Abstract
In the case of a smart home, the ability to recognize daily activities depends primarily on the strategy used for selecting the appropriate features related to these activities. To achieve the goal, this paper presents a daily activity feature selection strategy based on the Pearson Correlation Coefficient. Firstly, a daily activity feature is viewed as a vector in Pearson Correlation Coefficient formula. Secondly, the relation degree between daily activity features is obtained according to weighted Pearson Correlation Coefficient formula. At last, redundant features are removed by the relation degree between daily activity features. Two distinct datasets are adopted to mitigate the effects of the coupling of the dataset used and the sensor configuration. Three different machine learning techniques are employed to evaluate the performance of the proposed approach in activity recognition. The experiment results show that the proposed approach yields higher recognition rates and achieves average improvement F-measures of 1.56% and 2.7%, respectively.
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References
Chan M, Campo E, Estève D, Fourniols JY (2008) A review of smart homes- present state and future challenges. Comput Methods Programs Biomed 91(1):55–81
Ruyter BD, Zwartkruispelgrim E, Aarts E (2010) Ambient assisted living research in the carelab. Interactions 14(4):30–33
Machot FA, Mosa AH, Ali M, Kyamakya K (2018) Activity recognition in sensor data streams for active and assisted living environments. IEEE Trans Circuits Syst Video Technol 28(10):2933
Feuz KD, Cook DJ (2017) Collegial activity learning between heterogeneous sensors. Knowl Inf Syst 53(2):337–364
Yala N, Fergani B, Fleury A (2017) Towards improving feature extraction and classification for activity recognition on streaming data. J Ambient Intell Humaniz Comput 8(2):177–189
Guo SK, Liu YQ, Chen R, Sun X, Wang XX (2019) Improved SMOTE algorithm to deal with imbalanced activity classes in smart homes. Neural Process Lett 50(2):1503–1526
Liu YQ, Yi XK, Chen R, Zhai ZG, Gu JX (2018) Feature extraction based on information gain and sequential pattern for english question classification. IET Softw 12(6):520–526
Liu YQ, Wang XX, Zhai ZG, Chen R, Zhang B, Jiang Y (2019) Timely daily activity recognition from headmost sensor events. ISA Trans 94:379–390
Guo SK, Chen R, Wei MM, Li H, Liu YQ (2018) Ensemble data reduction techniques and multi-RSMOTE via fuzzy integral for bug report classification. IEEE Access 6:45934–45950
Deng W, Xu J, Zhao H (2019) An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2897580
Chen R, Guo SK, Wang XZ, Zhang TL (2019) fusion of multi-RSMOTE with fuzzy integral to classify bug reports with an imbalanced severity distribution. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2019.2899809
Deng W, Zhao H, Yang X, Xiong J, Meng S, Bo L (2017) Study on an improved adaptive pso algorithm for solving multi-objective gate assignment. Appl Soft Comput 59:288–302
Guo SK, Chen R, Li H, Zhang TL, Liu YQ (2019) Identify severity bug report with distribution imbalance by CR-SMOTE and ELM. Int J Softw Eng Knowl Eng 29(2):139–175
Deng W, Zhao H, Li Z, Li G, Yang X, Wu D (2017) A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput 21(15):4387–4398
Zhao H, Zheng J, Xu J, Deng W (2019) Fault diagnosis method based on principal component analysis and broad learning system. IEEE Access. https://doi.org/10.1109/access.2019.2929094
Li H, Gao GF, Chen R, Ge X, Guo SK, Hao LY (2019) The influence ranking for testers in bug tracking systems. Int J Softw Knowl Eng 29(1):93–113
Zhao H, Yao R, Xu L, Yuan Y, Li G, Deng W (2018) Study on a novel fault damage degree identification method using high-order differential mathematical morphology gradient spectrum entropy. Entropy 20(9):L682
Yang C, Liu H, Mcloone S, Chen CL, Wu X (2018) A novel variable precision reduction approach to comprehensive knowledge systems. IEEE Trans Cybern 48(2):661–674
Chen L, Nugent CD, Wang H (2012) A knowledge-driven approach to activity recognition in smart homes. IEEE Trans Knowl Data Eng 24(6):961–974
Latfi F, Lefebvre B, Descheneaux C (2007) Ontology-based management of the telehealth smart home, dedicated to elderly in loss of cognitive autonomy. In: CEUR workshop proceeding, June 2007
Salguero AG, Espinilla M (2018) Ontology-based feature generation to improve accuracy of activity recognition in smart environments. Comput Electr Eng 68:1–13
Gayathri KS, Easwarakumar KS, Elias S (2017) Probabilistic ontology based activity recognition in smart homes using markov logic network. Knowl Based Syst 121:173–184
Rodrguez ND, Cullar MP, Lilius J, Calvo-Flores MD (2014) A fuzzy ontology for semantic modelling and recognition of human behaviour. Knowl Based Syst 66:46–60
Safyan M, Qayyum ZU, Sarwar S, Garcia-Castro R, Ahmed M (2019) Ontology-driven semantic unified modelling for concurrent activity recognition. Multimed Tool Appl 78(2):2073–2104
Chiang YT, Lu CH, Hsu YJ (2017) A feature-based knowledge transfer framework for cross-environment activity recognition toward smart home applications. IEEE Trans Hum Mach Syst 47(3):310–322
Meditskos G, Kompatsiaris I (2017) iknow: Ontology-driven situational awareness for the recognition of activities of daily living. Pervasive Mob Comput 40:17–41
Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: 2nd international conference on pervasive computing, Linz and Vienna, Austria, April 2004
Oliver B, Crowley JL, Patrick R (2009) Learning situation models in a smart home. IEEE Trans Syst Man Cybern Part B Cybern 39(1):56
Tapia EM, Intille SS, Haskell W, Larson K, Wright JA, King A, Friedman RH (2007) Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: IEEE international symposium on wearable computers, Boston, MA, USA, October 2007
Patterson DJ, Fox D, Kautz H, Kautz H (2005) Fine-grained activity recognition by aggregating abstract object usage. In: Ninth IEEE international symposium on wearable computers, Osaka, Japan
Lu L, Cai QL, Zhan YJ (2017) Activity recognition in smart homes. Multimed Tools Appl 76(22):24203–24220
Kasteren TLMV, Englebienne G, Krse BJA (2011) Hierarchical activity recognition using automatically clustered actions. In: 2nd international joint conference on ambient intelligence, The Netherlands, November, 2011
Vail DL, Veloso MM, Lafferty JD (2007) Conditional random fields for activity recognition. In: International joint conference on autonomous agents & multiagent systems, May 2007
Fahad LG, Khan A, Rajarajan M (2015) Activity recognition in smart homes with self verification of assignments. Neurocomputing 149:1286–1298
Bourobou ST, Yoo Y (2015) User activity recognition in smart homes using pattern clustering applied to temporal ann algorithm. Sensors 15(5):11953–11971
Fang H, Lei H (2012) BP neural network for human activity recognition in smart home. In: International conference on computer science & service system, August 2012
Chen G, Wang A, Zhao S, Liu L, Chang CY (2018) Latent feature learning for activity recognition using simple sensors in smart homes. Multimed Tools Appl 77(12):15201–15219
Hassan MM, Huda S, Uddin MZ, Almogren A, Alrubaian M (2018) Human activity recognition from body sensor data using deep learning. J Med Syst 42(6):99
Yu G, Thomas P (2017) Ensembles of deep LSTM learners for activity recognition using wearables. In: Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies
Chen WH, Baca CAB, Tou CH (2017) Lstm-rnns combined with scene information for human activity recognition. In: IEEE international conference on E-health networking, Dalian, China, October 2017
Krishnan NC, Cook DJ (2014) Activity recognition on streaming sensor data. Pervasive Mob Comput 10(Pt B):138–154
WSU CASAS Datasets http://ailab.wsu.edu/casas/datasets.html. Accessed 2 Feb 2016
Weka 3.8. https://sourceforge.net/projects/weka/. Accessed 29 Apr 2016
Sprint G, Cook DJ, Fritz RS, Schmitter-Edgecombe M (2016) Using smart homes to detect and analyze health events. Computer 49(11):29–37
Dawidi PN, Cook DJ, Schmitter-Edgecombe M (2016) Automated clinical assessment from smart home-based behavior data. IEEE J Biomed Health Inform 20(4):1188–1194
Thomas BL, Cook DJ (2016) Activity-aware energy-efficient automation of smart buildings. Energies 9:624
Acknowledgements
We thank all the reviewers for their useful comments for improving the manuscript. This work was supported by the National Natural Science Foundation of China (No. 61976124); the Fundamental Research Funds for the Central Universities (No. 3132018194); the Open Project Program of Artificial Intelligence Key Laboratory of Sichuan Province (No. 2018RYJ09); the CERNET Innovation Project (No. NGII20181203).
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Liu, Y., Mu, Y., Chen, K. et al. Daily Activity Feature Selection in Smart Homes Based on Pearson Correlation Coefficient. Neural Process Lett 51, 1771–1787 (2020). https://doi.org/10.1007/s11063-019-10185-8
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DOI: https://doi.org/10.1007/s11063-019-10185-8