Variance Based Pattern Detection for Inferring Activities of Daily Living
Abstract
Being able to recognize activities of daily living (ADLs) using non-intrusive devices is very much dependent on the ability to discover a range of patterns within captured datasets. Pattern recognition plays an important role as the presence of patterns in one instance and the absence in another presents an accurate means of classification. In this paper, we present a pattern recognition approach based on recognizing the emerging patterns for inferring ADLs. In order to validate this proposed approach, a series of activity datasets have been used for validation experiments.
References
U.M. Fayyad, G.P. Shapiro and P. Smith, “From Data Mining to Knowledge Discovery in Databases”, in Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, AI Magazine vol. 17, No 3 pp. 1–34, 1996, Menlo Park, CA, USA.
U. Hinrichs and S. Carpendale, “Making Sense of Wild Data: Using Visualization to Analyze in-the-wild Video Records", in Proceedings of the Designing Interactive Systems Conference, Research in the Wild workshop, Newcastle Upon Tyne, United Kingdom, June 11, 2012.
B. Cvetkovic, M. Lustrek, B. Kaluza and M. Gams, ”Semi-supervised Learning for Adaptation of Human Activity Recognition Classifier to the User”, Proc. of Int. Joint Conf. on Artificial Intelligence, IJCAI, Barcelona, Catalonia, Spain, pp. 24–29, 2011.
D. Perera, J. Kay, I. Koprinska, K. Yacef and O. R. Zaïane, "Clustering and sequential pattern mining of online collaborative learning data", IEEE Transaction on Knowledge and Data Engg., vol. 21, pp. 759 -772, 2009
B. Liu, W. Hsu and Y. Ma, “Integrating classification and association rule mining”, Proc. of the 4th Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, August 27 – 31, 1998, pp. 80–86, 1998.
W. Li, J. Han and J. Pei, “CMAR: Accurate and efficient classification based on multiple class-association rules”, Proceedings of 1st International Conference on Data Mining, San Jose, CA, United States, Nov, 29 - Dec, 2, 2001, pp. 369–376, 2001.
X. Yin and J. Han, “CPAR: Classification based on predictive association rules,” in Proceedings of SIAM International Conference on Data Mining, San Francisco, CA, pp. 331–335, May 2003.
R. J. Hilderman and H. J. Hamilton. Knowledge Discovery and Interestingness Measures: A Survey. University of Regina, Regina, Canada.[Online].Available: http:// www. cs.uregina. ca/Research/Techreports/2000-01.pdf.
H. Lodhi, C. Saunders and J. Shawe-Taylor, N. Cristianini and C. Watkins, “Text classification using string kernels”, J. Machine Learning Res, vol. 2, pp. 419–444, 2002.
C. Leslie, E. Eskin and W. S. Noble, “The spectrum kernel: A string kernel for svm protein classification”, Proc. of the Seventh Pacific Symposium on Biocomputing (PSB), Singapore, , pp. 564– 575, 2002.
G. Dong and J. Li, “Efficient mining of emerging patterns: Discovering trends and differences,” Proceedings of Fifth ACM SIGKDD, Int. Conf. on Knowledge Discovery and Data Mining (KDD’99), San Diego, CA, USA, pp. 43– 52, Aug 15 - 18, 1999.
B. Bruno, F. Mastrogiovanni and A. Sgorbissa, “UCI Machine Learning Repository”, [https://archive.ics.uci.edu/ml/datasets/ Dataset+for+ADL+Recognition+with+Wrist-worn+Accelero meter], School of Information and Computer Science, University of California, Irvine, CA, USA.
W. Ali et al. / The Nucleus 54, No. 2 (2017) 128-135
P. Casale, O. Pujol and P. Radeva, "Personalization and user verification in wearable systems using biometric walking patterns". Personal and Ubiquitous Computing. [Online], vol. 16, no. 5, pp. 563-580, April, 2011, Available:https://www. researchgate.net/ profile/Petia_Radeva2/publication/227192676_Personalization_and_user_verification_in_wearable_systems_using_biometric_walking_patterns/links/09e4150b71b8445558000000.pdf.
Z. Wu, L. Wang, X. Tao and J. Lu, “Mining Emerging Patterns for Recognizing Activities of Multiple Users in Pervasive Computing”, In Proceedings of Sixth Annual International Conference on Mobile and Ubiquitous Systems: Networking & Services, MobiQuitous, July, 2009, pp. 1-10.
S. Nasreen, M. A. Azam, U. Naeem and M. A. Ghazanfar, "Inference of activities with unexpected actions using pattern mining", Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan, September 07 - 11, pp. 1479-1488, 2015.
M. E. Pollack, L. E. Brown, D. Colbry, C. E. McCarthy, COrosz, B. Peintner, S. Ramakrishnan and I. T. Samardinos, "Autominder : an intelligent cognitive orthotics system for people with memory impairment". Robotics and AutonomousSystems[Online], vol. 44, no. 3-4, pp.273-282, Available:http://www.math.ntua.gr/ aarg/projects/Thalis_files/Tsamardinos_2.pdf.
DHHu, S. J. Pan, V. W. Zheng and N. N. L, Q. Yang. "Real World Activity Recognition with Multiple Goals",In Proceedings of the Tenth International Conference on Ubiquitous computing Seoul, Korea, September 21 - 24, 2008, pp. 30-40.
M.A. Azam, J. Loo, U. Naeem and M. Adeel, "Recognizing indoor/Outdoor Activities of Low Entropy People using Bluetooth Proximity and Object Usage Data", International Journal of Pervasive Computing and Communications, IJPCC, vol. 9, No. 4, pp. 346 – 366, 2013.
M.A. Azam, J. Loo, S.K.A. Khan, U. Naeem, M. Adeel and W. Ejaz, "Behavioural Pattern Analysis of Low Entropy People Using Proximity Data", Journal of Wireless Mobile Networks, Ubiquitous Computing and Dependable Applications (JoWUA), vol. 3, No. 3, pp. 41 - 49, 2012.
K. H. Schmitz, D. R. Jacobs, A. S. Leon, P. J. Schreiner and B. Sternfeld, "Physical Activity and Body Weight: Associations over Ten Years in the CARDIA Study", International Journal of Obesity, vol. 24, No. 11, pp. 1475-1487, 2000.
M. Zhang and A. Sawchuk, "Motion primitive based human activity recognition using a bag-of-features approach", In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, Miami, Florida, USA, January 28 - 30, 2012, pp. 631–640.
N. Kawaguchi, N. Ogawa, Y. Iwasaki, K. Kaji, T. Terada, K. Murao, S. Inoue, Y. Kawahara, Y. Sumi and N. Nishio, “Hasc challenge: gathering large scale human activity corpus for the real-world activity understandings", Proc. of 2nd Augmented Human International Conference, Article No 27, Tokyo, Japan, March 13 - 13, 2011.
G.Z. Yang, B, Lo and S. Thiemjarus, Body Sensor Networks, 1st Edition, London,Springer Verlag London, 2006.
F. Albinali, S. Intille, W. Haskell and M. Rosenberger, "Using wearable activity type detection to improve physical activity energy expenditure estimation", Proc. of the 12th ACM Int. Conf. on Ubiquitous Computing, Copenhagen, Denmark, pp. 311–320, Sept. 26 - 29, 2010.
O. Chipara, C. Lu, T. Bailey and G.-C. Roman, "Reliable clinical monitoring using wireless sensor networks: experience in a step-down hospital unit", Proc. of the Eighth ACM Conf. on Embedded Networked Sensor Systems, Zürich, Switzerland, pp. 155–168, Nov. 03 - 05, 2010.
H. Lu, W. Pan, N. Lane, T. Choudhury and A. Campbell, "SoundSense: Scalable sound sensing for people-centric sensing
applications on mobile phones", Proc. of the 7th Int. Conf. on Mobile Systems, Applications and Services, Kraków, Poland, pp. 165–178, June 22 - 25, 2009.
P. Zappi, C. Lombriser, T. Steifmeier, E. Farella, D. Roggen, L. Benini and G. Troster, "Activity recognition from on body sensors: Accuracy-power trade-off by dynamic sensor selection", Proc. of the 5th European Conf on Wireless Sensor Networks, Bologna, Italy, pp. 17–33, Jan. 30 – Feb. 01, 2008.
A. Bujari, B. Licar and C. Palazzi, "Movement Pattern Recognition through Smartphone's Accelerometer", Proc. of IEEE Consumer Communications and Networking Conference, Las Vegas, NV, USA, pp. 502 – 506, 14 January-17 January, 2012.
T. Gu, Z. Wu, X. Tao, P. K., and J Lu, "epSICAR: An emerging patterns based approach to sequential, interleaved and concurrent activity recognition", Proc. IEEE Int. Conf. on Pervasive Computing and Communications, pp. 1 – 9, March 9- March 13, 2009.
T. Gu, Z. Wu, L. Wang, X. Tao and J. Lu, "Mining emerging patterns for recognizing activities of multiple users in pervasive computing", Proc. of the 6th Annual Int. Conf. on Mobile and Ubiquitous Systems: Networking Services, Toronto, Canada, pp. 13–16, July, 2009.
T. Gu, L. Wang, Z. Wu, X. Tao and J. Lu, "A pattern mining approach to sensor-based human activity recognition", IEEE Transaction on Knowledge and Data Engineering, vol. 23, pp. 1359 - 1372, Sept. 2011.
J. Modayil, T. Bai and H Kautz, "Improving the recognition of interleaved activities Research note", Proc. of the 10th Int. Conf. on Ubiquitous Computing, Seoul, Korea, pp. 40-43, 21- 24 Sept., 2008.
L. Chen, C. D. Nugent and H. Wang, "A knowledge-driven approach to activity recognition in smart homes", IEEE Transaction on Knowledge and Data Engineering, vol. 24, pp. 961 -975, 2012
T.Y. Wu, C.C. Lian and J.Y. Hsu, “Joint recognition of multiple concurrent activities using factorial conditional random fields”, Proc. of Association for the Advancement of Artificial Intelligence Workshop Plan, Activity, and Intent Recognition, California, 2007.
G. Singla and D. J. Cook, “Interleaved activity recognition for smart home residents”, Proc. of the Fifth International Conf. on Intelligent Environments, Amsterdam, The Netherlands: IOS Press, pp. 145–152, 2009.
L. Jinyan, “Mining emerging patterns to construct accurate and efficient classifier”, Ph.D. dissertation, DCS &SE. University of Melbourne, Melbourne, January 2001.
G. Dong, J. Li and X. Zhang. "Discovering jumping emerging patterns and experiments on real datasets", Proc. of 9th Int. Database Conf., Hong Kong, pp. 15–17, July, 1999.
J. Bailey,T. Manoukian and K. Ramamohanarao, "Classification using constrained emerging patterns", 4th Int. Conf. on Web-Age Inf. Manage., Springer Verlag LNCS 2762. Chengdu, China, August, pp 226-237, 2003.
G. Dong, X. Zhang, L. Wong and J. Li, "CAEP: Classification by Aggregating Emerging Patterns", Proc. of the 2nd Int. Conf. on Discovery Science, Springer-Verlag London, pp. 30-42, 1999.
F. Mastrogiovanni, A. Sgorbissa , T. Vernazza , R. Zaccaria. "Human motion modeling and recognition: A computational approach", Proc. of IEEE Int. Conf. on Automation Science and Engineering, Seoul, South Korea, pp. 156-161, 20 - 24 August, 2012.
P. Casale, O. Pujol and P. Radeva, "Human activity recognition from accelerometer data using a wearable device", Proc. of Iberian Conf. on Pattern Recognition and Image Analysis, Lecture Notes in Computer Science, vol. 6669, , pp. 289-296, 2001, Springer, Berlin, Heidelberg.
