Recognizing human daily activities from accelerometer signal

Procedia Engineering

Volumn 15, Issue , 2011, Pages 1780-1786

  Wang, Jin ^a   Chen, Ronghua ^a   Sun, Xiangping ^a   She, Mary F H ^a   Wu, Yuchuan ^b  

^a DEAKIN UNIVERSITY   (Australia)

^b WUHAN TEXTILE UNIVERSITY   (China)

Author keywords

Acceleration signal; Accelerometer; Activity recognition; Hidden Markov Model (HMM); Wearable system

Indexed keywords

ACCELERATION SIGNALS; ACCELEROMETER SIGNALS; ACTIVITY RECOGNITION; AGED CARE; AUTOMATED RECOGNITION; CONTINUOUS OBSERVATION; DAILY ACTIVITY; DATA SETS; GAUSSIAN MIXTURE MODEL; GENERALIZATION CAPABILITY; HIDDEN MARKOV MODEL (HMM); HIDDEN STATE; HIGH ACTIVITY; IN-SITU ASSESSMENT; RECOGNITION METHODS; RECOGNITION SYSTEMS; SENSOR SIGNALS; TEST SIGNAL; TRAINING SIGNAL; TRIAXIAL ACCELEROMETER; WEARABLE SENSORS; WEARABLE SYSTEMS;

ACCELEROMETERS; EXPERIMENTS; FACE RECOGNITION; GERIATRICS; HEALTH CARE; HIDDEN MARKOV MODELS; INFORMATION SCIENCE; SENSORS; WEARABLE COMPUTERS;

COMPUTATIONAL COMPLEXITY;

EID: 84055189239     PISSN: 18777058     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.proeng.2011.08.331     Document Type: Conference Paper

References (14)

1. J. K. Aggarwal and Q. Cai, "Human Motion Analysis: A Review," Computer Vision and Image Understanding, vol. 73, pp. 428-440, 1999.
2. L. Wang, W. Hu, and T. Tan, "Recent developments in human motion analysis," Pattern Recognition, vol. 36, pp. 585-601, 2003.
3. R. Poppe, "A survey on vision-based human action recognition," Image and Vision Computing, vol. 28, pp. 976-990, 2010.
4. M. Moreau, S. Siebert, A. Buerkert, and E. Schlecht, "Use of a tri-axial accelerometer for automated recording and classification of goats' grazing behaviour," Applied Animal Behaviour Science, vol. 119, pp. 158-170, Jul 2009.
5. O. Thomas, P. Sunehag, G. Dror, S. Yun, S. Kim, M. Robards, A. Smola, D. Green, and P. Saunders, "Wearable sensor activity analysis using semi-Markov models with a grammar," Pervasive and Mobile Computing, vol. 6, pp. 342-350, 2010.
6. P. Picerno, V. Camomilla, and L. Capranica, "Countermovement jump performance assessment using a wearable 3D inertial measurement unit," Journal of Sports Sciences, vol. 29, pp. 139-146, 2011.
7. M. Jiang, H. Shang, Z. Wang, H. Li, and Y. Wang, "A method to deal with installation errors of wearable accelerometers for human activity recognition," Physiological Measurement, vol. 32, pp. 347-358, 2011.
8. N. Bidargaddi, A. Sarela, L. Klingbeil, and M. Karunanithi, "Detecting walking activity in cardiac rehabilitation by using accelerometer," 2007, pp. 555-560.
9. M. Kangas, A. Konttila, P. Lindgren, I. Winblad, and T. Jämsä, "Comparison of low-complexity fall detection algorithms for body attached accelerometers," Gait and Posture, vol. 28, pp. 285-291, 2008.
10. Y. Wu, R. Chen, and M. F. H. She, "Automated Classification of Human Daily Activities in Ambulatory Environment," presented at the 12th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2011), University of Technology, Sydney, Australia, 2011.
11. L. R. Rabiner, "a Tutorial on hidden Markov models and selected applications in speech recognition," Proceedings of the IEEE, vol. 77, pp. 257-286, 1989.
12. L. Hyeon-Kyu and J. H. Kim, "An HMM-based threshold model approach for gesture recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, pp. 961-973, 1999.
13. N. V. Boulgouris, D. Hatzinakos, and K. N. Plataniotis, "Gait recognition: a challenging signal processing technology for biometric identification," Signal Processing Magazine, IEEE, vol. 22, pp. 78-90, 2005.
14. E. K. Antonsson and R. W. Mann, "The frequency content of gait," Journal of Biomechanics, vol. 18, pp. 39-47, 1985.