Sensor data acquisition and processing parameters for human activity classification

Sensors (Switzerland)

Volumn 14, Issue 3, 2014, Pages 4239-4270

  Bersch, Sebastian D ^a   Azzi, Djamel ^a   Khusainov, Rinat ^a   Achumba, Ifeyinwa E ^a   Ries, Jana ^b  

^a UNIVERSITY OF PORTSMOUTH   (United Kingdom)

^b UNIVERSITY OF PORTSMOUTH   (United Kingdom)

Author keywords

Ambient Assisted Living (AAL); Data acquisition; Data sampling; Event classification; Optimization

Indexed keywords

DATA ACQUISITION; OPTIMIZATION;

ACTIVITY OF DAILY LIVINGS; AMBIENT ASSISTED LIVING (AAL); CLASSIFICATION ALGORITHM; DATA SAMPLING; EMPIRICAL INVESTIGATION; EVENT CLASSIFICATION; SEGMENTATION ALGORITHMS; SEGMENTATION TECHNIQUES;

CLASSIFICATION (OF INFORMATION);

EID: 84896476565     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s140304239     Document Type: Article

References (42)

1. Amft, O.; Tröster, G. Automatic dietary monitoring: On-body sensing domains. In Automatic Dietary Monitoring Using On-Body Sensors; Ph. D. Thesis, ETH Zürich, Zürich, Switzerland, 2008.
2. Sixsmith, A. J. An evaluation of an intelligent home monitoring system. J. Telemed. Telecare 2000, 6, 63-72.
3. Pietka, E. Expert systems in parameter extraction of the ECG signal. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, New Orleans, LA, USA, 4-7 November 1988; pp. 165-166.
4. Orfanidis, S. J. Introduction to Signal Processing; Prentice-Hall: Upper Saddle River, NJ, USA, 1995.
5. Huynh, T.; Schiele, B. Analyzing Features for Activity Recognition. In Proceedings of the 2005 Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-Aware Services: Usages and Technologies, Grenoble, France, 12-14 October 2005; pp. 159-163.
6. Sekine, M.; Tamura, T.; Fujimoto, T.; Fukui, Y. Classification of Walking Pattern Using Acceleration Waveform in Elderly People. In Proceedings of the 22nd Annual IEEE International Conference of the Engineering in Medicine and Biology Society, Chicago, MI, USA, 23-28 July 2000; Volume 2, pp. 1356-1359.
7. Bao, L.; Intille, S. Activity recognition from user-annotated acceleration data. In Pervasive Computing; Springer: Berlin, Germany, 2004; pp. 1-17.
8. Preece, S. J.; Goulermas, J. Y.; Kenney, L. P.; Howard, D. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans. Biomed. Eng. 2009, 56, 871-879.
9. Wang, N.; Ambikairajah, E.; Lovell, N. H.; Celler, B. G. Accelerometry Based Classification of Walking Patterns Using Time-Frequency Analysis. In Proceedings of 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 22-26 August 2007; pp. 4899-4902.
10. Casale, P.; Pujol, O.; Radeva, P. Human activity recognition from accelerometer data using a wearable device. Pattern Recognit. Image Anal. 2011, 6669, 289-296.
11. Ravi, N.; Dandekar, N.; Mysore, P.; Littman, M. Activity Recognition from Accelerometer Data. In Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence, Menlo Park, CA, USA, 9-13 July 2005; pp. 1541-1546.
12. Parkka, J.; Cluitmans, L.; Ermes, M. Personalization algorithm for real-time activity recognition using PDA, wireless motion bands, and binary decision tree. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 1211-1215.
13. Maurer, U.; Smailagic, A.; Siewiorek, D. P.; Deisher, M. Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions. In Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks 2006, Boston, MA, USA, 3-5 April 2006; pp. 113-116.
14. Antonsson, E. K.; Mann, R. W. The frequency content of gait. J. Biomech. 1985, 18, 39-47.
15. Bouten, C. V.; Koekkoek, K. T.; Verduin, M.; Kodde, R.; Janssen, J. D. A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Trans. Biomed. Eng. 1997, 44, 136-147.
16. Gjoreski, H.; Gams, M.; Chorbev, I. 3-axial accelerometers activity recognition. ICT Innov. (2010) 2010, 51-58.
17. Keogh, E.; Chu, S.; Hart, D.; Pazzani, M. An Online Algorithm for Segmenting Time Series. In Proceedings of the International Conference on Data Mining, San Jose, CA, USA, 29 November-2 December 2001; pp. 289-296.
18. Chu, C.-S. J. Time series segmentation: A sliding window approach. Inf. Sci. 1995, 85, 147-173.
19. Kozina, S.; Lustrek, M.; Gams, M. Dynamic signal segmentation for activity recognition. In Proceedings of International Joint Conference on Artificial Intelligence, Barcelona, Spain, 16-22 July 2011; pp. 15-22.
20. Ortiz, J.; Olaya, A. G.; Borrajo, D. A Dynamic Sliding Window Approach for Activity Recognition. In Proceedings of the International Conference on User Modeling, Adaptation and Personalization, Girona, Spain, 11-15 July 2011; pp. 219-230.
21. Nyan, M. N.; Tay, F.; Seah, K.; Sitoh, Y. Y. Classification of gait patterns in the time-frequency domain. J. Biomech. 2006, 39, 2647-2656.
22. Van Kasteren, T. L. M.; Noulas, A.; Englebienne, G.; Kröse, B. J. Accurate Activity Recognition in a Home Setting. In Proceedings of the Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), Seoul, Korea, 21-24 September 2007; pp. 1-9.
23. Patterson, D. J.; Fox, D.; Kautz, H.; Philipose, M. Fine-Grained Activity Recognition by Aggregating Abstract Object Usage. In Proceedings of the International Semantic Web Conference (ISWC), Galway, Ireland, 18-21 October 2005; pp. 44-51.
24. Roggen, D.; Calatroni, A.; Rossi, M.; Holleczek, T.; Forster, K.; Tröster, G.; Lukowicz, P.; Bannach, D.; Pirkl, G.; Ferscha, A.; et al. Collecting complex activity datasets in highly rich networked sensor environments. In Proceedings of 2010 7th International Conference on Networked Sensing Systems (INSS), Kassel, Germany, 15-18 June 2010; pp. 233-240.
25. MATLAB-The Language of Technical Computing. Available online: http://www. mathworks. cn/products/matlab/(accessed on 3 March 2014).
26. Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I. H. The WEKA data mining software: An update. SIGKDD Explor. Newsl. 2009, 11, 10-18.
27. Achumba, I. E.; Bersch, S.; Khusainov, R.; Azzi, D.; Kamalu, U. On time series sensor data segmentation for fall and activity classification. In Proceedings of 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom), Beijing, China, 10-13 October 2012; pp. 427-430.
28. Farringdon, J.; Moore, A. J.; Tilbury, N.; Church, J.; Biemond, P. D. Wearable Sensor Badge and Sensor Jacket for Context Awareness. In Proceedings of the IEEE International Symposium on Wearable Computers (ISWC'99), San Francisco, CA, USA, 18-19 October 1999; pp. 107-114.
29. Kawahara, H. S. Y.; Hisashi Kurasawa, H. M.; Aoyama, T. A Context-Aware Collaborative Filtering Algorithm for Real World Oriented Content Delivery Service. In Proceedings of 7th International Conference Ubiquitous Computing, Tokyo, Japan, 11-14 September 2005.
30. Veltink, P.; Bussmann, H.; de Vries, W.; Martens, W.; van Lummel, R. Detection of static and dynamic activities using uniaxial accelerometers. IEEE Trans. Rehabilit. Eng. 1996, 4, 375-385.
31. Jin, G. H.; Lee, S. B.; Lee, T. S. Context awareness of human motion states using accelerometer. J. Med. Syst. 2007, 32, 93-100.
32. Kern, N.; Schiele, B.; Schmidt, A. Recognizing context for annotating a live life recording. Pers. Ubiquitous Comput. 2006, 11, 251-263.
33. Karantonis, D. M.; Narayanan, M. R.; Mathie, M.; Lovell, N. H.; Celler, B. G. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed. 2006, 10, 156-167.
34. Bianchi, F.; Redmond, S.; Narayanan, M.; Cerutti, S.; Celler, B.; Lovell, N. Falls event detection using triaxial accelerometry and barometric pressure measurement. In Proceedings of 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA, 3-6 September 2009; pp. 6111-6114.
35. Nham, B.; Siangliulue, K.; Yeung, S. Predicting Mode of Transport from iPhone Accelerometer Data; CS 229; Machine Learning Final Projects, Stanford University: Stanford, CA, USA, 2008.
36. Entropy (Wavelet Packet). Available online: http://www. mathworks. cn/cn/help/wavelet/ref/wentropy. html (accessed on 3 March 2014).
37. Fast Fourier Transform. Available online: http://www. mathworks. cn/cn/help/matlab/ref/fft. html (accessed on 3 March 2014).
38. Chernbumroong, S.; Atkins, A. S.; Yu, H. Activity Classification Using a Single Wrist-Worn Accelerometer. In Proceedings of 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA), Benevento, Italy, 8-11 September 2011; pp. 1-6.
39. SPSS Software-Predictive Analytics Software and Solutions. Available online: http://www-01. ibm. com/software/uk/analytics/spss/(accessed on 3 March 2014).
40. Chawla, N. V. Data Mining for Imbalanced Datasets: An Overview. In Data Mining and Knowledge Discovery Handbook; Springer US: Boston, MA, USA, 2010; pp. 875-886.
41. Ding, D.; Cooper, R. A.; Pasquina, P. F.; Fici-Pasquina, L. Sensor technology for smart homes. Maturitas 2011, 69, 131-136.
42. Elbert, D.; Storf, H.; Eisenbarth, M.; Ünalan, Ö.; Schmitt, M. An approach for detecting deviations in daily routine for long-term behavior analysis. In Proceedings of 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Dublin, Ireland, 23-26 May 2011; pp. 426-433.