Z. Yu, Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. B Cybern. Part C: Appl. Rev. 42, 790–808 (2012b).Google Scholar
  18. T. Choudhury, S. Consolvo, B. Harrison, J. Hightower, A. LaMarca, L. Legrand, A. Rahimi, A. Rea, G. Bordello, B. Hemingway, P. Klasnja, K. Koscher, J.A. Landay, J. Lester, D. Wyatt, D. Haehnel, The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Comput. 7, 32–41 (2008)CrossRefGoogle Scholar
  19. B. Coley, B. Najafi, A. Paraschiv-Ionescu, K. Aminian, Stair climbing detection during daily physical activity using a miniature gyroscope. Gait Posture 22, 287–294 (2005)CrossRefGoogle Scholar
  20. J.D. Cook, S.K. Das, How smart are our environments? an updated look at the state of the art. Pervasive Mob. Comput. 3, 53–73 (2007)CrossRefGoogle Scholar
  21. D.J. Cook, S.K. Das, Pervasive computing at scale: transforming the state of the art. Pervasive Mob. Comput. 8, 22–35 (2012)CrossRefGoogle Scholar
  22. M. Ermes, J. Parkka, L. Cluitmans, in Advancing from offline to online activity recognition with wearable sensors, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008Google Scholar
  23. A. Ganapathiraju, J.E. Hamaker, J. Picone, Applications of support vector machines to speech recognition. IEEE Trans. Signal Process. 52, 2348–2355 (2004)CrossRefGoogle Scholar
  24. M. Gandetto, L. Marchesooti, S. Sciutto, D. Negroni, C.S. Regazzoni, in From multi-sensor surveillance towards smart interactive spaces, IEEE International Conference on Multimedia and Expo, 2003Google Scholar
  25. I. Guyon, A. Elisseeff, An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  26. Z. He, L. Jin, in Activity recognition from acceleration data based on discrete consine transform and svm, IEEE International Conference on Systems, Man and Cybernetics, 2009Google Scholar
  27. S. Herrlich, S. Spieth, R. Nouna, R. Zengerle, L. Giannola, D.-E. Pardo-Ayala, E. Federico, P. Garino, in Ambulatory treatment and telemonitoring of patients with parkinsons disease, Ambient Assisted Living, 2011Google Scholar
  28. L.C. Jatoba, U. Grossmann, C. Kunze, J. Ottenbacher, W. Stork, in Context-aware mobile health monitoring: evaluation of different pattern recognition methods for classification of physical activity, International Conference of the IEEE Engineering in Medicine and Biology Society, 2008Google Scholar
  29. M.D. Karantonis, M.R. Narayanan, M. Mathie, N.H. Lovell, B.G. Celler, Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed. 10, 156–167 (2006)Google Scholar
  30. A.M. Khan, Y.-K. Lee, S.Y. Lee, T.-S. Kim, in Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis, IEEE International Conference on Future Information Technology, 2010aGoogle Scholar
  31. A.M. Khan, Y.-K. Lee, S.Y. Lee, T.-S. Kim, A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans. Inf. Technol. Biomed. 14, 1166–1172 (2010b)CrossRefGoogle Scholar
  32. M. Kose, O.D. Incel, C. Ersoy, in Online human activity recognition on smart phones, Workshop on Mobile Sensing: From Smartphones and Wearables to Big Data, 2012Google Scholar
  33. J.R. Kwapisz, G.M. Weiss, S.A. Moore, Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12, 74–82 (2011)CrossRefGoogle Scholar
  34. D.N. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, A.T. Campbell, A survey of mobile phone sensing. IEEE Commun. Mag. 48, 140–150 (2010)Google Scholar
  35. O. Lara, M. Labrador, A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutorials 1, 1–18 (2012a)Google Scholar
  36. O.D. Lara, M.A. Labrador, in A mobile platform for real-time human activity recognition, IEEE Consumer Communications and Networking Conference, 2012bGoogle Scholar
  37. Ó.D. Lara, A.J. Pérez, M.A. Labrador, J.D. Posada, Centinela: a human activity recognition system based on acceleration and vital sign data. Pervasive Mob. Comput. 8, 717–729 (2012)CrossRefGoogle Scholar
  38. Y. LeCun, L. Jackel, L. Bottou, A. Brunot, C. Cortes, J. Denker, H. Drucker, I. Guyon, U. Müller, E. Säckinger, P. Simard, V. Vapnik, in Comparison of learning algorithms for handwritten digit recognition, International Conference on Artificial Neural Networks, 1995Google Scholar
  39. S.-W. Lee, K. Mase, Activity and location recognition using wearable sensors. IEEE Pervasive Comput. 1, 24–32 (2002)Google Scholar
  40. Q. Li, J.A. Stankovic, M.A. Hanson, A.T. Barth, J. Lach, G. Zhou, in Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information, Wearable and Implantable Body Sensor Networks, 2009Google Scholar
  41. W. Lin, M.-T. Sun, R. Poovandran, Z. Zhang, in Human activity recognition for video surveillance, IEEE International Symposium on Circuits and Systems, 2008Google Scholar
  42. C. Liu, Q. Zhu, K.A. Holroyd, E.K. Seng, Status and trends of mobile-health applications for ios devices: a developer’s perspective. J. Syst. Softw. 84, 2022–2033 (2011)CrossRefGoogle Scholar
  43. S.R. Lord, Falls in older people: risk factors and strategies for prevention (Cambridge University Press, 2007)Google Scholar
  44. P. Lukowicz, J. Ward, H. Junker, M. Stäger, G. Tröster, A. Atrash, T. Starner, Recognizing workshop activity using body worn microphones and accelerometers. Pervasive Comput. (2004)Google Scholar
  45. A. Mannini, A.M. Sabatini, Machine learning methods for classifying human physical activity from on-body accelerometers. Sensor, 10, 1154–1175 (2010)Google Scholar
  46. J. Mantyjarvi, J. Himberg, T. Seppanen, in Recognizing human motion with multiple acceleration sensors, IEEE International Conference on Systems, Man, and Cybernetics, 2001Google Scholar
  47. U. Maurer, A. Smailagic, D.P. Siewiorek, M. Deisher, in Activity recognition and monitoring using multiple sensors on different body positions, IEEE international workshop on wearable and implantable body sensor networksk, 2006Google Scholar
  48. S. Mellone, C. Tacconi, L. Chiari, Validity of a smartphone-based instrumented timed up and go. Gait Posture 36, 163–165 (2012)CrossRefGoogle Scholar
  49. B. Najafi, K. Aminian, A. Paraschiv-Ionescu, F. Loew, C.J. Bula, P. Robert, Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Trans. Biomed. Eng. 50, 711–723 (2003)CrossRefGoogle Scholar
  50. B. Nham, K. Siangliulue, S. Yeung, Predicting mode of transport from iphone accelerometer data, Technical report (Stanford University, 2008)Google Scholar
  51. R. Parasuraman, T.B. Sheridan, C.D. Wickens, A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. B Cybern. 30, 286–297 (2000)Google Scholar
  52. R. Poppe, Vision-based human motion analysis: an overview. Comput. Vis. Image Underst. 108, 4–18 (2007)CrossRefGoogle Scholar
  53. R. Poppe, A survey on vision-based human action recognition. Image Vis. Comput. 28, 976–990 (2010)CrossRefGoogle Scholar
  54. N. Ravi, N. Dandekar, P. Mysore, M.L. Littman, in Activity recognition from accelerometer data, Innovative Applications of Artificial Intelligence, 2005Google Scholar
  55. D. Riboni, C. Bettini, Cosar: hybrid reasoning for context-aware activity recognition. Pers. Ubiquit. Comput. 15, 271–289 (2011)CrossRefGoogle Scholar
  56. D. Rodríguez-Martín, A. Samà, C. Perez-Lopez, A. Català, J. Cabestany, A. Rodriguez-Molinero, Svm-based posture identification with a single waist-located triaxial accelerometer. Expert Syst. Appl. 40, 7203–7211 (2013)CrossRefGoogle Scholar
  57. A. Salarian, H. Russmann, F.J.G. Vingerhoets, P.R. Burkhard, K. Aminian, Ambulatory monitoring of physical activities in patients with parkinson’s disease. IEEE Trans. Biomed. Eng. 54, 2296–2299 (2007)CrossRefGoogle Scholar
  58. A. Sama, C. Perez-Lopez, J. Romagosa, D. Rodriguez-Martin, A. Catala, J. Cabestany, D.A. Perez-Martinez, A. Rodriguez-Molinero, in Dyskinesia and motor state detection in parkinson’s disease patients with a single movement sensor, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012Google Scholar
  59. A. Schmidt, K.A. Aidoo, A. Takaluoma, U. Tuomela, K. Van Laerhoven, W. Van de Velde, in Advanced interaction in context, Handheld and Ubiquitous Computing, 1999Google Scholar
  60. M. Stikic, K. van Laerhoven, B. Schiele, in Exploring semi-supervised and active learning for activity recognition, IEEE International Symposium on Wearable Computers, 2008Google Scholar
  61. M. Stikic, D. Larlus, S. Ebert, B. Schiele, Weakly supervised recognition of daily life activities with wearable sensors. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2521–2537 (2011)CrossRefGoogle Scholar
  62. B. Takač, A. Català, D.R. Martín, N. van der Aa, W. Chen, M. Rauterberg, Position and orientation tracking in a ubiquitous monitoring system for parkinson disease patients with freezing of gait symptom. J. Med. Internet Res. 15, 1 (2013)CrossRefGoogle Scholar
  63. E.M. Tapia, S.S. Intille, W. Haskell, K. Larson, J. Wright, A. King, R. Friedman, in Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor, IEEE International Symposium on Wearable Computers, 2007Google Scholar
  64. P. Turaga, R. Chellappa, V.S. Subrahmanian, O. Udrea, Machine recognition of human activities: a survey. IEEE Trans. Circuits Syst. Video Technol. 18, 1473–1488 (2008)CrossRefGoogle Scholar
  65. Z. Wang, H.M. Jiang, H.L. Yaohua, An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors. IEEE Trans. Inf. Technol. Biomed. 16, 691–699 (2012)CrossRefGoogle Scholar
  66. W. Wanmin, S. Dasgupta, E.E. Ramirez, C. Peterson, G.J. Norman, Classification accuracies of physical activities using smartphone motion sensors. J. Med. Internet Res. 14, 105–130 (2012)CrossRefGoogle Scholar
  67. M. Weiser, Some computer science issues in ubiquitous computing. Commun. ACM 36, 75–84 (1993)CrossRefGoogle Scholar
  68. D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)CrossRefGoogle Scholar
  69. D. Wyatt, M. Philipose, T. Choudhury, in Unsupervised activity recognition using automatically mined common sense, National Conference on Artificial Intelligence, 2005Google Scholar
  70. G.-Z. Yang, M. Yacoub, Body Sens. Netw. (Springer, New York, 2006)Google Scholar
  71. Z. Zhao, Y. Chen, J. Liu, M. Liu, in Cross-people motion activity recognition, International Joint Conference on Artificial Intelligence, 2010Google Scholar
  72. V.W. Zheng, D.H. Hu, Q. Yang, in Cross-domain activity recognition, International Conference on Ubiquitous Computing, 2009Google Scholar
  73. C. Zhu, W. Sheng, in Human daily activity recognition in robot-assisted living using multi-sensor fusion, IEEE Int. Conf. Robot. Autom. (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CETpDUniversitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations