{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:47:06Z","timestamp":1772041626395,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T00:00:00Z","timestamp":1592438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T00:00:00Z","timestamp":1592438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100003077","name":"The Knowledge Foundation","doi-asserted-by":"crossref","award":["20100271"],"award-info":[{"award-number":["20100271"]}],"id":[{"id":"10.13039\/100003077","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Human activity recognition as an engineering tool as well as an active research field has become fundamental to many applications in various fields such as health care, smart home monitoring and surveillance. However, delivering sufficiently robust activity recognition systems from sensor data recorded in a smart home setting is a challenging task. Moreover, human activity datasets are typically highly imbalanced because generally certain activities occur more frequently than others. Consequently, it is challenging to train classifiers from imbalanced human activity datasets. Deep learning algorithms perform well on balanced datasets, yet their performance cannot be promised on imbalanced datasets. Therefore, we aim to address the problem of class imbalance in deep learning for smart home data. We assess it with Activities of Daily Living recognition using binary sensors dataset. This paper proposes a data level perspective combined with a temporal window technique to handle imbalanced human activities from smart homes in order to make the learning algorithms more sensitive to the minority class. The experimental results indicate that handling imbalanced human activities from the data-level outperforms algorithms level and improved the classification performance.<\/jats:p>","DOI":"10.1007\/s42979-020-00211-1","type":"journal-article","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T12:02:38Z","timestamp":1592481758000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Efficacy of Imbalanced Data Handling Methods on Deep Learning for Smart Homes Environments"],"prefix":"10.1007","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9489-8330","authenticated-orcid":false,"given":"Rebeen Ali","family":"Hamad","sequence":"first","affiliation":[]},{"given":"Masashi","family":"Kimura","sequence":"additional","affiliation":[]},{"given":"Jens","family":"Lundstr\u00f6m","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,18]]},"reference":[{"key":"211_CR1","unstructured":"What is healthy ageing? https:\/\/www.who.int\/ageing\/healthy-ageing\/en\/. Accessed: 2019-08-10."},{"key":"211_CR2","doi-asserted-by":"crossref","unstructured":"Ali\u00a0Hamad Rebeen, J\u00e4rpe Eric, Lundstr\u00f6m Jens. Stability analysis of the t-sne algorithm for humanactivity pattern data. In The 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), 2018.","DOI":"10.1109\/SMC.2018.00318"},{"key":"211_CR3","unstructured":"Bai Shaojie, Zico Kolter J, Koltun Vladlen. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271, 2018."},{"issue":"4","key":"211_CR4","doi-asserted-by":"publisher","first-page":"6474","DOI":"10.3390\/s140406474","volume":"14","author":"Oresti Banos","year":"2014","unstructured":"Banos Oresti, Galvez Juan-Manuel, Damas Miguel, Pomares Hector, Rojas Ignacio. Window size impact in human activity recognition. Sensors. 2014;14(4):6474\u201399.","journal-title":"Sensors"},{"key":"211_CR5","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.jpdc.2017.05.007","volume":"118","author":"Liang Cao","year":"2018","unstructured":"Cao Liang, Wang Yufeng, Zhang Bo, Jin Qun, V Vasilakos Athanasios. Gchar: An efficient group-based context\u2013aware human activity recognition on smartphone. Journal of Parallel and Distributed Computing. 2018;118:67\u201380.","journal-title":"Journal of Parallel and Distributed Computing"},{"key":"211_CR6","doi-asserted-by":"crossref","unstructured":"Manosha Chathuramali KG, Rodrigo Ranga . Faster human activity recognition with svm. In Advances in ICT for Emerging Regions (ICTer), 2012 International Conference on, pages 197\u2013203. IEEE, 2012.","DOI":"10.1109\/ICTer.2012.6421415"},{"key":"211_CR7","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"Nitesh\u00a0V Chawla","year":"2002","unstructured":"Chawla Nitesh\u00a0V, Bowyer Kevin\u00a0W, Hall Lawrence\u00a0O, Philip Kegelmeyer W. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research. 2002;16:321\u201357.","journal-title":"Journal of artificial intelligence research"},{"key":"211_CR8","first-page":"1050","volume":"28","author":"Jasmine Collins","year":"2017","unstructured":"Collins Jasmine, Sohl-Dickstein Jascha, Sussillo David. Capacity and trainability in recurrent neural networks. stat. 2017;28:1050.","journal-title":"stat"},{"key":"211_CR9","doi-asserted-by":"crossref","unstructured":"Das Barnan , Seelye Adriana\u00a0M, Thomas Brian\u00a0L, Cook Diane\u00a0J, Holder Larry\u00a0B, Schmitter-Edgecombe Maureen. Using smart phones for context-aware prompting in smart environments. In 2012 IEEE Consumer Communications and Networking Conference (CCNC), pages 399\u2013403. IEEE, 2012.","DOI":"10.1109\/CCNC.2012.6181023"},{"key":"211_CR10","unstructured":"Devarakonda Aditya, Naumov Maxim, Garland Michael. Adabatch: Adaptive batch sizes for training deep neural networks. arXiv preprint arXiv:1712.02029, 2017."},{"key":"211_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-0746-y","author":"M Espinilla","year":"2018","unstructured":"Espinilla M, Medina J, Hallberg J, Nugent C. A new approach based on temporal sub-windows for online sensor-based activity recognition. J Ambient Intell Human Comput. 2018. https:\/\/doi.org\/10.1007\/s12652-018-0746-y.","journal-title":"J Ambient Intell Human Comput"},{"issue":"2","key":"211_CR12","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1007\/s11227-013-0978-8","volume":"66","author":"Iram Fatima","year":"2013","unstructured":"Fatima Iram, Fahim Muhammad, Lee Young-Koo, Lee Sungyoung. Analysis and effects of smart home dataset characteristics for daily life activity recognition. The Journal of Supercomputing. 2013;66(2):760\u201380.","journal-title":"The Journal of Supercomputing"},{"key":"211_CR13","doi-asserted-by":"crossref","unstructured":"Fung Gabriel Pui\u00a0Cheong, Yu Jeffrey\u00a0Xu, Wang Haixun, Cheung David\u00a0W, Liu Huan. A balanced ensemble approach to weighting classifiers for text classification. In Sixth International Conference on Data Mining (ICDM\u201906), pages 869\u2013873. IEEE, 2006.","DOI":"10.1109\/ICDM.2006.2"},{"issue":"4","key":"211_CR14","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","volume":"42","author":"Mikel Galar","year":"2011","unstructured":"Galar Mikel, Fernandez Alberto, Barrenechea Edurne, Bustince Humberto, Herrera Francisco. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2011;42(4):463\u201384.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)"},{"key":"211_CR15","doi-asserted-by":"publisher","unstructured":"Hamad R.\u00a0A, Salguero A.\u00a0G, Bouguelia M, Espinilla M, Quero J.\u00a0M. Efficient activity recognition in smart homes using delayed fuzzy temporal windows on binary sensors. IEEE Journal of Biomedical and Health Informatics, pages 1\u20131, 2019. ISSN 2168-2194. https:\/\/doi.org\/10.1109\/JBHI.2019.2918412.","DOI":"10.1109\/JBHI.2019.2918412"},{"key":"211_CR16","unstructured":"Hammerla Nils\u00a0Y, Halloran Shane, Ploetz Thomas. Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880, 2016."},{"key":"211_CR17","doi-asserted-by":"crossref","unstructured":"Huang Chen, Li Yining, Change\u00a0Loy Chen, Tang Xiaoou. Learning deep representation for imbalanced classification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5375\u20135384, 2016.","DOI":"10.1109\/CVPR.2016.580"},{"issue":"5","key":"211_CR18","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3233\/IDA-2002-6504","volume":"6","author":"Nathalie Japkowicz","year":"2002","unstructured":"Japkowicz Nathalie, Stephen Shaju. The class imbalance problem: A systematic study. Intelligent data analysis. 2002;6(5):429\u201349.","journal-title":"Intelligent data analysis"},{"issue":"2","key":"211_CR19","doi-asserted-by":"publisher","first-page":"414","DOI":"10.3390\/s17020414","volume":"17","author":"Luyang Jing","year":"2017","unstructured":"Jing Luyang, Wang Taiyong, Zhao Ming, Wang Peng. An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox. Sensors. 2017;17(2):414.","journal-title":"Sensors"},{"key":"211_CR20","doi-asserted-by":"publisher","unstructured":"Johnson Justin\u00a0M, Khoshgoftaar Taghi\u00a0M. Survey on deep learning with class imbalance. Journal of Big Data, 6(1):27, Mar 2019. ISSN 2196-1115. https:\/\/doi.org\/10.1186\/s40537-019-0192-5.","DOI":"10.1186\/s40537-019-0192-5"},{"issue":"6","key":"211_CR21","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/s00779-009-0277-9","volume":"14","author":"TL Kasteren","year":"2010","unstructured":"Kasteren TL, Englebienne Gwenn, Kr\u00f6se BJ. An activity monitoring system for elderly care using generative and discriminative models. Personal and ubiquitous computing. 2010;14(6):489\u201398.","journal-title":"Personal and ubiquitous computing"},{"issue":"8","key":"211_CR22","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1109\/TNNLS.2017.2732482","volume":"29","author":"Salman\u00a0H Khan","year":"2017","unstructured":"Khan Salman\u00a0H, Hayat Munawar, Bennamoun Mohammed, Sohel Ferdous\u00a0A, Togneri Roberto. Cost-sensitive learning of deep feature representations from imbalanced data. IEEE transactions on neural networks and learning systems. 2017;29(8):3573\u201387.","journal-title":"IEEE transactions on neural networks and learning systems"},{"issue":"3","key":"211_CR23","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","volume":"15","author":"Oscar\u00a0D Lara","year":"2013","unstructured":"Lara Oscar\u00a0D, Labrador Miguel\u00a0A, et al. A survey on human activity recognition using wearable sensors. IEEE Communications Surveys and Tutorials. 2013;15(3):1192\u2013209.","journal-title":"IEEE Communications Surveys and Tutorials"},{"issue":"2","key":"211_CR24","doi-asserted-by":"publisher","first-page":"679","DOI":"10.3390\/s18020679","volume":"18","author":"Fr\u00e9d\u00e9ric Li","year":"2018","unstructured":"Li Fr\u00e9d\u00e9ric, Shirahama Kimiaki, Nisar Muhammad\u00a0Adeel, K\u00f6ping Lukas, Grzegorzek Marcin. Comparison of feature learning methods for human activity recognition using wearable sensors. Sensors. 2018;18(2):679.","journal-title":"Sensors"},{"key":"211_CR25","doi-asserted-by":"crossref","unstructured":"Medina-Quero Javier, Orr Claire, Zang Shuai, Nugent Chris, Salguero Alberto, Espinilla Macarena. Real-time recognition of interleaved activities based on ensemble classifier of long short-term memory with fuzzy temporal windows. In Multidisciplinary Digital Publishing Institute Proceedings, volume\u00a02, page 1225, 2018a.","DOI":"10.3390\/proceedings2191225"},{"key":"211_CR26","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.eswa.2018.07.068","volume":"114","author":"Javier Medina-Quero","year":"2018","unstructured":"Medina-Quero Javier, Zhang Shuai, Nugent Chris, Espinilla M. Ensemble classifier of long short-term memory with fuzzy temporal windows on binary sensors for activity recognition. Expert Systems with Applications. 2018b;114:441\u201353.","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"211_CR27","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s40860-018-0065-2","volume":"4","author":"G Mokhtari","year":"2018","unstructured":"Mokhtari G, Aminikhanghahi S, Zhang Qing, Cook Diane\u00a0J. Fall detection in smart home environments using uwb sensors and unsupervised change detection. Journal of Reliable Intelligent Environments. 2018;4(3):131\u20139.","journal-title":"Journal of Reliable Intelligent Environments"},{"key":"211_CR28","doi-asserted-by":"crossref","unstructured":"Rueda Fernando Moya, Grzeszick Ren\u00e9, Fink Gernot, Feldhorst Sascha, Hompel Michael ten. Convolutional neural networks for human activity recognition using body-worn sensors. In Informatics, volume\u00a05, page\u00a026. Multidisciplinary Digital Publishing Institute, 2018.","DOI":"10.3390\/informatics5020026"},{"issue":"11","key":"211_CR29","doi-asserted-by":"publisher","first-page":"2556","DOI":"10.3390\/s17112556","volume":"17","author":"Abdulmajid Murad","year":"2017","unstructured":"Murad Abdulmajid, Pyun Jae-Young. Deep recurrent neural networks for human activity recognition. Sensors. 2017;17(11):2556.","journal-title":"Sensors"},{"key":"211_CR30","unstructured":"Nguyen Ky Trung, Portet Francois, Garbay Catherine. Dealing with Imbalanced data sets for Human Activity Recognition using Mobile Phone Sensors. In 3rd International Workshop on Smart Sensing Systems, June 2018, Rome, Italy, 2018."},{"key":"211_CR31","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.eswa.2018.03.056","volume":"105","author":"HF Nweke","year":"2018","unstructured":"Nweke HF, Teh YW, Al-Garadi MA, Alo UR. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Syst Appl. 2018;105:233\u201361.","journal-title":"Expert Syst Appl"},{"issue":"5","key":"211_CR32","doi-asserted-by":"publisher","first-page":"5460","DOI":"10.3390\/s130505460","volume":"13","author":"F Ord\u00f3\u00f1ez","year":"2013","unstructured":"Ord\u00f3\u00f1ez F, De Toledo P, Sanchis A, et al. Activity recognition using hybrid generative\/discriminative models on home environments using binary sensors. Sensors. 2013;13(5):5460\u201377.","journal-title":"Sensors"},{"key":"211_CR33","doi-asserted-by":"crossref","unstructured":"Park Jiho, Jang Kiyoung, Yang Sung-Bong. Deep neural networks for activity recognition with multi-sensor data in a smart home. In Internet of Things (WF-IoT), 2018 IEEE 4th World Forum on, pages 155\u2013160. IEEE, 2018.","DOI":"10.1109\/WF-IoT.2018.8355147"},{"key":"211_CR34","doi-asserted-by":"crossref","unstructured":"Singh Deepika, Merdivan Erinc, Hanke Sten, Kropf Johannes, Geist Matthieu, Holzinger Andreas. Convolutional and recurrent neural networks for activity recognition in smart environment. In Towards integrative machine learning and knowledge extraction, pages 194\u2013205. Springer, 2017.","DOI":"10.1007\/978-3-319-69775-8_12"},{"issue":"1","key":"211_CR35","first-page":"1929","volume":"15","author":"Nitish Srivastava","year":"2014","unstructured":"Srivastava Nitish, Hinton Geoffrey, Krizhevsky Alex, Sutskever Ilya, Salakhutdinov Ruslan. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research. 2014;15(1):1929\u201358.","journal-title":"The Journal of Machine Learning Research"},{"key":"211_CR36","doi-asserted-by":"crossref","unstructured":"Stikic Maja, Huynh T\u00e2m, Van\u00a0Laerhoven Kristof, Schiele Bernt. Adl recognition based on the combination of rfid and accelerometer sensing. In Pervasive Computing Technologies for Healthcare, 2008. PervasiveHealth 2008. Second International Conference on, pages 258\u2013263. IEEE, 2008.","DOI":"10.1109\/PCTHEALTH.2008.4571084"},{"issue":"12","key":"211_CR37","doi-asserted-by":"publisher","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","volume":"40","author":"Yanmin Sun","year":"2007","unstructured":"Sun Yanmin, Kamel Mohamed\u00a0S, Wong Andrew\u00a0KC, Wang Yang. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition. 2007;40(12):3358\u201378.","journal-title":"Pattern Recognition"},{"key":"211_CR38","doi-asserted-by":"crossref","unstructured":"Tapia Emmanuel\u00a0Munguia, Intille Stephen\u00a0S, Larson Kent. Activity recognition in the home using simple and ubiquitous sensors. In International conference on pervasive computing, pages 158\u2013175. Springer, 2004.","DOI":"10.1007\/978-3-540-24646-6_10"},{"key":"211_CR39","doi-asserted-by":"crossref","unstructured":"Wang Jindong, Chen Yiqiang, Hao Shuji, Peng Xiaohui, Lisha Hu. Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters; 2018.","DOI":"10.1016\/j.patrec.2018.02.010"},{"key":"211_CR40","doi-asserted-by":"crossref","unstructured":"Wu Qiong, Zeng Zhiwei, Lin Jun, Chen Yiqiang. Ai empowered context-aware smart system for medication adherence. International Journal of Crowd Science, 2017.","DOI":"10.1108\/IJCS-07-2017-0006"},{"key":"211_CR41","doi-asserted-by":"publisher","first-page":"105613","DOI":"10.1016\/j.asoc.2019.105613","volume":"83","author":"Salisu\u00a0Wada Yahaya","year":"2019","unstructured":"Yahaya Salisu\u00a0Wada, Lotfi Ahmad, Mahmud Mufti. A consensus novelty detection ensemble approach for anomaly detection in activities of daily living. Applied Soft Computing. 2019;83:105613.","journal-title":"Applied Soft Computing"},{"key":"211_CR42","doi-asserted-by":"crossref","unstructured":"Yala Nawel, Fergani Belkacem, Fleury Anthony. Feature extraction for human activity recognition on streaming data. In Innovations in Intelligent SysTems and Applications (INISTA), 2015 International Symposium on, pages 1\u20136. IEEE, 2015.","DOI":"10.1109\/INISTA.2015.7276759"},{"key":"211_CR43","unstructured":"Yang Jianbo, Nguyen Minh\u00a0Nhut, San Phyo\u00a0Phyo , Li Xiaoli , Krishnaswamy Shonali. Deep convolutional neural networks on multichannel time series for human activity recognition. In Ijcai, volume\u00a015, pages 3995\u20134001, 2015."},{"issue":"6","key":"211_CR44","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/S1005-8885(11)60319-1","volume":"19","author":"LIU Zhen","year":"2012","unstructured":"Zhen LIU, Qiong LIU. Studying cost-sensitive learning for multi-class imbalance in internet traffic classification. The Journal of China Universities of Posts and Telecommunications. 2012;19(6):63\u201372.","journal-title":"The Journal of China Universities of Posts and Telecommunications"},{"issue":"3","key":"211_CR45","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1111\/j.1467-8640.2010.00358.x","volume":"26","author":"Zhi-Hua Zhou","year":"2010","unstructured":"Zhou Zhi-Hua, Liu Xu-Ying. On multi-class cost-sensitive learning. Computational Intelligence. 2010;26(3):232\u201357.","journal-title":"Computational Intelligence"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-020-00211-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-020-00211-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-020-00211-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T22:52:24Z","timestamp":1723071144000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-020-00211-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,18]]},"references-count":45,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["211"],"URL":"https:\/\/doi.org\/10.1007\/s42979-020-00211-1","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"value":"2662-995X","type":"print"},{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,18]]},"assertion":[{"value":"28 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"204"}}