Abstract
New healthcare technologies are emerging with the increasing age of the society, where the development of smart homes for monitoring the elders’ activities is in the center of them. Identifying the resident’s activities in an apartment is an important module in such systems. Dense sensing approach aims to embed sensors in the environment to report the detected events continuously. The events are segmented and analyzed via classifiers to identify the corresponding activity. Although several methods were introduced in recent years for detecting simple activities, the recognition of complex ones requires more effort. Due to the different time duration and event density of each activity, finding the best size of the segments is one of the challenges in detecting the activity. Also, using appropriate classifiers that are capable of detecting simple and interleaved activities is the other issue. In this paper, we devised a two-phase approach called CARER (Complex Activity Recognition using Emerging patterns and Random forest). In the first phase, the emerging patterns are mined, and various features of the activities are extracted to build a model using the Random Forest technique. In the second phase, the sequences of events are segmented dynamically by considering their recency and sensor correlation. Then, the segments are analyzed by the generated model from the previous phase to recognize both simple and complex activities. We examined the performance of the devised approach using the CASAS dataset. To do this, first we investigated several classifiers. The outcome showed that the combination of emerging patterns and the random forest provide a higher degree of accuracy. Then, we compared CARER with the static window approach, which used Hidden Markov Model. To have a fair comparison, we replaced the dynamic segmentation module of CARER with the static one. The results showed more than 12% improvement in f-measure. Finally, we compared our work with Dynamic sensor segmentation for real-time activity recognition, which used dynamic segmentation. The f-measure metric demonstrated up to 12.73% improvement.
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References
McNicoll G (2002) World population ageing 1950–2050. Popul Dev Rev 28(4):814–816
Zilm F-A, Scharnweber C, Haux R (2014) Knowledge representations in ambient assisted living-context awareness in smart homes. In: MIE, pp 308–312
Krishnan N C, Cook D J (2014) Activity recognition on streaming sensor data. Pervasive Mob Comput 10:138–154
Cook DJ, Krishnan NC (2015) Activity learning: discovering, recognizing, and predicting human behavior from sensor data. Wiley
Chen L, Hoey J, Nugent CD, Cook DJ, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):790–808
Queirós A, Silva A, Alvarelhão J, Rocha NP, Teixeira A (2015) Usability, accessibility and ambient-assisted living: a systematic literature review. Univ Access Inf Soc 14(1):57–66
Hu Y, Tilke D, Adams T, Crandall AS, Cook DJ, Schmitter-Edgecombe M (2016) Smart home in a box: usability study for a large scale self-installation of smart home technologies. Journal of Reliable Intelligent Environments 2(2):93–106
Pantelopoulos A, Bourbakis N G (2010) A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern Part C Appl Rev 40(1):1–12
Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutorials 15(3):1192–1209
Mubashir M, Shao L, Seed L (2013) A survey on fall detection: principles and approaches. Neurocomputing 100:144–152
Jalal A, Uddin MZ, Kim T-S (2012) Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home. IEEE Trans Consum Electron 58(3):863–871
Song Y, Tang J, Liu F, Yan S (2014) Body surface context: a new robust feature for action recognition from depth videos. IEEE Trans Circuits Syst Video Technol 24(6):952–964
Chen C, Jafari R, Kehtarnavaz N (2015) Improving human action recognition using fusion of depth camera and inertial sensors. IEEE Transactions on Human-Machine Systems 45(1):51– 61
Onofri L, Soda P, Pechenizkiy M, Iannello G (2016) A survey on using domain and contextual knowledge for human activity recognition in video streams. Expert Syst Appl 63:97–111
Sprint G, Cook DJ, Weeks DL (2016) Designing wearable sensor-based analytics for quantitative mobility assessment. In: 2016 IEEE international conference on smart computing (SMARTCOMP). IEEE, pp 1–8
Kern N, Schiele B, Junker H, Lukowicz P, Tröster G (2003) Wearable sensing to annotate meeting recordings. Pers Ubiquit Comput 7(5):263–274
Sprint G, Cook D, Fritz R, Schmitter-Edgecombe M (2016) Detecting health and behavior change by analyzing smart home sensor data. In: 2016 IEEE international conference on smart computing (SMARTCOMP). IEEE, pp 1–3
Thomas BL, Crandall AS, Cook DJ (2016) A genetic algorithm approach to motion sensor placement in smart environments. Journal of Reliable Intelligent Environments 2(1):3–16
Benmansour A, Bouchachia A, Feham M (2016) Multioccupant activity recognition in pervasive smart home environments. ACM Comput Surv (CSUR) 48(3):34
Baig MM, Gholamhosseini H, Connolly MJ (2013) A comprehensive survey of wearable and wireless ecg monitoring systems for older adults. Med Biol Eng Comput 51(5):485–495
Sardini E, Serpelloni M (2014) T-shirt for vital parameter monitoring. In: Sensors. Springer, pp 201–205
Singla G, Cook DJ (2009) Interleaved activity recognition for smart home residents. Intelligent Environments 9:145–152
Liu L, Peng Y, Liu M, Huang Z (2015) Sensor-based human activity recognition system with a multilayered model using time series shapelets. Knowl-Based Syst 90:138–152
Wan J, O’Grady MJ, O’Hare GMP (2015) Dynamic sensor event segmentation for real-time activity recognition in a smart home context. Pers Ubiquit Comput 19(2):287–301
Abin AA (2016) Clustering with side information: further efforts to improve efficiency. Pattern Recogn Lett 84:252–258
Gu T, Wu Z, Tao X, Pung HK, Lu J (2009) epsicar: an emerging patterns based approach to sequential, interleaved and concurrent activity recognition. In: IEEE international conference on pervasive computing and communications, 2009. PerCom 2009. IEEE, pp 1–9
Crandall A, Cook DJ (2010) Learning activity models for multiple agents in a smart space. In: Handbook of ambient intelligence and smart environments. Springer, pp 751–769
Alemdar H, Ertan H, Incel OD, Ersoy C (2013) Aras human activity datasets in multiple homes with multiple residents. In: 2013 7th international conference on pervasive computing technologies for healthcare and workshops. IEEE, pp 232–235
Chen R, Tong Y (2014) A two-stage method for solving multi-resident activity recognition in smart environments. Entropy 16(4):2184–2203
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Wang L, Gu T, Tao X, Lu J (2012) A hierarchical approach to real-time activity recognition in body sensor networks. Pervasive Mob Comput 8(1):115–130
De Maio C, Fenza G, Furno D, Loia V (2012) Swarm-based semantic fuzzy reasoning for situation awareness computing. In: 2012 IEEE international conference on fuzzy systems, pp 1–7
De Maio C, Fenza G, Loia V, Orciuoli F (2017) Unfolding social content evolution along time and semantics. Futur Gener Comput Syst 66:146–159
Mabroukeh NR, Ezeife CI (2010) A taxonomy of sequential pattern mining algorithms. ACM Comput Surv (CSUR) 43(1):3
Dong G, Bailey J (2012) Contrast data mining: concepts, algorithms, and applications. CRC Press
Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier
Gu T, Wang L, Chen H, Liu G, Tao X, Lu J (2010) Mining emerging sequential patterns for activity recognition in body sensor networks. In: International conference on mobile and ubiquitous systems: computing, networking, and services. Springer, pp 102–113
Dong G, Li J (1999) Efficient mining of emerging patterns: Discovering trends and differences. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 43–52
Fournier-Viger P, Gomariz A, Campos M, Thomas R (2014) Fast vertical mining of sequential patterns using co-occurrence information. In: Pacific-asia conference on knowledge discovery and data mining. Springer, pp 40–52
Wemlinger Z (2012) Smart home activities. http://ailab.wsu.edu/casas/tools/adviz/
Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer series in statistics, vol 1. Springer, Berlin
Cook D, Schmitter-Edgecombe M, Crandall A, Sanders C, Thomas B (2009) Collecting and disseminating smart home sensor data in the casas project. In: Proceedings of the CHI workshop on developing shared home behavior datasets to advance HCI and ubiquitous computing research, pp 1–7
Ye J, Stevenson G, Dobson S (2015) Kcar: a knowledge-driven approach for concurrent activity recognition. Pervasive Mob Comput 19:47–70
Washington state university casas datasets, http://ailab.wsu.edu/casas/datasets.html
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Tabatabaee Malazi, H., Davari, M. Combining emerging patterns with random forest for complex activity recognition in smart homes. Appl Intell 48, 315–330 (2018). https://doi.org/10.1007/s10489-017-0976-2
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DOI: https://doi.org/10.1007/s10489-017-0976-2