COM: A method for mining and monitoring human activity patterns in home-based health monitoring systems

ACM Transactions on Intelligent Systems and Technology

Volumn 4, Issue 4, 2013, Pages

  Rashidi, Parisa ^a   Cook, Diane J ^b  

^a NORTHWESTERN UNIVERSITY   (United States)

^b Washington State University   (United States)

Author keywords

Assisted living technology; Health monitoring; Sequence mining; Smart environments

Indexed keywords

ASSISTED LIVING TECHNOLOGIES; DAILY ACTIVITY PATTERNS; HEALTH CARE PROFESSIONALS; HEALTH MONITORING; HEALTH MONITORING SYSTEM; REMOTE MONITORING SYSTEM; SEQUENCE MINING; SMART ENVIRONMENT;

HEALTH CARE;

MONITORING;

EID: 84885674772     PISSN: 21576904     EISSN: 21576912     Source Type: Journal    
DOI: 10.1145/2508037.2508045     Document Type: Article

References (60)

1. ABOWD, G. AND MYNATT, E. 2004. Smart Environments: Technology, Protocols, and Applications. Wiley, Hoboken, NJ, 153-174.
2. AGRAWAL, R. AND SRIKANT, R. 1995. Mining sequential patterns. In Proceedings of the International Conference on Data Engineering. 3-14.
3. AOA. 2011. Aging statistics. http://www.aoa.gov.
4. BARGER, T.,BROWN, D., AND ALWAN,M. 2005. Health-statusmonitoring through analysis of behavioral patterns. IEEE Trans. Syst. Man Cybernet. Part A 35, 1, 22-27.
5. BRDICZKA, O.,MAISONNASSE, J., AND REIGNIER, P. 2005. Automatic detection of interaction groups. In Proceedings of the International Conference on Multimodal Interfaces. 32-36.
6. CHAN, M., ESTEVE, D., ESCRIBA, C., AND CAMPO, E. 2008. A review of smart homes-present state and future challenges. Comput. Method. Program. Biomed. 91, 1, 55-81.
7. CHIU, D.-Y., WU, Y.-H., AND CHEN, A. L. P. 2004. An efficient algorithm for mining frequent sequences by a new strategy without support counting. In Proceedings of the 20th International Conference on Data Engineering (ICDE'04). 375-382.
8. COOK, D. AND DAS, S. 2004. Smart Environments: Technology, Protocols and Applications. Wiley-Interscience, New York, NY.
9. COOK, D. AND SCHMITTER-EDGECOMBE, M. 2009. Assessing the quality of activities in a smart environment. Method. Inf. Med. 48, 5, 480-485.
10. COOK, D., YOUNGBLOOD, M., HEIERMAN, E. O., GOPALRATNAM, K., RAO, S., LITVIN, A., AND KHAWAJA, F. 2003. MavHome: An agent-based smart home. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications. 521-524.
11. DIMITROV, T., PAULI, J., AND NAROSKA, E. 2010. Unsupervised recognition of adls. In Artificial Intelligence: Theories, Models and Applications, Lecture Notes in Computer Science, vol. 6040. Springer, Berlin Heidelberg, 71-80.
12. DOCTOR, F.,HAGRAS, H., AND CALLAGHAN, V. 2005. A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments. IEEE Trans. Syst.Man Cybernet. Part A 35, 1, 55-65.
13. GOPALRATNAM, K. AND COOK, D. J. 2007. Online sequential prediction via incremental parsing: The active lezi algorithm. IEEE Intell. Syst. 22, 1, 52-58.
14. GU, T.,WU, Z., TAO, X., PUNG, H., AND LU, J. 2009. epsicar: An emerging patterns based approach to sequential, interleaved and concurrent activity recognition. In Proceedings of the International Conference on Pervasive Computing and Communication.
15. GUYON, I. AND ELISSEEFF, A. 2003. An introduction to variable and feature selection. Mach. Learn. Res. 3, 1157-1182.
16. HAYES, T. L., PAVEL, M., LARIMER, N., TSAY, I. A., NUTT, J., AND ADAMI, A. G. 2007. Distributed healthcare: Simultaneous assessment of multiple individuals. IEEE Perv. Comput. 6, 1, 36-43.
17. HEBERT, L. E., BECKETT, L. A., SCHERR, P. A., AND EVANS, D. A. 2001. Annual incidence of alzheimer disease in the United States projected to the years 2000 through 2050. Alzheimer Dis. Assoc. Disorders 15, 4, 169-173.
18. HEIERMAN, III, E. O. AND COOK, D. J. 2003. Improving home automation by discovering regularly occurring device usage patterns. In Proceedings of the International Conference on Data Mining. 537.
19. HELAL, S.,MANN, W.,EL-ZABADANI, H.,KING, J.,KADDOURA, Y., AND JANSEN, E. 2005. The gator tech smart house: A programmable pervasive space. Computer 38, 50-60.
20. HENSEL, B. K., DEMIRIS, G., AND COURTNEY, K. 2006. Defining obtrusiveness in home telehealth technologies: A conceptual framework. J. Am. Med. Inf. Assoc. 13, 428-431.
21. HUYNH, T. AND SCHIELE, B. 2006. Unsupervised discovery of structure in activity data using multiple eigenspaces. Lecture Notes in Computer Science, vol. 3987. Springer, Berlin Heidelberg, 151-167.
22. INOMATA, T., NAYA, F., KUWAHARA, N., HATTORI, F., AND KOGURE, K. 2009. Activity recognition from interactions with objects using dynamic bayesian network. In Proceedings of the International Workshop on Context-Awareness for Self-Managing Systems. 39-42.
23. LIAO, L., FOX, D., AND KAUTZ, H. 2005. Location-based activity recognition using relational markov networks. In Proceedings of the International Joint Conference on Artificial Intelligence. 773-778.
24. LIU, B.,HSU, W., AND MA, Y. 1999. Mining association rules with multiple minimum supports. In Proceedings of the Conference on Knowledge Discovery and Data Mining. 337-341.
25. LOO, K. K., TONG, I., KAO, B., AND CHEUNG, D. 2005. Online algorithms for mining inter-stream associations from large sensor networks. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).
26. MASSEGLIA, F., CATHALA, F., AND PONCELET, P. 1998. The PSP approach for mining sequential patterns. In Proceedings of the European Symposium on Principles of Data Mining and Knowledge Discovery. 176-184.
27. MAURER, U., SMAILAGIC, A., SIEWIOREK, D. P., AND DEISHER, M. 2006. Activity recognition and monitoring using multiple sensors on different body positions. In Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks. 113-116.
28. MCDOWELL, I. AND NEWELL, C. 1996. Measuring Health: A Guide to Rating Scales and Questionnaires 2nd Ed. Oxford University Press, New York, NY.
29. MOZER, M. C.,DODIER, R. H.,ANDERSON, M.,VIDMAR, L.,CRUICKSHANK III, R. F., ANDMILLER, D. 1998. The neural network house: An overview. In Proceedings of the American Association for Artificial Intelligence Spring Symposium on Intelligent Environments. 110-114.
30. PALMES, P.,PUNG, H. K.,GU, T.,XUE, W., AND CHEN, S. 2010. Object relevance weight pattern mining for activity recognition and segmentation. Perv. Mob. Comput. 6, 43-57.
31. PAPADIMITRIOU, S., BROCKWELL, A., AND FALOUTSOS, C. 2003. Adaptive, hands-off stream mining. In Proceedings of the Conference on Very Large Data Bases. 560-571.
32. PEI, J., HAN, J., MORTAZAVI-ASL, B., PINTO, H., CHEN, Q., DAYAL, U., AND HSU, M. 2001. Prefixspan: Mining sequential patterns by prefix-projected growth. In Proceedings of the International Conference on Data Engineering. 215-224.
33. PEI, J., HAN, J., MORTAZAVI-ASL, B., WANG, J., PINTO, H., CHEN, Q., DAYAL, U., AND HSU, M.-C. 2004. Mining sequential patterns by pattern-growth: The prefixspan approach. IEEE Trans. Knowl. Data Eng. 16, 1424-1440.
34. PEI, J.,HAN, J., ANDWANG,W. 2007. Constraint-based sequential pattern mining: The pattern-growthmethods. J. Intell. Inf. Syst. 28, 2, 133-160.
35. PERKOWITZ, M., PHILIPOSE, M., FISHKIN, K., AND PATTERSON, D. J. 2004. Mining models of human activities from the Web. In Proceedings of the International Conference on World Wide Web. 573-582.
36. PHILIPOSE, M., FISHKIN, K., PERKOWITZ, M., PATTERSON, D., FOX, D., KAUTZ, H., AND HAHNEL, D. 2004. Inferring activities from interactions with objects. IEEE Perv. Comput. 3, 4, 50-57.
37. PYLE, D. 2005. Do Smart Adaptive Systems Exist? Springer, New York, NY. 27-53.
38. RABINER, L. R. 1990. Readings in Speech Recognition. Morgan Kaufmann Publishers Inc., San Francisco, CA, 267-296.
39. RASHIDI, P. AND COOK, D. J. 2008. Adapting to resident preferences in smart environments. In Proceedings of the AAAI Workshop on Preference Handling. 78-84.
40. RASHIDI, P. AND COOK, D. J. 2009. The resident in the loop: Adapting the smart home to the user. IEEE Trans. Syst. Man Cybernet. Part A 39, 5, 949-959.
41. RASHIDI, P., COOK, D. J.,HOLDER, L. B., AND SCHMITTER-EDGECOMBE, M. 2011. Discovering activities to recognize and track in a smart environment. IEEE Trans. Knowl. Data Eng. 23, 527-539.
42. REISBERG, B., FINKEL, S., OVERALL, J., SCHMIDT-GOLLAS, N., KANOWSKI, S., LEHFELD, H., HULLA, F., SCLAN, S. G., WILMS, H.-U., HEININGER, K., HINDMARCH, I., STEMMLER, M., POON, L., KLUGER, A., COOLER, C., BERGENER, M., HUGONOT-DIENER, L., ROBERT, P. H., AND ERZIGKEIT, H. 2001. The Alzheimer's disease activities of daily living international scale. Int. Psychogeriatrics 13, 2, 163-181.
43. RIALLE, V., OLLIVET, C., GUIGUI, C., AND HERV́e, C. 2008. What do family caregivers of alzheimer's disease patients desire in smart home technologies? Contrasted results of a wide survey.Method. Inf. Med. 47, 63-9.
44. RISSANEN, J. 1978. Modeling by shortest data description. Automatica 14, 465-471.
45. SCHIELE, B. 2006. Unsupervised discovery of structure in activity data using multiple eigenspaces. In Proceeding of the 2nd International Workshop on Location and Context Awareness. Lecture Notes in Computer Science, vol. 3987, Springer, Berlin.
46. STAUFFER, C. AND GRIMSON,W. 2000. Learning patterns of activity using real-Time tracking. IEEE Tran. Pattern Anal. Mach. Intell. 22, 8, 747-757.
47. TAN, P.-N., STEINBACH, M., AND KUMAR, V. 2005. Introduction to Data Mining. Addison Wesley, Reading, MA.
48. TAPIA, E. M., INTILLE, S. S., AND LARSON, K. 2004. Activity recognition in the home using simple and ubiquitous sensors. In Proceedings of the 2nd Conference on Pervasive Computing. Lecture Notes in Computer Science, vol. 3001, Springer, Berlin. 158-175.
49. VAHDATPOUR, A., AMINI, N., AND SARRAFZADEH, M. 2009. Toward unsupervised activity discovery using multidimensional motif detection in time series. In Proceedings of the International Jont Conference on Artifical Intelligence. 1261-1266.
50. VAIL, D. L., VELOSO, M. M., AND LAFFERTY, J. D. 2007. Conditional random fields for activity recognition. In Proceedings of the Conference on Autonomous Agents andMultiagent Systems. ACM, New York, NY, 1-8.
51. VAN KASTEREN, T., ENGLEBIENNE, G., AND KROSE, B. 2008. Recognizing activities in multiple contexts using transfer learning. In Proceedings of the AAAI AI in Eldercare Symposium.
52. VITERBI, A. 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Tran. Inf. Theor. 13, 2, 260-269.
53. WADLEY, V. G., OKONKWO, O., CROWE, M., VANCE, D. E., ELGIA, J. M., BALL, K. K., AND OWSLEY, C. 2008. Mild cognitive impairment and everyday function: Evidence of reduced speed in performing instrumental activities of daily living. Amer. J. Geriatric Psych. 16, 5, 416-424.
54. WANG, J. AND HAN, J. 2004. Bide: Efficient mining of frequent closed sequences. In Proceedings of the International Conference on Data Engineering. 79.
55. WRAY, R. AND LAIRD, J. 2003. Variability in human modeling of military simulations. In Proceedings of the Conference of Behaviour Representation in Modeling and Simulation. 160-169.
56. WREN, C. ANDMUNGUIA-TAPIA,E. 2006. Toward scalable activity recognition for sensor networks. In Proceedings of the Workshop on Location and Context-Awareness. 218-235.
57. YANG, Z., KITSUREGAWA, M., AND WANG, Y. 2006. Paid: Mining sequential patterns by passed item deduction in large databases. In Proceedings of the International Database Engineering and Applications Symposium. 113-120.
58. YIPING, T., SHUNJING, J., ZHONGYUAN, Y., AND SISI, Y. 2006. Detection elder abnormal activities by using omnidirectional vision sensor: Activity data collection and modeling. In Proceedings of the International Joint Conference SICE-ICASE. 3850-3853.
59. ZAKI, M. J. 2001. Spade: An efficient algorithm for mining frequent sequences. Mach. Learn. 42, 31-60.
60. ZHAO, L., WANG, X., SUKTHANKAR, G., AND SUKTHANKAR, R. 2010. Motif discovery and feature selection for crf-based activity recognition. In Proceedings of the International Conference on Pattern Recognition, 3826-3829.