State of the Art
Chapter
First Online:
- 1.1k Downloads
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
- 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
- K. Altun, B. Barshan, in Human activity recognition using inertial/magnetic sensor units, Human Behavior Understanding, 2010Google Scholar
- 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
- 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
- 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
- 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
- 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
- L. Bao, S.S. Intille, in Activity recognition from user-annotated acceleration data. Pervasive Comput. (2004)Google Scholar
- 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
- C.M. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2006)Google Scholar
- 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
- 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
- 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
- C. Cedras, M. Shah, Motion-based recognition a survey. Image Vis. Comput. 13, 129–155 (1995)CrossRefGoogle Scholar
- C.-C. Chang, C.-J. Lin, LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27–54 (2011)CrossRefGoogle Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- I. Guyon, A. Elisseeff, An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- J.R. Kwapisz, G.M. Weiss, S.A. Moore, Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12, 74–82 (2011)CrossRefGoogle Scholar
- 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
- O. Lara, M. Labrador, A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutorials 1, 1–18 (2012a)Google Scholar
- O.D. Lara, M.A. Labrador, in A mobile platform for real-time human activity recognition, IEEE Consumer Communications and Networking Conference, 2012bGoogle Scholar
- 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
- 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
- S.-W. Lee, K. Mase, Activity and location recognition using wearable sensors. IEEE Pervasive Comput. 1, 24–32 (2002)Google Scholar
- 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
- 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
- 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
- S.R. Lord, Falls in older people: risk factors and strategies for prevention (Cambridge University Press, 2007)Google Scholar
- 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
- A. Mannini, A.M. Sabatini, Machine learning methods for classifying human physical activity from on-body accelerometers. Sensor, 10, 1154–1175 (2010)Google Scholar
- J. Mantyjarvi, J. Himberg, T. Seppanen, in Recognizing human motion with multiple acceleration sensors, IEEE International Conference on Systems, Man, and Cybernetics, 2001Google Scholar
- 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
- S. Mellone, C. Tacconi, L. Chiari, Validity of a smartphone-based instrumented timed up and go. Gait Posture 36, 163–165 (2012)CrossRefGoogle Scholar
- 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
- B. Nham, K. Siangliulue, S. Yeung, Predicting mode of transport from iphone accelerometer data, Technical report (Stanford University, 2008)Google Scholar
- 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
- R. Poppe, Vision-based human motion analysis: an overview. Comput. Vis. Image Underst. 108, 4–18 (2007)CrossRefGoogle Scholar
- R. Poppe, A survey on vision-based human action recognition. Image Vis. Comput. 28, 976–990 (2010)CrossRefGoogle Scholar
- N. Ravi, N. Dandekar, P. Mysore, M.L. Littman, in Activity recognition from accelerometer data, Innovative Applications of Artificial Intelligence, 2005Google Scholar
- D. Riboni, C. Bettini, Cosar: hybrid reasoning for context-aware activity recognition. Pers. Ubiquit. Comput. 15, 271–289 (2011)CrossRefGoogle Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- M. Weiser, Some computer science issues in ubiquitous computing. Commun. ACM 36, 75–84 (1993)CrossRefGoogle Scholar
- D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)CrossRefGoogle Scholar
- D. Wyatt, M. Philipose, T. Choudhury, in Unsupervised activity recognition using automatically mined common sense, National Conference on Artificial Intelligence, 2005Google Scholar
- G.-Z. Yang, M. Yacoub, Body Sens. Netw. (Springer, New York, 2006)Google Scholar
- Z. Zhao, Y. Chen, J. Liu, M. Liu, in Cross-people motion activity recognition, International Joint Conference on Artificial Intelligence, 2010Google Scholar
- V.W. Zheng, D.H. Hu, Q. Yang, in Cross-domain activity recognition, International Conference on Ubiquitous Computing, 2009Google Scholar
- 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