Wearable sensor activity analysis using semi-Markov models with a grammar

Pervasive and Mobile Computing

Volumn 6, Issue 3, 2010, Pages 342-350

  Thomas, O ^a   Sunehag, P ^a   Dror, G ^b   Yun, S ^c   Kim, S ^c   Robards, M ^a   Smola, A ^d   Green, D ^e   Saunders, P ^e  

^a CSIRO   (Australia)

^b ACADEMIC COLLEGE OF TEL AVIV YAFFO   (Israel)

^c Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^d YAHOO RESEARCH   (United States)

^e AUSTRALIAN INSTITUTE OF SPORT   (Australia)

Author keywords

Accelerometer; Activity recognition; Human performance; Machine learning; Semi Markov models

Indexed keywords

ACCELEROMETERS; LEARNING SYSTEMS; WEARABLE SENSORS;

ACTIVITY ANALYSIS; ACTIVITY RECOGNITION; DISCRIMINATIVE HIDDEN MARKOV MODELS; HIGHLY ACCURATE; HUMAN PERFORMANCE; SEMI MARKOV MODEL; SEQUENTIAL DATA; TRAINING SESSIONS;

HIDDEN MARKOV MODELS;

EID: 77955714938     PISSN: 15741192     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pmcj.2010.01.002     Document Type: Article

References (25)

1. B.N. Schilit, N. Adams, R. Want, Context-aware computing applications, in: IEEE Workshop on Mobile Computing Systems and Applications, 1994
2. O. Masoud, N. Papanikolopoulos, Recognizing human activities, in: AVSS '03: Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003, p. 157
3. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri Actions as space-time shapes Transactions on Pattern Analysis and Machine Intelligence 29 12 2007 2247 2253
4. M. Ermes, J. Parkka, J. Mantyjarvi, and I. Korhonen Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions IEEE Transactions on Information Technology in Biomedicine 12 1 2008 20 26
5. O. Aziz, B.P.L. Lo, G.Z. Yang, R. King, A. Darzi, Pervasive body sensor network: An approach to monitoring the post-operative surgical patient, in: 2006 International Workshop on Wearable and Implantable Body Sensor Networks, 2006, pp. 1318
6. S. Patel, D.M. Sherrill, R. Hughes, T. Hester, T. Lie-Nemeth, P. Bonato, D. Standaert, N. Huggins, Analysis of the severity of dyskinesia in patients with parkinson's disease via wearable sensors, in: 2006 International Workshop on Wearable and Implantable Body Sensor Networks, 2006, pp. 123126
7. R. Williamson, and B.J. Andrews Gait event detection for FES using accelerometers and supervised machine learning IEEE Transactions on Rehabilitation Engineering 8 3 2000 312 319
8. M.M. Skelly, and H.H. Chizeck Real-time gait event detection for paraplegic FES walking IEEE Transactions on Neural Systems and Rehabilitation Engineering 9 1 2001 59 68
9. B.T. Smith, D.J. Coiro, R. Finson, R.R. Betz, and J. McCarthy Evaluation of force-sensing resistors for gait event detection Ro trigger electrical stimulation to improve walking in the child with cerebral Palsy IEEE Transactions on Neural Systems and Rehabilitation Engineering 10 1 2002 22 29
10. M. Hansen, M.K. Haugland, and T. Sinkjaer Evaluating robustness of gait event detection based on machine learning and natural sensors IEEE Transactions on Neural Systems and Rehabilitation Engineering 1 12 2004 81 88
11. R.T. Lauer, B.T. Smith, and R.R. Betz Application of a neuro-fuzzy network for gait event detection using electromyography in the child with cerebral palsy IEEE Transactions on Biomedical Engineering 52 9 2005 1532 1540
12. B. Huang, M. Chen, X. Shi, Y. Xu Gait, Event detection with intelligent shoes, in: Proceedings of the 2007 International Conference on Information Acquisition, 2007
13. D. James, N. Davey, and T. Rice An accelerometer based sensor platform for insitu elite athlete performance analysis Sensors, Proc. of IEEE vol. 3 2005 1373 1376
14. S. Sarawagi, and W. Cohen Semi-markov conditional random fields for information extraction S. Thrun, L. Saul, B. Schlkopf, NIPS 16 2004
15. L.R. Rabiner A tutorial on hidden Markov models and selected applications in speech recognition Proceedings of IEEE 77 2 1989 257 286
16. S. Furui Speaker-independent isolated word recognition using dynamic features of speech spectrum IEEE Transactions on Acoustics, Speech and Signal Processing 34 1 1986 52 59
17. F. Lefevre, C. Montacie, M.-J. Caraty, On the influence of the delta coefficients in a HMM-based speech recognition system, in: Proc. ICSLP98, 1998
18. A.P. Dempster, N.M. Laird, and D.B. Rubin Maximum likelihood from incomplete data via the EM algorithm Journal of Royal Statistical Society Series B 39 1977 1 38
19. L.E. Baum, T. Peterie, G. Souled, and N. Weiss A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains The Annals of Statistics 41 1 1970 164 171
20. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun Large margin methods for structured and interdependent output variables Journal of Machine Learning Research 6 2005 1453 1484
21. B. Taskar, C. Guestrin, and D. Koller Max-margin Markov networks S. Thrun, L. Saul, B. Schlkopf, NIPS 16 2004 MIT Press Cambridge, MA 25 32
22. A. Nedich, and D.P. Bertsekas Convergence rate of incremental subgradient algorithms S. Uryasev, P.M. Pardalos, Stochastic Optimization: Algorithms and Applications 2000 Kluwer 263 304
23. H.E. Robbins, and S. Monro A stochastic approximation method Annals of Mathematical Statistics 22 1951 400 407
24. S. Young, G. Evermann, D. Kershaw, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev, and P. Woodland The HTK Boook (for HTK version 3.2) 2002 Univ. Cambridge Cambridge, U.K
25. P. Liang, M. Jordan, An Asymptotic Analyssi of Generative, Discriminative and Psuedolikelihood Estimators, in: ICML '08: The 25th International Conference on Machine Learning, Helsinki, Finland, 2008