An assistive environment for improving human safety utilizing advanced sound and motion data classification

Universal Access in the Information Society

Volumn 10, Issue 2, 2011, Pages 217-228

  Doukas, Charalampos ^a   Maglogiannis, Ilias ^b  

^a UNIVERSITY OF THE AEGEAN   (Greece)

^b UNIVERSITY OF CENTRAL GREECE   (Greece)

Author keywords

Emergency event detection; Movement and sound analysis; Patient status awareness; SVM classification

Indexed keywords

ASSISTIVE; AWARENESS SYSTEMS; CLASSIFICATION TECHNIQUE; EVALUATION RESULTS; EVENT DETECTION; HUMAN ACTIVITIES; HUMAN BODIES; HUMAN SAFETY; MOTION DATA; MOVEMENT AND SOUND ANALYSIS; PATIENT STATUS AWARENESS; PERFORMANCE COMPARISON; PHYSIOLOGICAL DATA; SHORT TIME FOURIER TRANSFORMS; SOUND DATA; SPECTROGRAMS; SVM CLASSIFICATION; SYSTEM ARCHITECTURES;

ACCELEROMETERS; FOURIER TRANSFORMS; OCEAN HABITATS; SPEECH RECOGNITION;

CLASSIFICATION (OF INFORMATION);

EID: 79957855393     PISSN: 16155289     EISSN: 16155297     Source Type: Journal    
DOI: 10.1007/s10209-010-0196-6     Document Type: Article

References (54)

1. Lines, L., Hone, K. S.: Eliciting user requirements with older adults: lessons from the design of an interactive domestic alarm system. Univ. Access Inform. Soc. 3(2), 141-148 (2004).
2. Orpwood, R., Gibbs, C., Adlam, T., Faulkner, R., Meegahawatte, D.: The design of smart homes for people with dementia-user-interface aspects. Univ. Access Inform. Soc. 4(2), 156-164 (2005).
3. Neerincx, M. A., Cremers, A. H. M., Kessens, J. M., van Leeuwen, D. A., Truong, K. P.: Attuning speech-enabled interfaces to user and context for inclusive design: technology, methodology and practice. Univ. Access Inform. Soc. (2008). doi: 10. 1007/s10209-008-0136-x.
4. Holzinger, A., Errath, M.: Mobile computer Web-application design in medicine: some research based guidelines. Univ. Access Inform. Soc. 6(1), 31-41 (2006).
5. Noury, N., Herve, T., Rialle, V., Virone, G., Mercier, E., Morey, G., Moro, A., Porcheron, T.: Monitoring behavior in home using a smart fall sensor and position sensors. In: Proceedings of 1st Annual International Conference on Microtechnologies in Medicine and Biology, pp. 607-610 (2000).
6. Noury, N.: A smart sensor for the remote follow up of activity and fall detection of the elderly. In: Proceedings of 2nd Annual International Conference on Microtechnologies in Medicine and Biology, pp. 314-317 (2002).
7. Prado, M., Reina-Tosina, J., Roa, L.: Distributed intelligent architecture for falling detection and physical activity analysis in the elderly. In: Proceedings of the 24th Annual IEEE EMBS Conference, pp. 1910-1911 (2002).
8. Fukaya, K.: Fall detection sensor for fall protection airbag. In: Proceedings of the 41st SICE Annual Conference, pp. 419-420 (2002).
9. Sixsmith, A., Johnson, N.: A smart sensor to detect the falls of the elderly. IEEE Pervas. Comput. 3(2), 42-47 (2004).
10. Nait-Charif, H., McKenna, S. J.: Activity summarisation and fall detection in a supportive home environment, In: Proceedings of the 17th International Conference on Pattern Recognition ICPR 2004, pp. 323-236 (2004).
11. Hwang, J. Y., Kang, J. M., Jang, Y. W., Kim, H. C.: Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly. In: Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2204-2207 (2004).
12. Luo, S., Hu, Q.: A dynamic motion pattern analysis approach to fall detection, In: Proceedings of the 2004 IEEE International Workshop on Biomedical Circuits and Systems, pp. 1-8a (2004).
13. Wang, S., Yang, J., Chen, N., Chen, X., Zhang, Q.: Human activity recognition with user-free accelerometers in the sensor networks, In: Proceedings of International Conference on Neural Networks and Brain, pp. 1212-1217 (2005).
14. Miaou, S.-G., Sung, P.-H., Huang, C.-Y.: A customized human fall detection system using omni-camera images and personal information. In: Proceedings of 1st Transdisciplinary conference on Distributed Diagnosis and Home Healthcare, pp. 39-42 (2006).
15. Alwan, M., Rajendran, P. J., Kell, S., Mack, D., Dalal, S., Wolfe, M., Felder, R.: A smart and passive floor-vibration based fall detector for elderly. In: Proceedings of 2nd Information and Communication Technologies Conference, ICTTA 06, pp. 1003-1007 (2006).
16. Allen, F. R., Ambikairajah, E., Lovell, N. H., Celler, B. G.: An adapted gaussian mixture model approach to accelerometry-based movement classification using time-domain features. In: Proceedings of 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3600-3603 (2006).
17. Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Monocular 3D head tracking to detect falls of elderly people. In: Proceedings of 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6384-6387 (2006).
18. Cao, X. B., Chen, D., Qiao, H., Xu, Y. W.: An evolutionary support vector machines classifier for pedestrian detection. In: Proceedings of 2006 IEEE International Conference on Intelligent Robots and Systems, pp. 4223-4227 (2006).
19. Bart, J., Rudi, D.: Context aware inactivity recognition for visual fall detection. In: Proceedings of 2006 Pervasive Health Conference and Workshops, pp. 1-4 (2006).
20. Gaura, E. I., Rider, R. J., Steele, N., Naguib, R. N. G.: Neural-network compensation methods for capacitive micromachined accelerometers for use in telecare medicine. IEEE Trans. Inform. Technol. Biomed. 5(3), 248-252 (2001).
21. Mukasa, K. S., Holzinger, A., Karshmer, A. (eds.): Proceedings International Workshop on Intelligent User Interfaces for Ambient Assisted Living (IUI4AAL 2008), Stuttgart: Fraunhofer IRB Verlag (ISBN 978-3-8167-7521-8) (2008).
22. th Annual International Conference of the IEEE in Engineering in Medicine and Biology Society, vol 2, pp. 1364-1367 (2003).
23. Istrate, D., Castelli, E., Vacher, M., Besacier, L., Serignat, J.-F.: Information extraction from sound for medical telemonitoring. IEEE Trans. Inform. Theory Biomed. 10(2), 264-274 (2006).
24. Vacher, M., Istrate, D., Serignat, J. F.: Sound detection and classification through transient models using wavelet coefficient trees, presented at 12th Eurasip European Signal Processing Conference (2004).
25. Istrate, D., Vacher, M., Serignat, J. F.: Generic implementation of a distress sound extraction system for elder care. In: Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA (Aug 30-Sept 3), pp. 3309-3312 (2006).
26. Laydrus, N. C., Ambikairajah, E., Celler, B.: Automated sound analysis system for home telemonitoring using shifted delta cepstral features. In: 15th International Conference on Digital Signal Processing, July 2007, pp. 135-138 (2007).
27. Nguyen, C. P., Pham, T. N. Y., Eric, C.: Toward a sound analysis system for telemedicine, fuzzy systems and knowledge discovery. Springer 3614(2005), 352-361 (2005).
28. Alwan, M., Dalal, S., Mack, D., Kell, S. W., Turner, B., Leachtenauer, J., Felder, R.: Impact of monitoring technology in assisted living: outcome pilot. IEEE Trans. Inform. Technol. Biomed. 10(1), 192-198 (2000).
29. Sonogram Speech and Sound processing tool, http://www. christoph-lauer. de/.
30. Doukas, C., Maglogiannis, I.: Advanced Patient or Elder Fall Detection based on Movement and Sound Data. Presented at 2nd International Conference on Pervasive Computing Technologies for Healthcare (2008).
31. Doukas, C., Maglogiannis I.: Enabling human status awareness in assistive environments based on advanced sound and motion data classification. Presented at The 1st ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRAE), July 16-19 2008, Athens, Greece (2008).
32. Doukas, C., Maglogiannis, I., Tragkas, P., Liapis, D., Yovanof, G.: Patient fall detection using support vector machines. In: Proceedings of the 4th IFIP Conference on Artificial Intelligence Applications & Innovations (AIAI), Sept. 19-21, Athens, Greece (2007).
33. www. freescale. com.
34. Chang, C.-C., Lin, C.-J., LIBSVM: A library for support vector machines (2001). Software available at http://www. csie. ntu. edu. tw/~cjlin/libsvm.
35. Freund, Y., Schapire, R. E.: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, pp. 148-156 (1996).
36. Frank, E., Wang, Y., Inglis, S., Holmes, G., Witten, I. H.: Using model trees for classification. Mach. Learn. 32(1), 63-76 (1998).
37. Kohavi, R.: Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University (1995).
38. Ho, T. K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832-844 (1998).
39. Dong, L., Frank, E., Kramer, S.: Ensembles of balanced nested dichotomies for multi-class problems. In: PKDD, pp. 84-95 (2005).
40. Ting, K. M., Witten, I. H.: Stacking bagged and dagged models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, pp. 367-375 (1997).
41. Carletta, J.: Assessing agreement on classification tasks: the kappa statistic. Comput. Linguistics 22(2), 249-254 (1996).
42. Megalooikonomou, V., Kontos, D.: Medical data fusion for telemedicine. IEEE Eng. Med. Biol. Magazine 26(5), 36-42 (2007).
43. Grewal, M. S., Andrews, A. P., Filterinf, K.: Second Edition. Wiley (2001).
44. Holzinger, A.: Usability engineering for software developers. Commun. ACM 48(1), 71-74 (2005).
45. Holzinger, A., Searle, G., Kleinberger, T., Seffah, A., Javahery, H.: Investigating usability metrics for the design and development of applications for the elderly In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A. (eds.) 11th International Conference on Computers Helping People with Special Needs (ICCHP 2008), Lecture Notes in Computer Science (LNCS 5105), Berlin, Heidelberg, New York: Springer, 98-105 (ISBN: 978-3-540-70539-0) (2008).
46. Holzinger, A., Schaupp, K., Eder-Halbedl, W.: An investigation on acceptance of ubiquitous devices for the elderly in an geriatric hospital environment: using the example of person tracking. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A. (eds.) 11th International Conference on Computers Helping People with Special Needs, Lecture Notes in Computer Science (LNCS 5105), 22-29 (ISBN: 978-3-540-70539-0) (2008).
47. Birkmire-Peters, D. P., Peters, L. J., Whitaker, L. A.: A usability evaluation for telemedicine medical equipment. Telemed. J. 5(2), 209-212 (1999). doi: 10. 1089/107830299312177.
48. Soppera, A., Burbridge, T.: Maintaining privacy in pervasive computing-enabling acceptance of sensor-based services. BT Technol. J. 22(3), 106-118 (2010).
49. Blazic, A. J., Dolinar, K., Porekar, J.: Enabling privacy in pervasive computing using fusion of privacy negotiation, identity management and trust management techniques. In: Proceedings of 1st International Conference on Digital Society, 2007. ICDS '07, p. 30 (2007).
50. Centers for Medicare & Medicaid Services. http://cms. hhs. gov/.
51. www. zigbee. org.
52. Sasiadek, J. Z., Khe, J.: Sensor fusion based on fuzzy Kalman filter, In: Proceedings of 2nd International IEEE Workshop on Robot Motion and Control, pp. 275-283 (2001).
53. SensiNode. http://www. sensinode. com.
54. Witten, I. H., Frank, E.: Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005).