Unsupervised Human Activity Recognition Using the Clustering Approach: A Review

Currently, many applications have emerged from the implementation of softwaredevelopment and hardware use, known as the Internet of things. One of the most importantapplication areas of this type of technology is in health care. Various applications arise daily inorder to improve the quality of life...

Full description

Autores:
Ariza Colpas, Paola Patricia
VICARIO, ENRICO
De-La-Hoz-Franco, Emiro
Pineres-Melo, Marlon
Oviedo Carrascal, Ana Isabel
PATARA, FULVIO
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7356
Acceso en línea:
https://hdl.handle.net/11323/7356
https://doi.org/10.3390/s20092702
https://repositorio.cuc.edu.co/
Palabra clave:
ambient assisted living—AAL
human activity recognition—HAR
activities of dailyliving—ADL
ctivity recognition systems—ARS
clustering
unsupervised activity recognition
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_c3760ff97322dfb05366dc6a2eb94d4f
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7356
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Unsupervised Human Activity Recognition Using the Clustering Approach: A Review
title Unsupervised Human Activity Recognition Using the Clustering Approach: A Review
spellingShingle Unsupervised Human Activity Recognition Using the Clustering Approach: A Review
ambient assisted living—AAL
human activity recognition—HAR
activities of dailyliving—ADL
ctivity recognition systems—ARS
clustering
unsupervised activity recognition
title_short Unsupervised Human Activity Recognition Using the Clustering Approach: A Review
title_full Unsupervised Human Activity Recognition Using the Clustering Approach: A Review
title_fullStr Unsupervised Human Activity Recognition Using the Clustering Approach: A Review
title_full_unstemmed Unsupervised Human Activity Recognition Using the Clustering Approach: A Review
title_sort Unsupervised Human Activity Recognition Using the Clustering Approach: A Review
dc.creator.fl_str_mv Ariza Colpas, Paola Patricia
VICARIO, ENRICO
De-La-Hoz-Franco, Emiro
Pineres-Melo, Marlon
Oviedo Carrascal, Ana Isabel
PATARA, FULVIO
dc.contributor.author.spa.fl_str_mv Ariza Colpas, Paola Patricia
VICARIO, ENRICO
De-La-Hoz-Franco, Emiro
Pineres-Melo, Marlon
Oviedo Carrascal, Ana Isabel
PATARA, FULVIO
dc.subject.spa.fl_str_mv ambient assisted living—AAL
human activity recognition—HAR
activities of dailyliving—ADL
ctivity recognition systems—ARS
clustering
unsupervised activity recognition
topic ambient assisted living—AAL
human activity recognition—HAR
activities of dailyliving—ADL
ctivity recognition systems—ARS
clustering
unsupervised activity recognition
description Currently, many applications have emerged from the implementation of softwaredevelopment and hardware use, known as the Internet of things. One of the most importantapplication areas of this type of technology is in health care. Various applications arise daily inorder to improve the quality of life and to promote an improvement in the treatments of patients athome that suffer from different pathologies. That is why there has emerged a line of work of greatinterest, focused on the study and analysis of daily life activities, on the use of different data analysistechniques to identify and to help manage this type of patient. This article shows the result of thesystematic review of the literature on the use of the Clustering method, which is one of the mostused techniques in the analysis of unsupervised data applied to activities of daily living, as well asthe description of variables of high importance as a year of publication, type of article, most usedalgorithms, types of dataset used, and metrics implemented. These data will allow the reader tolocate the recent results of the application of this technique to a particular area of knowledge
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-11-19T14:39:51Z
dc.date.available.none.fl_str_mv 2020-11-19T14:39:51Z
dc.date.issued.none.fl_str_mv 2020
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7356
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.3390/s20092702
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/7356
https://doi.org/10.3390/s20092702
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv https://www.mdpi.com/journal/sensors/special_issues/human-machine
dc.relation.references.spa.fl_str_mv 1. Jain, A.; Duin, R.; Mao, J. Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 4–37. [CrossRef]
2. Jain, A.; Murty, M.; Flynn, P. Data clustering: A review. ACM Comput. Surv. 1999, 31, 264–323. [CrossRef]
3. Bishop, C. Neural Networks for Pattern Recognition; Oxford University Press: New York, NY, USA, 1995.
4. Sklansky, J.; Siedlecki, W. Large-scale feature selection. In Handbook of Pattern Recognition and Computer Vision; Chen, C., Pau, L., Wang, P., Eds.; World Scientific: Singapore, 1993; pp. 61–124.
5. Kleinberg, J. An impossibility theorem for clustering. In Proceedings of the 2002 15th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 9–14 December 2002; Volume 15, pp. 463–470.
6. Jain, A.; Dubes, R. Algorithms for Clustering Data; Prentice-Hall: Englewood Cliffs, NJ, USA, 1988.
7. Gordon, A. Cluster validation. In Data Science, Classification, and Related Methods; Hayashi, C., Ohsumi, N., Yajima, K., Tanaka, Y., Bock, H., Bada, Y., Eds.; Springer: New York, NY, USA, 1998; pp. 22–39.
8. Dubes, R. Cluster analysis and related issue. In Handbook of Pattern Recognition and Computer Vision; Chen, C., Pau, L., Wang, P., Eds.; World Scientific: Singapore, 1993; pp. 3–32.
9. Bandyopadhyay, S.; Maulik, U. Nonparametric genetic clustering: Comparison of validity indices. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 2001, 31, 120–125. [CrossRef]
10. Bezdek, J.; Pal, N. Some new indexes of cluster validity. IEEE Trans. Syst. Man Cybern. B Cybern. 1998, 28, 301–315. [CrossRef]
11. Dunn, J.C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 1973, 3, 32–57. [CrossRef]
12. Halkidi, M.; Batistakis, Y.; Vazirgiannis, M. Cluster validity methods: Part I & II. SIGMOD Rec. 2002, 31, 40–45.
13. Leung, Y.; Zhang, J.; Xu, Z. Clustering by scale-space filtering. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 1396–1410. [CrossRef]
14. Levine, E.; Domany, E. Resampling method for unsupervised estimation of cluster validity. Neural Comput. 2001, 13, 2573–2593. [CrossRef] [PubMed]
15. Davé, R.; Krishnapuram, R. Robust clustering methods: A unified view. IEEE Trans. Fuzzy Syst. 1997, 5, 270–293. [CrossRef]
16. Geva, A. Hierarchical unsupervised fuzzy clustering. IEEE Trans. Fuzzy Syst. 1999, 7, 723–733. [CrossRef]
17. Hammah, R.; Curran, J. Validity measures for the fuzzy cluster analysis of orientations. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 1467–1472. [CrossRef]
18. Rand, W.M. Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 1971, 66, 846–850. [CrossRef]
19. Lane, N.; Miluzzo, E.; Lu, H.; Peebles, D.; Choudhury, T.; Campbell, A. A survey of mobile phone sensing. IEEE Commun. Mag. 2010, 48, 140–150. [CrossRef]
20. bin Abdullah, M.F.A.; Negara, A.F.P.; Sayeed, M.S.; Choi, D.J.; Muthu, K.S. Classification algorithms in human activity recognition using smartphones. World Acad. Sci. Eng. Technol. 2012, 68, 422–430.
21. Stikic, M.; Schiele, B. Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning. In Proceedings of the 4th International Symposium Location and Context Awareness, Tokyo, Japan, 7–8 May 2009; Volume 5561, pp. 156–173.
22. Chen, L.; Nugent, C.D. Ontology-based activity recognition in intelligent pervasive environments. Int. J. Web Inf. Syst. 2009, 5, 410–430. [CrossRef]
23. Palmes, P.; Pung, H.K.; Gu, T.; Xue, W.; Chen, S. Object relevance weight pattern mining for activity recognition and segmentation. Pervasive Mob. Comput. 2010, 6, 43–57. [CrossRef]
24. Chen, L.; Nugent, C.D.; Wang, H. A Knowledge-Driven Approach to Activity Recognition in Smart Homes. IEEE Trans. Knowl. Data Eng. 2011, 24, 961–974. [CrossRef]
25. Ye, J.; Stevenson, G.; Dobson, S. A top-level ontology for smart environments. Pervasive Mob. Comput. 2011, 7, 359–378. [CrossRef]
26. Jain, A.K.; Flynn, P. Image segmentation using clustering. In Advances in Image Understanding; IEEE Computer Society Press: Piscataway, NJ, USA, 1996.
27. Van Kasteren, T.L.M.; Englebienne, G.; Kröse, B.J.A. Activity recognition using semi-Markov models on real world smart home datasets. J. Ambient Intell. Smart Environ. 2010, 2, 311–325. [CrossRef]
28. Cook, D.; Crandall, A.S.; Thomas, B.L.; Krishnan, N.C. CASAS: A smart home in a box. Computer 2013, 46, 62–69. [CrossRef]
29. Cook, D. Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 2012, 27, 32–38. [CrossRef] [PubMed]
30. Singla, G.; Cook, D.J.; Schmitter-Edgecombe, M. Recognizing independent and joint activities among multiple residents in smart environments. J. Ambient Intell. Hum. Comput. 2010, 1, 57–63. [CrossRef] [PubMed]
31. Almaslukh, B.; AlMuhtadi, J.; Artoli, A. An effective deep autoencoder approach for online smartphone-based human activity recognition. Int. J. Comput. Sci. Netw. Secur. 2017, 17, 160–165.
32. Ordóñez, F.; Roggen, D. Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 2016, 16, 115. [CrossRef] [PubMed]
33. Ha, S.; Choi, S. Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 24–29 July 2016; pp. 381–388.
34. Frigui, H.; Krishnapuram, R. A robust competitive clustering algorithm with applications in computer vision. IEEE Trans. Pattern Anal. Mach. Intell. 1999, 21, 450–465. [CrossRef]
35. Shi, J.; Malik, J. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 888–905.
36. Iwayama, M.; Tokunaga, T. Cluster-based text categorization: A comparison of category search strategies. In Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, WA, USA, 9–13 July 1995; pp. 273–280.
37. Sahami, M. Using Machine Learning to Improve Information Access. Ph.D. Thesis, Stanford University, Stanford, CA, USA, 15 December 1998.
38. Bhatia, S.K.; Deogun, J.S. Conceptual clustering in information retrieval. IEEE Trans. Syst. Man Cybern. Part B Cybern. 1998, 28, 427–436. [CrossRef]
39. Hubert, L.; Arabie, P. The analysis of proximity matrices through sums of matrices having (anti-) Robinson forms. Br. J. Math. Stat. Psychol. 1994, 47, 1–40. [CrossRef]
40. Hu, W.Y.; Scott, J.S. Behavioral obstacles in the annuity market. Financ. Anal. J. 2007, 63, 71–82. [CrossRef]
41. Hung, S.P.; Baldi, P.; Hatfield, G.W. Global Gene Expression Profiling in Escherichia coliK12 THE EFFECTS OF LEUCINE-RESPONSIVE REGULATORY PROTEIN. J. Biol. Chem. 2002, 277, 40309–40323. [CrossRef]
42. De-La-Hoz-Franco, E.; Ariza-Colpas, P.; Quero, J.M.; Espinilla, M. Sensor-based datasets for human activity recognition—A systematic review of literature. IEEE Access 2018, 6, 59192–59210. [CrossRef]
43. Rawassizadeh, R.; Dobbins, C.; Akbari, M.; Pazzani, M. Indexing multivariate mobile data through spatio-temporal event detection and clustering. Sensors 2019, 19, 448. [CrossRef]
44. Bouchard, K.; Lapalu, J.; Bouchard, B.; Bouzouane, A. Clustering of human activities from emerging movements. J. Ambient Intell. Hum. Comput. 2019, 10, 3505–3517. [CrossRef]
45. Jain, A.K. Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 2010, 31, 651–666. [CrossRef]
46. Drineas, P.; Frieze, A.M.; Kannan, R.; Vempala, S.; Vinay, V. Clustering in Large Graphs and Matrices. In Proceeding of the Symposium on Discrete Algorithms (SODA), Baltimore, MD, USA, 17–19 January 1999; Volume 99, pp. 291–299.
47. Gonzalez, T.F. Clustering to minimize the maximum intercluster distance. Theor. Comput. Sci. 1985, 38, 293–306. [CrossRef]
48. Fisher, D.H. Knowledge acquisition via incremental conceptual clustering. Mach. Learn. 1987, 2, 139–172. [CrossRef]
49. Gennari, J.H.; Langley, P.; Fisher, D. Models of incremental concept formation. Artif. Intel. 1989, 40, 11–61. [CrossRef]
50. Aguilar-Martin, J.; De Mantaras, R.L. The Process of Classification and Learning the Meaning of Linguistic Descriptors of Concepts. Approx. Reason. Decis. Anal. 1982, 1982, 165–175.
51. Omran, M.G.; Engelbrecht, A.P.; Salman, A. An overview of clustering methods. Intell. Data Anal. 2007, 11, 583–605. [CrossRef]
52. Bezdek, J.C. Pattern Recognition with Fuzzy Objective Function Algorithms; Plenum Publishing Corporation: New York, NY, USA, 1981.
53. Tamayo, P.; Slomin, D.; Mesirov, J.; Zhu, Q.; Kitareewan, S.; Dmitrovsky, E.; Lander, E.S.; Golub, T.R. Interpreting patterns of gene expresión with self-organizing map: Methos and application to hematopoietic differentiation. Proc. Natl Acad. Sci. USA 1999, 96, 2901–2912. [CrossRef]
54. Toronen, P.; Kolehmainen, M.; Wong, G.; Catrén, E. Analysis of gene expresión data using self-organizing maps. FEBS Lett. 1999, 451, 142–146. [CrossRef]
55. Kohonen, T. Self-Organizing Maps; Springer: Berlin, Germany, 1997.
56. Goldberg, D. Genetic Algorithms in Search, Optimization, and Machine Learning; Addison Wesley: Reading, MA, USA, 1989.
57. Holland, J.H. Hidden Orderhow Adaptation Builds Complexity; Helix Books: Totowa, NJ, USA, 1995.
58. Fogel, L.J.; Owens, A.J.; Walsh, M.J. Artificial Intelligence through Simulated Evolution; Wiley: Chichester, WS, UK, 1966.
59. Fogel, D.B. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence; John Wiley & Sons: Hoboken, NJ, USA, 2006.
60. Holland, J. Genetic algorithms. Sci. Am. 1992, 267, 66–73. [CrossRef]
61. Davis, L. (Ed.) Handbook of Genetic Algorithms; Van Nostrand Reinhold: New York, NY, USA, 1991; p. 385.
62. Chung, F.R.K. Spectral Graph Theory, CBMS Regional Conference Series in Mathematics; American Mathematical Society: Providence, RI, USA, 1997; Volume 92.
63. Fiedler, M. Algebraic connectivity of graphs. Czechoslov. Math. J. 1973, 23, 298–305.
64. Schölkopf, B.; Smola, A.J.; Müller, K.R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 1998, 10, 1299–1319. [CrossRef]
65. Girolami, M. Mercer kernel based clustering in feature space. IEEE Trans. Neural Netw. 2002, 13, 780–784. [CrossRef]
66. Ng, A.Y.; Jordan, M.I.; Weiss, Y. On Spectral Clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, USA, 2001.
67. Rechenber, I. Evolution strategy. In Computational Intelligence: Imitating Life; Zurada, J.M., Marks, R.J., Robinson, C., Eds.; IEEE Press: Piscataway, NJ, USA, 1994.
68. Schwefel, H.-P. Evolution and Optimum Seeking; Wiley: New York, NY, USA, 1995.
69. Koza, J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection; MIT Press: Cambridge, MA, USA, 1992.
70. Von Luxburg, U. A tutorial on spectral clustering. Stat. Comput. 2007, 17, 395–416. [CrossRef]
71. Kohonen, T. The self-organizing map. Proc. IEEE 1990, 78, 1464–1480. [CrossRef]
72. Howedi, A.; Lotfi, A.; Pourabdollah, A. Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living. Entropy 2019, 21, 416. [CrossRef]
73. Cook, D.; Schmitter-Edgecombe, M. Assessing the quality of activities in a smart environment. Methods Inf. Med. 2009, 48, 480–485.
74. Singla,G.; Cook,D.; Schmitter-Edgecombe,M.Trackingactivitiesincomplexsettingsusingsmartenvironment technologies. Int. J. BioSci. Psychiatry Technol. 2009, 1, 25–35.
75. Cook, D.J.; Youngblood, M.; Das, S.K. A multi-agent approach to controlling a smart environment. In Designing Smart Homes; Springer: Berlin/Heidelberg, Germany, 2006; pp. 165–182.
76. Dernbach, S.; Das, B.; Krishnan, N.C.; Thomas, B.L.; Cook, D.J. Simple and complex activity recognition through smart phones. In Proceedings of the 2012 Eighth International Conference on Intelligent Environments, Guanajuato, Mexico, 26–29 June 2012.
77. Sahaf, Y. Comparing Sensor Modalities for Activity Recognition. Master’s Thesis, Washington State University, Pullman, WA, USA, August 2011.
78. Rawassizadeh, R.; Keshavarz, H.; Pazzani, M. Ghost imputation: Accurately reconstructing missing data of the off period. IEEE Trans. Knowl. Data Eng. 2019. [CrossRef]
79. Wilson, D.H. Assistive Intelligent Environments for Automatic Health Monitoring. Ph.D. Thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA, September 2005.
80. Singla, G.; Cook, D.J.; Schmitter-Edgecombe, M. Incorporating temporal reasoning into activity recognition for smart home residents. In Proceedings of the AAAI Workshop on Spatial and Temporal Reasoning, Chicago, IL, USA, 13 July 2008; pp. 53–61.
81. Wren, C.R.; Tapia, E.M. Hierarchical Processing in Scalable and Portable Sensor Networks for Activity Recognition. U.S. Patent No. 7359836, 15 April 2008.
82. Van Kasteren, T.; Noulas, A.; Englebienne, G.; Kröse, B. Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea, 21–24 September 2008; pp. 1–9.
83. Philipose, M.; Fishkin, K.P.; Perkowitz, M.; Patterson, D.J.; Fox, D.; Kautz, H.; Hahnel, D. Inferring activities from interactions with objects. IEEE Pervasive Comput. 2004, 3, 50–57. [CrossRef]
84. Patterson, D.J.; Fox, D.; Kautz, H.; Philipose, M. Fine-grained activity recognition by aggregating abstract object usage. In Proceedings of the Ninth IEEE International Symposium on Wearable Computers (ISWC’05), Osaka, Japan, 18–21 October 2005; pp. 44–51.
85. Hodges, M.R.; Newman, M.W.; Pollack, M.E. Object-Use Activity Monitoring: Feasibility for People with Cognitive Impairments. In Proceedings of the AAAI Spring Symposium: Human Behavior Modeling, Stanford, CA, USA, 23–25 March 2009; pp. 13–18.
86. Fang, F.; Aabith, S.; Homer-Vanniasinkam, S.; Tiwari, M.K. High-resolution 3D printing for healthcare underpinned by small-scale fluidics. In 3D Printing in Medicine; Woodhead Publishing: Cambrigde, MA, USA, 2017; pp. 167–206.
87. Veltink, P.H.; Bussmann, H.J.; De Vries, W.; Martens, W.J.; Van Lummel, R.C. Detection of static and dynamic activities using uniaxial accelerometers. IEEE Trans. Rehab. Eng. 1996, 4, 375–385. [CrossRef] [PubMed]
88. Mathie, M.J.; Coster, A.C.F.; Lovell, N.H.; Celler, B.G. Detection of daily physical activities using a triaxial accelerometer. Med. Biol. Eng. Comput. 2003, 41, 296–301. [CrossRef] [PubMed]
89. Bao, L.; Intille, S.S. Activity recognition from user-annotated acceleration data. In Proceedings of the International Conference on Pervasive Computing, Linz/Vienna, Austria, 21–23 April 2004; Springer: Berlin/Heidelberg, Germany, 2004; pp. 1–17.
90. Chambers, G.S.; Venkatesh, S.; West, G.A.; Bui, H.H. Hierarchical recognition of intentional human gestures for sports video annotation. In Object Recognition Supported by User Interaction for Service Robots; IEEE: Quebec, PQ, Canada, 2002; pp. 1082–1085.
91. Lester, J.; Choudhury, T.; Borriello, G. A practical approach to recognizing physical activities. In Proceedings of the 4th International Conference on Pervasive Computing, Dublin, Ireland, 7–10 May 2006; Springer: Berlin/Heidelberg, Germany, 2006; pp. 1–16.
92. Mantyjarvi, J.; Himberg, J.; Seppanen, T. Recognizing human motion with multiple acceleration sensors. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Tucson, AZ, USA, 7–10 October 2001; pp. 747–752.
93. Al-Ani, T.; Le Ba, Q.T.; Monacelli, E. On-line automatic detection of human activity in home using wavelet and hidden markov models scilab toolkits. In Proceedings of the 2007 IEEE International Conference on Control Applications, Singapore, 1–3 October 2007; pp. 485–490.
94. Zheng,Y.; Liu,Q.; Chen,E.; Ge,Y.; Zhao,J.L.Timeseriesclassificationusingmulti-channelsdeepconvolutional neural networks. In Proceedings of the International Conference on Web-Age Information Management, Macau, China, 16–18 June 2014; Springer: Cham, Switzerland, 2014; pp. 298–310.
95. Jiang, W.; Yin, Z. Human activity recognition using wearable sensors by deep convolutional neural networks. In Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia, 26–30 October 2015; pp. 1307–1310.
96. Alsheikh, M.A.; Selim, A.; Niyato, D.; Doyle, L.; Lin, S.; Tan, H.P. Deep activity recognition models with triaxial accelerometers. In Proceedings of the Workshops at the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 12–13 February 2016.
97. Hu, D.H.; Yang, Q. CIGAR: Concurrent and Interleaving Goal and Activity Recognition. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, Chicago, IL, USA, 13–17 July 2008; Volume 8, pp. 1363–1368.
98. Zhang, L.; Wu, X.; Luo, D. Recognizing human activities from raw accelerometer data using deep neural networks. In Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 9–11 December 2015; pp. 865–870.
99. Using the Multicom Domus Dataset 2011. Available online: https://hal.archives-ouvertes.fr/hal-01473142/ (accessed on 25 January 2020).
100. Zhang, S.; McCullagh, P.; Nugent, C.; Zheng, H. Activity monitoring using a smart phone’s accelerometer with hierarchical classification. In Proceedings of the 2010 Sixth International Conference on Intelligent Environments, Kuala Lumpur, Malaysia, 19–21 July 2010; pp. 158–163.
101. Espinilla, M.; Martínez, L.; Medina, J.; Nugent, C. The experience of developing the UJAmI Smart lab. IEEE Access 2018, 6, 34631–34642. [CrossRef]
102. Kitchenham, B.; Brereton, O.P.; Budgen, D.; Turner, M.; Bailey, J.; Linkman, S. Systematic literature reviews in software engineering—A systematic literature review. Inf. Softw. Technol. 2009, 51, 7–15. [CrossRef]
103. Fahad, L.G.; Ali, A.; Rajarajan, M. Learning models for activity recognition in smart homes. In Information Science and Applications; Springer: Berlin/Heidelberg, Germany, 2015; pp. 819–826.
104. Nguyen, D.; Le, T.; Nguyen, S. An Algorithmic Method of Calculating Neighborhood Radius for Clustering In-home Activities within Smart Home Environment. In Proceedings of the 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), Bangkok, Thailand, 25–27 January 2016; pp. 42–47.
105. Nguyen, D.; Le, T.; Nguyen, S. A Novel Approach to Clustering Activities within Sensor Smart Homes. Int. J. Simul. Syst. Sci. Technol. 2016, 17. [CrossRef]
106. Sukor, A.S.A.; Zakaria, A.; Rahim, N.A.; Setchi, R. Semantic knowledge base in support of activity recognition in smart home environments. Int. J. Eng. Technol. 2018, 7, 67–72. [CrossRef]
107. Jänicke, M.; Sick, B.; Tomforde, S. Self-adaptive multi-sensor activity recognition systems based on gaussian mixture models. Informatics 2018, 5, 38. [CrossRef]
108. Honarvar, A.R.; Zaree, T. Frequent sequence pattern based activity recognition in smart environment. Intell. Decis. Technol. 2018, 12, 349–357. [CrossRef]
109. Chen, W.H.; Chen, Y. An ensemble approach to activity recognition based on binary sensor readings. In Proceedings of the 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, China, 12–15 October 2017; pp. 1–5.
110. Khan, M.A.A.H.; Roy, N. Transact: Transfer learning enabled activity recognition. In Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, HI, USA, 13–17 March 2017; pp. 545–550.
111. Fahad, L.G.; Tahir, S.F.; Rajarajan, M. Feature selection and data balancing for activity recognition in smart homes. In Proceedings of the 2015 IEEE International Conference on Communications (ICC), London, UK, 8–12 June 2015; pp. 512–517.
112. Fahad, L.G.; Khan, A.; Rajarajan, M. Activity recognition in smart homes with self verification of assignments. Neurocomputing 2015, 149, 1286–1298. [CrossRef]
113. Bota, P.; Silva, J.; Folgado, D.; Gamboa, H. A Semi-Automatic Annotation Approach for Human Activity Recognition. Sensors 2019, 19, 501. [CrossRef] [PubMed]
114. Zhang, S.; Ng, W.W.; Zhang, J.; Nugent, C.D.; Irvine, N.; Wang, T. Evaluation of radial basis function neural network minimizing L-GEM for sensor-based activity recognition. J. Ambient Intell. Hum. Comput. 2019. [CrossRef]
115. Wen, J.; Zhong, M. Activity discovering and modelling with labelled and unlabelled data in smart environments. Expert Syst. Appl. 2015, 42, 5800–5810. [CrossRef]
116. Fahad, L.G.; Rajarajan, M. Integration of discriminative and generative models for activity recognition in smart homes. Appl. Soft Comput. 2015, 37, 992–1001. [CrossRef]
117. Ihianle, I.; Naeem, U.; Islam, S.; Tawil, A.R. A hybrid approach to recognising activities of daily living from object use in the home environment. Informatics 2018, 5, 6. [CrossRef]
118. Chua, S.L.; Foo, L.K. Sensor selection in smart homes. Procedia Comput. Sci. 2015, 69, 116–124. [CrossRef]
119. Shahi Soozaei, A. Human Activity Recognition in Smart Homes. Ph.D. Thesis, University of Otago, Dunedin, New Zealand, January 2019.
120. Caldas, T.V. From Binary to Multi-Class Divisions: Improvements on Hierarchical Divisive Human Activity Recognition. Master’s Thesis, Universidade do Porto, Oporto, Portugal, July 2019.
121. Fang, L.; Ye, J.; Dobson, S. Discovery and recognition of emerging human activities using a hierarchical mixture of directional statistical models. IEEE Trans. Knowl. Data Eng. 2019. [CrossRef]
122. Guo, J.; Li, Y.; Hou, M.; Han, S.; Ren, J. Recognition of Daily Activities of Two Residents in a Smart Home Based on Time Clustering. Sensors 2020, 20, 1457. [CrossRef]
123. Kavitha, R.; Binu, S. Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living. Procedia Comput. Sci. 2019, 165, 314–321. [CrossRef]
124. Akter, S.S. Improving Sensor Network Predictions through the Identification of Graphical Features. Ph.D. Thesis, Washington State University, Washington, DC, USA, August 2019.
125. Oukrich, N. Daily Human Activity Recognition in Smart Home based on Feature Selection, Neural Network and Load Signature of Appliances. Ph.D. Thesis, Mohammed V University In Rabat, Rabat, Morocco, April 2019.
126. Yala, N. Contribution aux Méthodes de Classification de Signaux de Capteurs dans un Habitat Intelligent. Ph.D. Thesis, The University of Science and Technology—Houari Boumediene, Bab-Ezzouar, Algeria, October 2019.
127. Lyu, F.; Fang, L.; Xue, G.; Xue, H.; Li, M. Large-Scale Full WiFi Coverage: Deployment and Management Strategy Based on User Spatio-Temporal Association Analytics. IEEE Internet Things J. 2019, 6, 9386–9398. [CrossRef]
128. Chetty, G.; White, M. Body sensor networks for human activity recognition. In Proceedings of the 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 11–12 February 2016; pp. 660–665.
129. Singh, T.; Vishwakarma, D.K. Video benchmarks of human action datasets: A review. Artif. Intell. Rev. 2019, 52, 1107–1154. [CrossRef]
130. Senda, M.; Ha, D.; Watanabe, H.; Katagiri, S.; Ohsaki, M. Maximum Bayes Boundary-Ness Training for Pattern Classification. In Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning, Hangzhou, China, 27–29 November 2019; pp. 18–28.
131. Petrovich, M.; Yamada, M. Fast local linear regression with anchor regularization. arXiv 2020, arXiv:2003.05747.
132. Yadav, A.; Kumar, E. A Literature Survey on Cyber Security Intrusion Detection Based on Classification Methods of Supervised Machine Learning; Bloomsbury: New Delhi, India, 2019.
133. Marimuthu, P.; Perumal, V.; Vijayakumar, V. OAFPM: Optimized ANFIS using frequent pattern mining for activity recognition. J. Supercomput. 2019, 75, 5347–5366. [CrossRef]
134. Raeiszadeh, M.; Tahayori, H.; Visconti, A. Discovering varying patterns of Normal and interleaved ADLs in smart homes. Appl. Intell. 2019, 49, 4175–4188. [CrossRef]
135. Hossain, H.S.; Roy, N. Active Deep Learning for Activity Recognition with Context Aware Annotator Selection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 4–8 August 2019; pp. 1862–1870.
136. Kong, D.; Bao, Y.; Chen, W. Collaborative learning based on centroid-distance-vector for wearable devices. Knowl.-Based Syst. 2020. [CrossRef]
137. Lentzas, A.; Vrakas, D. Non-intrusive human activity recognition and abnormal behavior detection on elderly people: A review. Artif. Intell. Rev. 2020, 53, 1975–2021. [CrossRef]
138. Arya, M. Automated Detection of Acute Leukemia Using K-Means Clustering Algorithm. Master’s Thesis, North Dakota State University, Fargo, ND, USA, May 2019.
139. Chetty, G.; Yamin, M. Intelligent human activity recognition scheme for eHealth applications. Malays. J. Comput. Sci. 2015, 28, 59–69.
140. Soulas, J.; Lenca, P.; Thépaut, A. Unsupervised discovery of activities of daily living characterized by their periodicity and variability. Eng. Appl. Artif. Intell. 2015, 45, 90–102. [CrossRef]
141. Rojlertjanya, P. Customer Segmentation Based on the RFM Analysis Model Using K-Means Clustering Technique: A Case of IT Solution and Service Provider in Thailand. Master’s Thesis, Bangkok University, Bangkok, Thailand, 16 August 2019.
142. Zhao, B.; Shao, B. Analysis the Consumption Behavior Based on Weekly Load Correlation and K-means Clustering Algorithm. In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, Cairo, Egypt, 26–28 October 2019; Springer: Cham, Switzerland, 2019; pp. 70–81.
143. Zahi,S.; Achchab,B.ClusteringofthepopulationbenefitingfromhealthinsuranceusingK-means. InProceedings of the 4th International Conference on Smart City Applications, Casablanca, Morocco, 2–4 October 2019; pp. 1–6.
144. Dana, R.D.; Dikananda, A.R.; Sudrajat, D.; Wanto, A.; Fasya, F. Measurement of health service performance through machine learning using clustering techniques. J. Phys. Conf. Ser. 2019, 1360, 012017. [CrossRef]
145. Baek, J.W.; Kim, J.C.; Chun, J.; Chung, K. Hybrid clustering based health decision-making for improving dietary habits. Technol. Health Care 2019, 27, 459–472. [CrossRef] [PubMed]
146. Rashid, J.; Shah, A.; Muhammad, S.; Irtaza, A. A novel fuzzy k-means latent semantic analysis (FKLSA) approach for topic modeling over medical and health text corpora. J. Intell. Fuzzy Syst. 2019, 37, 6573–6588. [CrossRef]
147. Lütz, E. Unsupervised Machine Learning to Detect Patient Subgroups in Electronic Health Records. Master’s Thesis, KTH Royal Institute of Technology, Stockholm, Sweden, January 2019.
148. Maturo, F.; Ferguson, J.; Di Battista, T.; Ventre, V. A fuzzy functional k-means approach for monitoring Italian regions according to health evolution over time. Soft Comput. 2019. [CrossRef]
149. Wang, S.; Li, M.; Hu, N.; Zhu, E.; Hu, J.; Liu, X.; Yin, J. K-means clustering with incomplete data. IEEE Access 2019, 7, 69162–69171. [CrossRef]
150. Long, J.; Sun, W.; Yang, Z.; & Raymond, O.I. Asymmetric Residual Neural Network for Accurate Human Activity Recognition. Information 2019, 10, 203. [CrossRef]
151. Yuan, C.; Yang, H. Research on K-value selection method of K-means clustering algorithm. J. Multidiscip. Sci. J. 2019, 2, 226–235. [CrossRef]
152. Wang, P.; Shi, H.; Yang, X.; Mi, J. Three-way k-means: Integrating k-means and three-way decision. Int. J. Mach. Learn. Cybern. 2019, 10, 2767–2777. [CrossRef]
153. Sadeq, S.; Yetkin, G. Semi-Supervised Sparse Data Clustering Performance Investigation. In Proceedings of the International Conference on Data Science, MachineLearning and Statistics, Van, Turkey, 26–29 June 2019; p. 463.
154. Boddana, S.; Talla, H. Performance Examination of Hard Clustering Algorithm with Distance Metrics. Int. J. Innov. Technol. Explor. Eng. 2019, 9. [CrossRef]
155. Xiao, Y.; Chang, Z.; Liu, B. An efficient active learning method for multi-task learning. Knowl. Based Syst. 2020, 190, 105137. [CrossRef]
156. Yao, L.; Nie, F.; Sheng, Q.Z.; Gu, T.; Li, X.; Wang, S. Learning from less for better: Semi-supervised activity recognition via shared structure discovery. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 12–16 September 2016; pp. 13–24.
157. Tax, N.; Sidorova, N.; van der Aalst, W.M. Discovering more precise process models from event logs by filtering out chaotic activities. J. Intell. Inf. Syst. 2019, 52, 107–139. [CrossRef]
158. Artavanis-Tsakonas, K.; Karpiyevich, M.; Adjalley, S.; Mol, M.; Ascher, D.; Mason, B.; van der Heden van Noort, G.; Laman, H.; Ovaa, H.; Lee, M. Nedd8 hydrolysis by UCH proteases in Plasmodium parasites. PLoS Pathog. 2019, 15, e1008086.
159. Koole, G. An Introduction to Business Analytics; MG Books: Amsterdam, The Netherlands, 2019.
160. Oh, H.; Jain, R. Detecting Events of Daily Living Using Multimodal Data. arXiv 2019, arXiv:1905.09402.
161. Caleb-Solly, P.; Gupta, P.; McClatchey, R. Tracking changes in user activity from unlabelled smart home sensor data using unsupervised learning methods. Neural Comput. Appl. 2020. [CrossRef]
162. Patel, A.; Shah, J. Sensor-based activity recognition in the context of ambient assisted living systems: A review. J. Ambient Intell. Smart Environ. 2019, 11, 301–322. [CrossRef]
163. Leotta, F.; Mecella, M.; Sora, D. Visual process maps: A visualization tool for discovering habits in smart homes. J. Ambient Intell. Hum. Comput. 2019. [CrossRef]
164. Ferilli, S.; Angelastro, S. Activity prediction in process mining using the WoMan framework. J. Intell. Inf. Syst. 2019, 53, 93–112. [CrossRef]
165. Wong, W. Combination Clustering: Evidence Accumulation Clustering for Dubious Feature Sets. OSF Prepr. 2019. [CrossRef]
166. Wong, W.; Tsuchiya, N. Evidence Accumulation Clustering Using Combinations of Features; Center for Open Science: Victoria, Australia, 2019.
167. Zhao, W.; Li, P.; Zhu, C.; Liu, D.; Liu, X. Defense Against Poisoning Attack via Evaluating Training Samples Using Multiple Spectral Clustering Aggregation Method. CMC-Comput. Mater. Cont. 2019, 59, 817–832. [CrossRef]
168. Yang, Y.; Zheng, K.; Wu, C.; Niu, X.; Yang, Y. Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks. Appl. Sci. 2019, 9, 238. [CrossRef]
169. Cuzzocrea, A.; Gaber, M.M.; Fadda, E.; Grasso, G.M. An innovative framework for supporting big atmospheric data analytics via clustering-based spatio-temporal analysis. J. Ambient Intell. Hum. Comput. 2019, 10, 3383–3398. [CrossRef]
dc.rights.spa.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.source.spa.fl_str_mv sensors
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv https://www.mdpi.com/1424-8220/20/9/2702
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/de46b136-b0b5-4160-b38f-bb1380662ec2/download
https://repositorio.cuc.edu.co/bitstreams/63bb4b83-7b52-4eb9-8c71-b2bf4b23b9d4/download
https://repositorio.cuc.edu.co/bitstreams/2d99197b-6d2e-46c0-bac3-459f5aa7a534/download
https://repositorio.cuc.edu.co/bitstreams/a0b5032b-6745-4f64-8ed1-5c13779e5a92/download
https://repositorio.cuc.edu.co/bitstreams/3c0487ae-f081-4d4b-8db1-0f95aae7be97/download
https://repositorio.cuc.edu.co/bitstreams/2d39f465-cf62-4703-a615-2cce17fcf18d/download
bitstream.checksum.fl_str_mv cf4d0c64f513994ca9dd28d10e031fc9
4460e5956bc1d1639be9ae6146a50347
e30e9215131d99561d40d6b0abbe9bad
36211ab3e5a80f28dff15147880d9734
36211ab3e5a80f28dff15147880d9734
ab5e4717fb4942903217b2f762767b72
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1828166880719798272
spelling Ariza Colpas, Paola PatriciaVICARIO, ENRICODe-La-Hoz-Franco, EmiroPineres-Melo, MarlonOviedo Carrascal, Ana IsabelPATARA, FULVIO2020-11-19T14:39:51Z2020-11-19T14:39:51Z2020https://hdl.handle.net/11323/7356https://doi.org/10.3390/s20092702Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Currently, many applications have emerged from the implementation of softwaredevelopment and hardware use, known as the Internet of things. One of the most importantapplication areas of this type of technology is in health care. Various applications arise daily inorder to improve the quality of life and to promote an improvement in the treatments of patients athome that suffer from different pathologies. That is why there has emerged a line of work of greatinterest, focused on the study and analysis of daily life activities, on the use of different data analysistechniques to identify and to help manage this type of patient. This article shows the result of thesystematic review of the literature on the use of the Clustering method, which is one of the mostused techniques in the analysis of unsupervised data applied to activities of daily living, as well asthe description of variables of high importance as a year of publication, type of article, most usedalgorithms, types of dataset used, and metrics implemented. These data will allow the reader tolocate the recent results of the application of this technique to a particular area of knowledgeAriza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600VICARIO, ENRICO-will be generated-orcid-0000-0002-4983-4386-600De-La-Hoz-Franco, Emiro-will be generated-orcid-0000-0002-4926-7414-600Pineres-Melo, MarlonOviedo Carrascal, Ana Isabel-will be generated-orcid-0000-0002-7105-7819-600PATARA, FULVIO-will be generated-orcid-0000-0002-9050-088X-600application/pdfengCorporación Universidad de la Costahttps://www.mdpi.com/journal/sensors/special_issues/human-machine1. Jain, A.; Duin, R.; Mao, J. Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 4–37. [CrossRef]2. Jain, A.; Murty, M.; Flynn, P. Data clustering: A review. ACM Comput. Surv. 1999, 31, 264–323. [CrossRef]3. Bishop, C. Neural Networks for Pattern Recognition; Oxford University Press: New York, NY, USA, 1995.4. Sklansky, J.; Siedlecki, W. Large-scale feature selection. In Handbook of Pattern Recognition and Computer Vision; Chen, C., Pau, L., Wang, P., Eds.; World Scientific: Singapore, 1993; pp. 61–124.5. Kleinberg, J. An impossibility theorem for clustering. In Proceedings of the 2002 15th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 9–14 December 2002; Volume 15, pp. 463–470.6. Jain, A.; Dubes, R. Algorithms for Clustering Data; Prentice-Hall: Englewood Cliffs, NJ, USA, 1988.7. Gordon, A. Cluster validation. In Data Science, Classification, and Related Methods; Hayashi, C., Ohsumi, N., Yajima, K., Tanaka, Y., Bock, H., Bada, Y., Eds.; Springer: New York, NY, USA, 1998; pp. 22–39.8. Dubes, R. Cluster analysis and related issue. In Handbook of Pattern Recognition and Computer Vision; Chen, C., Pau, L., Wang, P., Eds.; World Scientific: Singapore, 1993; pp. 3–32.9. Bandyopadhyay, S.; Maulik, U. Nonparametric genetic clustering: Comparison of validity indices. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 2001, 31, 120–125. [CrossRef]10. Bezdek, J.; Pal, N. Some new indexes of cluster validity. IEEE Trans. Syst. Man Cybern. B Cybern. 1998, 28, 301–315. [CrossRef]11. Dunn, J.C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 1973, 3, 32–57. [CrossRef]12. Halkidi, M.; Batistakis, Y.; Vazirgiannis, M. Cluster validity methods: Part I & II. SIGMOD Rec. 2002, 31, 40–45.13. Leung, Y.; Zhang, J.; Xu, Z. Clustering by scale-space filtering. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 1396–1410. [CrossRef]14. Levine, E.; Domany, E. Resampling method for unsupervised estimation of cluster validity. Neural Comput. 2001, 13, 2573–2593. [CrossRef] [PubMed]15. Davé, R.; Krishnapuram, R. Robust clustering methods: A unified view. IEEE Trans. Fuzzy Syst. 1997, 5, 270–293. [CrossRef]16. Geva, A. Hierarchical unsupervised fuzzy clustering. IEEE Trans. Fuzzy Syst. 1999, 7, 723–733. [CrossRef]17. Hammah, R.; Curran, J. Validity measures for the fuzzy cluster analysis of orientations. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 1467–1472. [CrossRef]18. Rand, W.M. Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 1971, 66, 846–850. [CrossRef]19. Lane, N.; Miluzzo, E.; Lu, H.; Peebles, D.; Choudhury, T.; Campbell, A. A survey of mobile phone sensing. IEEE Commun. Mag. 2010, 48, 140–150. [CrossRef]20. bin Abdullah, M.F.A.; Negara, A.F.P.; Sayeed, M.S.; Choi, D.J.; Muthu, K.S. Classification algorithms in human activity recognition using smartphones. World Acad. Sci. Eng. Technol. 2012, 68, 422–430.21. Stikic, M.; Schiele, B. Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning. In Proceedings of the 4th International Symposium Location and Context Awareness, Tokyo, Japan, 7–8 May 2009; Volume 5561, pp. 156–173.22. Chen, L.; Nugent, C.D. Ontology-based activity recognition in intelligent pervasive environments. Int. J. Web Inf. Syst. 2009, 5, 410–430. [CrossRef]23. Palmes, P.; Pung, H.K.; Gu, T.; Xue, W.; Chen, S. Object relevance weight pattern mining for activity recognition and segmentation. Pervasive Mob. Comput. 2010, 6, 43–57. [CrossRef]24. Chen, L.; Nugent, C.D.; Wang, H. A Knowledge-Driven Approach to Activity Recognition in Smart Homes. IEEE Trans. Knowl. Data Eng. 2011, 24, 961–974. [CrossRef]25. Ye, J.; Stevenson, G.; Dobson, S. A top-level ontology for smart environments. Pervasive Mob. Comput. 2011, 7, 359–378. [CrossRef]26. Jain, A.K.; Flynn, P. Image segmentation using clustering. In Advances in Image Understanding; IEEE Computer Society Press: Piscataway, NJ, USA, 1996.27. Van Kasteren, T.L.M.; Englebienne, G.; Kröse, B.J.A. Activity recognition using semi-Markov models on real world smart home datasets. J. Ambient Intell. Smart Environ. 2010, 2, 311–325. [CrossRef]28. Cook, D.; Crandall, A.S.; Thomas, B.L.; Krishnan, N.C. CASAS: A smart home in a box. Computer 2013, 46, 62–69. [CrossRef]29. Cook, D. Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 2012, 27, 32–38. [CrossRef] [PubMed]30. Singla, G.; Cook, D.J.; Schmitter-Edgecombe, M. Recognizing independent and joint activities among multiple residents in smart environments. J. Ambient Intell. Hum. Comput. 2010, 1, 57–63. [CrossRef] [PubMed]31. Almaslukh, B.; AlMuhtadi, J.; Artoli, A. An effective deep autoencoder approach for online smartphone-based human activity recognition. Int. J. Comput. Sci. Netw. Secur. 2017, 17, 160–165.32. Ordóñez, F.; Roggen, D. Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 2016, 16, 115. [CrossRef] [PubMed]33. Ha, S.; Choi, S. Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 24–29 July 2016; pp. 381–388.34. Frigui, H.; Krishnapuram, R. A robust competitive clustering algorithm with applications in computer vision. IEEE Trans. Pattern Anal. Mach. Intell. 1999, 21, 450–465. [CrossRef]35. Shi, J.; Malik, J. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 888–905.36. Iwayama, M.; Tokunaga, T. Cluster-based text categorization: A comparison of category search strategies. In Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, WA, USA, 9–13 July 1995; pp. 273–280.37. Sahami, M. Using Machine Learning to Improve Information Access. Ph.D. Thesis, Stanford University, Stanford, CA, USA, 15 December 1998.38. Bhatia, S.K.; Deogun, J.S. Conceptual clustering in information retrieval. IEEE Trans. Syst. Man Cybern. Part B Cybern. 1998, 28, 427–436. [CrossRef]39. Hubert, L.; Arabie, P. The analysis of proximity matrices through sums of matrices having (anti-) Robinson forms. Br. J. Math. Stat. Psychol. 1994, 47, 1–40. [CrossRef]40. Hu, W.Y.; Scott, J.S. Behavioral obstacles in the annuity market. Financ. Anal. J. 2007, 63, 71–82. [CrossRef]41. Hung, S.P.; Baldi, P.; Hatfield, G.W. Global Gene Expression Profiling in Escherichia coliK12 THE EFFECTS OF LEUCINE-RESPONSIVE REGULATORY PROTEIN. J. Biol. Chem. 2002, 277, 40309–40323. [CrossRef]42. De-La-Hoz-Franco, E.; Ariza-Colpas, P.; Quero, J.M.; Espinilla, M. Sensor-based datasets for human activity recognition—A systematic review of literature. IEEE Access 2018, 6, 59192–59210. [CrossRef]43. Rawassizadeh, R.; Dobbins, C.; Akbari, M.; Pazzani, M. Indexing multivariate mobile data through spatio-temporal event detection and clustering. Sensors 2019, 19, 448. [CrossRef]44. Bouchard, K.; Lapalu, J.; Bouchard, B.; Bouzouane, A. Clustering of human activities from emerging movements. J. Ambient Intell. Hum. Comput. 2019, 10, 3505–3517. [CrossRef]45. Jain, A.K. Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 2010, 31, 651–666. [CrossRef]46. Drineas, P.; Frieze, A.M.; Kannan, R.; Vempala, S.; Vinay, V. Clustering in Large Graphs and Matrices. In Proceeding of the Symposium on Discrete Algorithms (SODA), Baltimore, MD, USA, 17–19 January 1999; Volume 99, pp. 291–299.47. Gonzalez, T.F. Clustering to minimize the maximum intercluster distance. Theor. Comput. Sci. 1985, 38, 293–306. [CrossRef]48. Fisher, D.H. Knowledge acquisition via incremental conceptual clustering. Mach. Learn. 1987, 2, 139–172. [CrossRef]49. Gennari, J.H.; Langley, P.; Fisher, D. Models of incremental concept formation. Artif. Intel. 1989, 40, 11–61. [CrossRef]50. Aguilar-Martin, J.; De Mantaras, R.L. The Process of Classification and Learning the Meaning of Linguistic Descriptors of Concepts. Approx. Reason. Decis. Anal. 1982, 1982, 165–175.51. Omran, M.G.; Engelbrecht, A.P.; Salman, A. An overview of clustering methods. Intell. Data Anal. 2007, 11, 583–605. [CrossRef]52. Bezdek, J.C. Pattern Recognition with Fuzzy Objective Function Algorithms; Plenum Publishing Corporation: New York, NY, USA, 1981.53. Tamayo, P.; Slomin, D.; Mesirov, J.; Zhu, Q.; Kitareewan, S.; Dmitrovsky, E.; Lander, E.S.; Golub, T.R. Interpreting patterns of gene expresión with self-organizing map: Methos and application to hematopoietic differentiation. Proc. Natl Acad. Sci. USA 1999, 96, 2901–2912. [CrossRef]54. Toronen, P.; Kolehmainen, M.; Wong, G.; Catrén, E. Analysis of gene expresión data using self-organizing maps. FEBS Lett. 1999, 451, 142–146. [CrossRef]55. Kohonen, T. Self-Organizing Maps; Springer: Berlin, Germany, 1997.56. Goldberg, D. Genetic Algorithms in Search, Optimization, and Machine Learning; Addison Wesley: Reading, MA, USA, 1989.57. Holland, J.H. Hidden Orderhow Adaptation Builds Complexity; Helix Books: Totowa, NJ, USA, 1995.58. Fogel, L.J.; Owens, A.J.; Walsh, M.J. Artificial Intelligence through Simulated Evolution; Wiley: Chichester, WS, UK, 1966.59. Fogel, D.B. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence; John Wiley & Sons: Hoboken, NJ, USA, 2006.60. Holland, J. Genetic algorithms. Sci. Am. 1992, 267, 66–73. [CrossRef]61. Davis, L. (Ed.) Handbook of Genetic Algorithms; Van Nostrand Reinhold: New York, NY, USA, 1991; p. 385.62. Chung, F.R.K. Spectral Graph Theory, CBMS Regional Conference Series in Mathematics; American Mathematical Society: Providence, RI, USA, 1997; Volume 92.63. Fiedler, M. Algebraic connectivity of graphs. Czechoslov. Math. J. 1973, 23, 298–305.64. Schölkopf, B.; Smola, A.J.; Müller, K.R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 1998, 10, 1299–1319. [CrossRef]65. Girolami, M. Mercer kernel based clustering in feature space. IEEE Trans. Neural Netw. 2002, 13, 780–784. [CrossRef]66. Ng, A.Y.; Jordan, M.I.; Weiss, Y. On Spectral Clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, USA, 2001.67. Rechenber, I. Evolution strategy. In Computational Intelligence: Imitating Life; Zurada, J.M., Marks, R.J., Robinson, C., Eds.; IEEE Press: Piscataway, NJ, USA, 1994.68. Schwefel, H.-P. Evolution and Optimum Seeking; Wiley: New York, NY, USA, 1995.69. Koza, J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection; MIT Press: Cambridge, MA, USA, 1992.70. Von Luxburg, U. A tutorial on spectral clustering. Stat. Comput. 2007, 17, 395–416. [CrossRef]71. Kohonen, T. The self-organizing map. Proc. IEEE 1990, 78, 1464–1480. [CrossRef]72. Howedi, A.; Lotfi, A.; Pourabdollah, A. Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living. Entropy 2019, 21, 416. [CrossRef]73. Cook, D.; Schmitter-Edgecombe, M. Assessing the quality of activities in a smart environment. Methods Inf. Med. 2009, 48, 480–485.74. Singla,G.; Cook,D.; Schmitter-Edgecombe,M.Trackingactivitiesincomplexsettingsusingsmartenvironment technologies. Int. J. BioSci. Psychiatry Technol. 2009, 1, 25–35.75. Cook, D.J.; Youngblood, M.; Das, S.K. A multi-agent approach to controlling a smart environment. In Designing Smart Homes; Springer: Berlin/Heidelberg, Germany, 2006; pp. 165–182.76. Dernbach, S.; Das, B.; Krishnan, N.C.; Thomas, B.L.; Cook, D.J. Simple and complex activity recognition through smart phones. In Proceedings of the 2012 Eighth International Conference on Intelligent Environments, Guanajuato, Mexico, 26–29 June 2012.77. Sahaf, Y. Comparing Sensor Modalities for Activity Recognition. Master’s Thesis, Washington State University, Pullman, WA, USA, August 2011.78. Rawassizadeh, R.; Keshavarz, H.; Pazzani, M. Ghost imputation: Accurately reconstructing missing data of the off period. IEEE Trans. Knowl. Data Eng. 2019. [CrossRef]79. Wilson, D.H. Assistive Intelligent Environments for Automatic Health Monitoring. Ph.D. Thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA, September 2005.80. Singla, G.; Cook, D.J.; Schmitter-Edgecombe, M. Incorporating temporal reasoning into activity recognition for smart home residents. In Proceedings of the AAAI Workshop on Spatial and Temporal Reasoning, Chicago, IL, USA, 13 July 2008; pp. 53–61.81. Wren, C.R.; Tapia, E.M. Hierarchical Processing in Scalable and Portable Sensor Networks for Activity Recognition. U.S. Patent No. 7359836, 15 April 2008.82. Van Kasteren, T.; Noulas, A.; Englebienne, G.; Kröse, B. Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea, 21–24 September 2008; pp. 1–9.83. Philipose, M.; Fishkin, K.P.; Perkowitz, M.; Patterson, D.J.; Fox, D.; Kautz, H.; Hahnel, D. Inferring activities from interactions with objects. IEEE Pervasive Comput. 2004, 3, 50–57. [CrossRef]84. Patterson, D.J.; Fox, D.; Kautz, H.; Philipose, M. Fine-grained activity recognition by aggregating abstract object usage. In Proceedings of the Ninth IEEE International Symposium on Wearable Computers (ISWC’05), Osaka, Japan, 18–21 October 2005; pp. 44–51.85. Hodges, M.R.; Newman, M.W.; Pollack, M.E. Object-Use Activity Monitoring: Feasibility for People with Cognitive Impairments. In Proceedings of the AAAI Spring Symposium: Human Behavior Modeling, Stanford, CA, USA, 23–25 March 2009; pp. 13–18.86. Fang, F.; Aabith, S.; Homer-Vanniasinkam, S.; Tiwari, M.K. High-resolution 3D printing for healthcare underpinned by small-scale fluidics. In 3D Printing in Medicine; Woodhead Publishing: Cambrigde, MA, USA, 2017; pp. 167–206.87. Veltink, P.H.; Bussmann, H.J.; De Vries, W.; Martens, W.J.; Van Lummel, R.C. Detection of static and dynamic activities using uniaxial accelerometers. IEEE Trans. Rehab. Eng. 1996, 4, 375–385. [CrossRef] [PubMed]88. Mathie, M.J.; Coster, A.C.F.; Lovell, N.H.; Celler, B.G. Detection of daily physical activities using a triaxial accelerometer. Med. Biol. Eng. Comput. 2003, 41, 296–301. [CrossRef] [PubMed]89. Bao, L.; Intille, S.S. Activity recognition from user-annotated acceleration data. In Proceedings of the International Conference on Pervasive Computing, Linz/Vienna, Austria, 21–23 April 2004; Springer: Berlin/Heidelberg, Germany, 2004; pp. 1–17.90. Chambers, G.S.; Venkatesh, S.; West, G.A.; Bui, H.H. Hierarchical recognition of intentional human gestures for sports video annotation. In Object Recognition Supported by User Interaction for Service Robots; IEEE: Quebec, PQ, Canada, 2002; pp. 1082–1085.91. Lester, J.; Choudhury, T.; Borriello, G. A practical approach to recognizing physical activities. In Proceedings of the 4th International Conference on Pervasive Computing, Dublin, Ireland, 7–10 May 2006; Springer: Berlin/Heidelberg, Germany, 2006; pp. 1–16.92. Mantyjarvi, J.; Himberg, J.; Seppanen, T. Recognizing human motion with multiple acceleration sensors. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Tucson, AZ, USA, 7–10 October 2001; pp. 747–752.93. Al-Ani, T.; Le Ba, Q.T.; Monacelli, E. On-line automatic detection of human activity in home using wavelet and hidden markov models scilab toolkits. In Proceedings of the 2007 IEEE International Conference on Control Applications, Singapore, 1–3 October 2007; pp. 485–490.94. Zheng,Y.; Liu,Q.; Chen,E.; Ge,Y.; Zhao,J.L.Timeseriesclassificationusingmulti-channelsdeepconvolutional neural networks. In Proceedings of the International Conference on Web-Age Information Management, Macau, China, 16–18 June 2014; Springer: Cham, Switzerland, 2014; pp. 298–310.95. Jiang, W.; Yin, Z. Human activity recognition using wearable sensors by deep convolutional neural networks. In Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia, 26–30 October 2015; pp. 1307–1310.96. Alsheikh, M.A.; Selim, A.; Niyato, D.; Doyle, L.; Lin, S.; Tan, H.P. Deep activity recognition models with triaxial accelerometers. In Proceedings of the Workshops at the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 12–13 February 2016.97. Hu, D.H.; Yang, Q. CIGAR: Concurrent and Interleaving Goal and Activity Recognition. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, Chicago, IL, USA, 13–17 July 2008; Volume 8, pp. 1363–1368.98. Zhang, L.; Wu, X.; Luo, D. Recognizing human activities from raw accelerometer data using deep neural networks. In Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 9–11 December 2015; pp. 865–870.99. Using the Multicom Domus Dataset 2011. Available online: https://hal.archives-ouvertes.fr/hal-01473142/ (accessed on 25 January 2020).100. Zhang, S.; McCullagh, P.; Nugent, C.; Zheng, H. Activity monitoring using a smart phone’s accelerometer with hierarchical classification. In Proceedings of the 2010 Sixth International Conference on Intelligent Environments, Kuala Lumpur, Malaysia, 19–21 July 2010; pp. 158–163.101. Espinilla, M.; Martínez, L.; Medina, J.; Nugent, C. The experience of developing the UJAmI Smart lab. IEEE Access 2018, 6, 34631–34642. [CrossRef]102. Kitchenham, B.; Brereton, O.P.; Budgen, D.; Turner, M.; Bailey, J.; Linkman, S. Systematic literature reviews in software engineering—A systematic literature review. Inf. Softw. Technol. 2009, 51, 7–15. [CrossRef]103. Fahad, L.G.; Ali, A.; Rajarajan, M. Learning models for activity recognition in smart homes. In Information Science and Applications; Springer: Berlin/Heidelberg, Germany, 2015; pp. 819–826.104. Nguyen, D.; Le, T.; Nguyen, S. An Algorithmic Method of Calculating Neighborhood Radius for Clustering In-home Activities within Smart Home Environment. In Proceedings of the 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), Bangkok, Thailand, 25–27 January 2016; pp. 42–47.105. Nguyen, D.; Le, T.; Nguyen, S. A Novel Approach to Clustering Activities within Sensor Smart Homes. Int. J. Simul. Syst. Sci. Technol. 2016, 17. [CrossRef]106. Sukor, A.S.A.; Zakaria, A.; Rahim, N.A.; Setchi, R. Semantic knowledge base in support of activity recognition in smart home environments. Int. J. Eng. Technol. 2018, 7, 67–72. [CrossRef]107. Jänicke, M.; Sick, B.; Tomforde, S. Self-adaptive multi-sensor activity recognition systems based on gaussian mixture models. Informatics 2018, 5, 38. [CrossRef]108. Honarvar, A.R.; Zaree, T. Frequent sequence pattern based activity recognition in smart environment. Intell. Decis. Technol. 2018, 12, 349–357. [CrossRef]109. Chen, W.H.; Chen, Y. An ensemble approach to activity recognition based on binary sensor readings. In Proceedings of the 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, China, 12–15 October 2017; pp. 1–5.110. Khan, M.A.A.H.; Roy, N. Transact: Transfer learning enabled activity recognition. In Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, HI, USA, 13–17 March 2017; pp. 545–550.111. Fahad, L.G.; Tahir, S.F.; Rajarajan, M. Feature selection and data balancing for activity recognition in smart homes. In Proceedings of the 2015 IEEE International Conference on Communications (ICC), London, UK, 8–12 June 2015; pp. 512–517.112. Fahad, L.G.; Khan, A.; Rajarajan, M. Activity recognition in smart homes with self verification of assignments. Neurocomputing 2015, 149, 1286–1298. [CrossRef]113. Bota, P.; Silva, J.; Folgado, D.; Gamboa, H. A Semi-Automatic Annotation Approach for Human Activity Recognition. Sensors 2019, 19, 501. [CrossRef] [PubMed]114. Zhang, S.; Ng, W.W.; Zhang, J.; Nugent, C.D.; Irvine, N.; Wang, T. Evaluation of radial basis function neural network minimizing L-GEM for sensor-based activity recognition. J. Ambient Intell. Hum. Comput. 2019. [CrossRef]115. Wen, J.; Zhong, M. Activity discovering and modelling with labelled and unlabelled data in smart environments. Expert Syst. Appl. 2015, 42, 5800–5810. [CrossRef]116. Fahad, L.G.; Rajarajan, M. Integration of discriminative and generative models for activity recognition in smart homes. Appl. Soft Comput. 2015, 37, 992–1001. [CrossRef]117. Ihianle, I.; Naeem, U.; Islam, S.; Tawil, A.R. A hybrid approach to recognising activities of daily living from object use in the home environment. Informatics 2018, 5, 6. [CrossRef]118. Chua, S.L.; Foo, L.K. Sensor selection in smart homes. Procedia Comput. Sci. 2015, 69, 116–124. [CrossRef]119. Shahi Soozaei, A. Human Activity Recognition in Smart Homes. Ph.D. Thesis, University of Otago, Dunedin, New Zealand, January 2019.120. Caldas, T.V. From Binary to Multi-Class Divisions: Improvements on Hierarchical Divisive Human Activity Recognition. Master’s Thesis, Universidade do Porto, Oporto, Portugal, July 2019.121. Fang, L.; Ye, J.; Dobson, S. Discovery and recognition of emerging human activities using a hierarchical mixture of directional statistical models. IEEE Trans. Knowl. Data Eng. 2019. [CrossRef]122. Guo, J.; Li, Y.; Hou, M.; Han, S.; Ren, J. Recognition of Daily Activities of Two Residents in a Smart Home Based on Time Clustering. Sensors 2020, 20, 1457. [CrossRef]123. Kavitha, R.; Binu, S. Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living. Procedia Comput. Sci. 2019, 165, 314–321. [CrossRef]124. Akter, S.S. Improving Sensor Network Predictions through the Identification of Graphical Features. Ph.D. Thesis, Washington State University, Washington, DC, USA, August 2019.125. Oukrich, N. Daily Human Activity Recognition in Smart Home based on Feature Selection, Neural Network and Load Signature of Appliances. Ph.D. Thesis, Mohammed V University In Rabat, Rabat, Morocco, April 2019.126. Yala, N. Contribution aux Méthodes de Classification de Signaux de Capteurs dans un Habitat Intelligent. Ph.D. Thesis, The University of Science and Technology—Houari Boumediene, Bab-Ezzouar, Algeria, October 2019.127. Lyu, F.; Fang, L.; Xue, G.; Xue, H.; Li, M. Large-Scale Full WiFi Coverage: Deployment and Management Strategy Based on User Spatio-Temporal Association Analytics. IEEE Internet Things J. 2019, 6, 9386–9398. [CrossRef]128. Chetty, G.; White, M. Body sensor networks for human activity recognition. In Proceedings of the 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 11–12 February 2016; pp. 660–665.129. Singh, T.; Vishwakarma, D.K. Video benchmarks of human action datasets: A review. Artif. Intell. Rev. 2019, 52, 1107–1154. [CrossRef]130. Senda, M.; Ha, D.; Watanabe, H.; Katagiri, S.; Ohsaki, M. Maximum Bayes Boundary-Ness Training for Pattern Classification. In Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning, Hangzhou, China, 27–29 November 2019; pp. 18–28.131. Petrovich, M.; Yamada, M. Fast local linear regression with anchor regularization. arXiv 2020, arXiv:2003.05747.132. Yadav, A.; Kumar, E. A Literature Survey on Cyber Security Intrusion Detection Based on Classification Methods of Supervised Machine Learning; Bloomsbury: New Delhi, India, 2019.133. Marimuthu, P.; Perumal, V.; Vijayakumar, V. OAFPM: Optimized ANFIS using frequent pattern mining for activity recognition. J. Supercomput. 2019, 75, 5347–5366. [CrossRef]134. Raeiszadeh, M.; Tahayori, H.; Visconti, A. Discovering varying patterns of Normal and interleaved ADLs in smart homes. Appl. Intell. 2019, 49, 4175–4188. [CrossRef]135. Hossain, H.S.; Roy, N. Active Deep Learning for Activity Recognition with Context Aware Annotator Selection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 4–8 August 2019; pp. 1862–1870.136. Kong, D.; Bao, Y.; Chen, W. Collaborative learning based on centroid-distance-vector for wearable devices. Knowl.-Based Syst. 2020. [CrossRef]137. Lentzas, A.; Vrakas, D. Non-intrusive human activity recognition and abnormal behavior detection on elderly people: A review. Artif. Intell. Rev. 2020, 53, 1975–2021. [CrossRef]138. Arya, M. Automated Detection of Acute Leukemia Using K-Means Clustering Algorithm. Master’s Thesis, North Dakota State University, Fargo, ND, USA, May 2019.139. Chetty, G.; Yamin, M. Intelligent human activity recognition scheme for eHealth applications. Malays. J. Comput. Sci. 2015, 28, 59–69.140. Soulas, J.; Lenca, P.; Thépaut, A. Unsupervised discovery of activities of daily living characterized by their periodicity and variability. Eng. Appl. Artif. Intell. 2015, 45, 90–102. [CrossRef]141. Rojlertjanya, P. Customer Segmentation Based on the RFM Analysis Model Using K-Means Clustering Technique: A Case of IT Solution and Service Provider in Thailand. Master’s Thesis, Bangkok University, Bangkok, Thailand, 16 August 2019.142. Zhao, B.; Shao, B. Analysis the Consumption Behavior Based on Weekly Load Correlation and K-means Clustering Algorithm. In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, Cairo, Egypt, 26–28 October 2019; Springer: Cham, Switzerland, 2019; pp. 70–81.143. Zahi,S.; Achchab,B.ClusteringofthepopulationbenefitingfromhealthinsuranceusingK-means. InProceedings of the 4th International Conference on Smart City Applications, Casablanca, Morocco, 2–4 October 2019; pp. 1–6.144. Dana, R.D.; Dikananda, A.R.; Sudrajat, D.; Wanto, A.; Fasya, F. Measurement of health service performance through machine learning using clustering techniques. J. Phys. Conf. Ser. 2019, 1360, 012017. [CrossRef]145. Baek, J.W.; Kim, J.C.; Chun, J.; Chung, K. Hybrid clustering based health decision-making for improving dietary habits. Technol. Health Care 2019, 27, 459–472. [CrossRef] [PubMed]146. Rashid, J.; Shah, A.; Muhammad, S.; Irtaza, A. A novel fuzzy k-means latent semantic analysis (FKLSA) approach for topic modeling over medical and health text corpora. J. Intell. Fuzzy Syst. 2019, 37, 6573–6588. [CrossRef]147. Lütz, E. Unsupervised Machine Learning to Detect Patient Subgroups in Electronic Health Records. Master’s Thesis, KTH Royal Institute of Technology, Stockholm, Sweden, January 2019.148. Maturo, F.; Ferguson, J.; Di Battista, T.; Ventre, V. A fuzzy functional k-means approach for monitoring Italian regions according to health evolution over time. Soft Comput. 2019. [CrossRef]149. Wang, S.; Li, M.; Hu, N.; Zhu, E.; Hu, J.; Liu, X.; Yin, J. K-means clustering with incomplete data. IEEE Access 2019, 7, 69162–69171. [CrossRef]150. Long, J.; Sun, W.; Yang, Z.; & Raymond, O.I. Asymmetric Residual Neural Network for Accurate Human Activity Recognition. Information 2019, 10, 203. [CrossRef]151. Yuan, C.; Yang, H. Research on K-value selection method of K-means clustering algorithm. J. Multidiscip. Sci. J. 2019, 2, 226–235. [CrossRef]152. Wang, P.; Shi, H.; Yang, X.; Mi, J. Three-way k-means: Integrating k-means and three-way decision. Int. J. Mach. Learn. Cybern. 2019, 10, 2767–2777. [CrossRef]153. Sadeq, S.; Yetkin, G. Semi-Supervised Sparse Data Clustering Performance Investigation. In Proceedings of the International Conference on Data Science, MachineLearning and Statistics, Van, Turkey, 26–29 June 2019; p. 463.154. Boddana, S.; Talla, H. Performance Examination of Hard Clustering Algorithm with Distance Metrics. Int. J. Innov. Technol. Explor. Eng. 2019, 9. [CrossRef]155. Xiao, Y.; Chang, Z.; Liu, B. An efficient active learning method for multi-task learning. Knowl. Based Syst. 2020, 190, 105137. [CrossRef]156. Yao, L.; Nie, F.; Sheng, Q.Z.; Gu, T.; Li, X.; Wang, S. Learning from less for better: Semi-supervised activity recognition via shared structure discovery. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 12–16 September 2016; pp. 13–24.157. Tax, N.; Sidorova, N.; van der Aalst, W.M. Discovering more precise process models from event logs by filtering out chaotic activities. J. Intell. Inf. Syst. 2019, 52, 107–139. [CrossRef]158. Artavanis-Tsakonas, K.; Karpiyevich, M.; Adjalley, S.; Mol, M.; Ascher, D.; Mason, B.; van der Heden van Noort, G.; Laman, H.; Ovaa, H.; Lee, M. Nedd8 hydrolysis by UCH proteases in Plasmodium parasites. PLoS Pathog. 2019, 15, e1008086.159. Koole, G. An Introduction to Business Analytics; MG Books: Amsterdam, The Netherlands, 2019.160. Oh, H.; Jain, R. Detecting Events of Daily Living Using Multimodal Data. arXiv 2019, arXiv:1905.09402.161. Caleb-Solly, P.; Gupta, P.; McClatchey, R. Tracking changes in user activity from unlabelled smart home sensor data using unsupervised learning methods. Neural Comput. Appl. 2020. [CrossRef]162. Patel, A.; Shah, J. Sensor-based activity recognition in the context of ambient assisted living systems: A review. J. Ambient Intell. Smart Environ. 2019, 11, 301–322. [CrossRef]163. Leotta, F.; Mecella, M.; Sora, D. Visual process maps: A visualization tool for discovering habits in smart homes. J. Ambient Intell. Hum. Comput. 2019. [CrossRef]164. Ferilli, S.; Angelastro, S. Activity prediction in process mining using the WoMan framework. J. Intell. Inf. Syst. 2019, 53, 93–112. [CrossRef]165. Wong, W. Combination Clustering: Evidence Accumulation Clustering for Dubious Feature Sets. OSF Prepr. 2019. [CrossRef]166. Wong, W.; Tsuchiya, N. Evidence Accumulation Clustering Using Combinations of Features; Center for Open Science: Victoria, Australia, 2019.167. Zhao, W.; Li, P.; Zhu, C.; Liu, D.; Liu, X. Defense Against Poisoning Attack via Evaluating Training Samples Using Multiple Spectral Clustering Aggregation Method. CMC-Comput. Mater. Cont. 2019, 59, 817–832. [CrossRef]168. Yang, Y.; Zheng, K.; Wu, C.; Niu, X.; Yang, Y. Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks. Appl. Sci. 2019, 9, 238. [CrossRef]169. Cuzzocrea, A.; Gaber, M.M.; Fadda, E.; Grasso, G.M. An innovative framework for supporting big atmospheric data analytics via clustering-based spatio-temporal analysis. J. Ambient Intell. Hum. Comput. 2019, 10, 3383–3398. [CrossRef]Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2sensorshttps://www.mdpi.com/1424-8220/20/9/2702ambient assisted living—AALhuman activity recognition—HARactivities of dailyliving—ADLctivity recognition systems—ARSclusteringunsupervised activity recognitionUnsupervised Human Activity Recognition Using the Clustering Approach: A ReviewArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALUnsupervised Human Activity Recognition Using the Clustering Approach A Review.pdfUnsupervised Human Activity Recognition Using the Clustering Approach A Review.pdfapplication/pdf4636410https://repositorio.cuc.edu.co/bitstreams/de46b136-b0b5-4160-b38f-bb1380662ec2/downloadcf4d0c64f513994ca9dd28d10e031fc9MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/63bb4b83-7b52-4eb9-8c71-b2bf4b23b9d4/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/2d99197b-6d2e-46c0-bac3-459f5aa7a534/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILUnsupervised Human Activity Recognition Using the Clustering Approach A Review.pdf.jpgUnsupervised Human Activity Recognition Using the Clustering Approach A Review.pdf.jpgimage/jpeg64491https://repositorio.cuc.edu.co/bitstreams/a0b5032b-6745-4f64-8ed1-5c13779e5a92/download36211ab3e5a80f28dff15147880d9734MD54THUMBNAILUnsupervised Human Activity Recognition Using the Clustering Approach A Review.pdf.jpgUnsupervised Human Activity Recognition Using the Clustering Approach A Review.pdf.jpgimage/jpeg64491https://repositorio.cuc.edu.co/bitstreams/3c0487ae-f081-4d4b-8db1-0f95aae7be97/download36211ab3e5a80f28dff15147880d9734MD54TEXTUnsupervised Human Activity Recognition Using the Clustering Approach A Review.pdf.txtUnsupervised Human Activity Recognition Using the Clustering Approach A Review.pdf.txttext/plain106135https://repositorio.cuc.edu.co/bitstreams/2d39f465-cf62-4703-a615-2cce17fcf18d/downloadab5e4717fb4942903217b2f762767b72MD5511323/7356oai:repositorio.cuc.edu.co:11323/73562024-09-17 14:21:23.242http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares