{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T06:40:19Z","timestamp":1768286419461,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,23]],"date-time":"2018-04-23T00:00:00Z","timestamp":1524441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.<\/jats:p>","DOI":"10.3390\/s18041288","type":"journal-article","created":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T04:44:48Z","timestamp":1524545088000},"page":"1288","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8906-7572","authenticated-orcid":false,"given":"Alejandro","family":"Baldominos","sequence":"first","affiliation":[{"name":"Computer Science Department, Universidad Carlos III de Madrid, 28911 Leganes, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0998-2907","authenticated-orcid":false,"given":"Yago","family":"Saez","sequence":"additional","affiliation":[{"name":"Computer Science Department, Universidad Carlos III de Madrid, 28911 Leganes, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5121-4821","authenticated-orcid":false,"given":"Pedro","family":"Isasi","sequence":"additional","affiliation":[{"name":"Computer Science Department, Universidad Carlos III de Madrid, 28911 Leganes, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, O., Chamoso, P., Prieto, J., Rodr\u00edguez, S., and de la Prieta, F. (2017). A Serious Game to Reduce Consumption in Smart Buildings. Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems, Springer International Publishing.","DOI":"10.1007\/978-3-319-60285-1_41"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Canizes, B., Pinto, T., Soares, J., Vale, Z., Chamoso, P., and Santos, D. (2018). Smart City: A GECAD-BISITE Energy Management Case Study. Proceedings of the Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection\u201415th International Conference, PAAMS 2017, Springer International Publishing.","DOI":"10.1007\/978-3-319-61578-3_9"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Prieto, J., Chamoso, P., la Prieta, F.D., and Corchado, J.M. (2017, January 12\u201315). A generalized framework for wireless localization in gerontechnology. Proceedings of the 2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB), Salamanca, Spain.","DOI":"10.1109\/ICUWB.2017.8250981"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep convolutional and LSTM recurrent neural neworks for multimodal wearable activity recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_5","unstructured":"Hammerla, N.Y., Halloran, S., and Pl\u00f6tz, T. (arXiv, 2016). Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables, arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.neucom.2017.12.049","article-title":"Evolutionary convolutional neural networks: An application to handwriting recognition","volume":"283","author":"Baldominos","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_7","unstructured":"LeCun, Y., and Bengio, Y. (1998). Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Network, MIT Press."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., and Bengio, Y. (arXiv, 2014). On the properties of neural machine translation: Encoder-decoder approaches, arXiv.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_11","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_12","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_13","unstructured":"Zeiler, M.D. (arXiv, 2012). ADADELTA: An adaptive learning rate method, arXiv."},{"key":"ref_14","unstructured":"Tieleman, T., and Hinton, G. (2018, April 20). Rmsprop: Divide the Gradient by a Running Average of Its Recent Magnitude, 2012. Available online: https:\/\/es.coursera.org\/learn\/neural-networks\/lecture\/YQHki\/rmsprop-divide-the-gradient-by-a-running-average-of-its-recent-magnitude."},{"key":"ref_15","unstructured":"Kingma, D., and Ba, J. (arXiv, 2014). Adam: A Method for Stochastic Optimization, arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/72.572107","article-title":"A new evolutionary system for evolving artificial neural networks","volume":"8","author":"Yao","year":"1997","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1162\/106365602320169811","article-title":"Evolving Neural Networks through Augmenting Topologies","volume":"10","author":"Stanley","year":"2002","journal-title":"Evol. Comput."},{"key":"ref_18","unstructured":"Kassahun, Y., and Sommer, G. (2005, January 29\u201329). Efficient reinforcement learning through evolutionary acquisition of neural topologies. Proceedings of the 13th European Symposium on Artificial Neural Networks, Bruges, Belgium."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Koutn\u00edk, J., Schmidhuber, J., and Gomez, F. (2014, January 12\u201316). Evolving Deep Unsupervised Convolutional Networks for Vision-Based Reinforcement Learning. Proceedings of the 2014 Genetic and Evolutionary Computation Conference, Vancouver, BC, Canada.","DOI":"10.1145\/2576768.2598358"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Verbancsics, P., and Harguess, J. (2015, January 5\u20139). Image Classification using Generative NeuroEvolution for Deep Learning. Proceedings of the 2015 IEEE Winter Conference on Applied Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2015.71"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1162\/artl.2009.15.2.15202","article-title":"A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks","volume":"15","author":"Stanley","year":"2009","journal-title":"Artif. Life"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Young, S.R., Rose, D.C., Karnowsky, T.P., Lim, S.H., and Patton, R.M. (2015, January 15). Optimizing deep learning hyper-parameters through an evolutionary algorithm. Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, Austin, TX, USA.","DOI":"10.1145\/2834892.2834896"},{"key":"ref_23","unstructured":"Loshchilov, I., and Hutter, F. (arXiv, 2016). CMA-ES for Hyperparameter Optimization of Deep Neural Networks, arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fernando, C., Banarse, D., Reynolds, M., Besse, F., Pfau, D., Jaderberg, M., Lanctot, M., and Wierstra, D. (2016, January 20\u201324). Convolution by Evolution: Differentiable Pattern Producing Networks. Proceedings of the 2016 Genetic and Evolutionary Computation Conference, Denver, CO, USA.","DOI":"10.1145\/2908812.2908890"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xie, L., and Yuille, A. (arXiv, 2017). Genetic CNN, arXiv.","DOI":"10.1109\/ICCV.2017.154"},{"key":"ref_26","unstructured":"Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., and Duffy, N. (arXiv, 2017). Evolving Deep Neural Networks, arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Desell, T. (2017, January 15\u201319). Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing. Proceedings of the 2017 Genetic and Evolutionary Computation Conference Companion, Berlin, Germany.","DOI":"10.1145\/3067695.3076002"},{"key":"ref_28","unstructured":"Real, E., Moore, S., Selle, A., Saxena, S., Leon-Suematsu, Y., Tan, J., Le, Q.V., and Kurakin, A. (arXiv, 2017). Large-Scale Evolution of Image Classifiers, arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Suganuma, M., Shirakawa, S., and Nagao, T. (2017, January 15\u201319). A Genetic Programming Approach to Designing Convolutional Neural Network Architectures. Proceedings of the 2017 Genetic and Evolutionary Computation Conference Companion, Berlin, Germany.","DOI":"10.1145\/3071178.3071229"},{"key":"ref_30","unstructured":"Baker, B., Gupta, O., Naik, N., and Raskar, R. (arXiv, 2016). Designing Neural Network Architectures using Reinforcement Learning, arXiv."},{"key":"ref_31","unstructured":"Zoph, B., and Le, Q.V. (arXiv, 2017). Neural architecture search with reinforcement learning, arXiv."},{"key":"ref_32","unstructured":"Le, Q.V., and Zoph, B. (2018, April 20). Using Machine Learning to Explore Neural Network Architecture, 2017. Available online: https:\/\/research.googleblog.com\/2017\/05\/using-machine-learning-to-explore.html."},{"key":"ref_33","unstructured":"BigML (2018, April 20). Deepnets, 2017. Available online: https:\/\/bigml.com\/whatsnew\/deepnet."},{"key":"ref_34","unstructured":"Davison, J. (2017, July 01). DEvol: Automated Deep Neural Network Design via Genetic Programming, 2017. Available online: https:\/\/github.com\/joeddav\/devol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., F\u00f6rster, K., Tr\u00f6ster, G., Lukowicz, P., Bannach, D., Pirkl, G., and Ferscha, A. (2010, January 15\u201318). Collecting complex activity datasets in highly rich networked sensor environments. Proceedings of the Seventh International Conference on Networked Sensing Systems, Kassel, Germany.","DOI":"10.1109\/INSS.2010.5573462"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Roggen, D., B\u00e4chlin, M., Sch\u00fcmm, J., Holleczek, T., Lombriser, C., Tr\u00f6ster, G., Widmer, L., Majoe, D., and Gutknecht, J. (2010, January 7\u20139). An educational and research kit for activity and context recognition from on-body sensors. Proceedings of the 2010 International Conference on Body Sensor Networks, Singapore.","DOI":"10.1109\/BSN.2010.35"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MPRV.2008.40","article-title":"Wearable activity tracking in car manufacturing","volume":"7","author":"Stiefmeier","year":"2008","journal-title":"IEEE Pervasive Comput."},{"key":"ref_38","unstructured":"Xsens (2017, April 05). IMU Inertial Measurement Unit\u2014Xsens 3D Motion Tracking, 2017. Available online: https:\/\/www.xsens.com\/tags\/imu\/."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pirkl, G., Stockinger, K., Kunze, K., and Lukowicz, P. (October, January 28). Adapting magnetic resonant coupling based relative positioning technology for wearable activity recognition. Proceedings of the 2008 International Symposium on Wearable Computers, Pittsburgh, PA, USA.","DOI":"10.1109\/ISWC.2008.4911584"},{"key":"ref_40","unstructured":"Intersense (2017, April 05). InterSense Wireless InertiaCube3, 2017. Available online: http:\/\/forums.ni.com\/attachments\/ni\/280\/4310\/1\/WirelessInertiaCube3.pdf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/978-3-540-77690-1_2","article-title":"Activity recognition from on-body sensors: Accuracy-power trade-off by dynamic sensor selection","volume":"Volume 4913","author":"Zappi","year":"2008","journal-title":"Wireless Sensor Networks"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MPRV.2008.36","article-title":"Rapid prototyping of activity recognition applications","volume":"7","author":"Bannach","year":"2008","journal-title":"IEEE Pervasive Comput."},{"key":"ref_43","unstructured":"Roggen, D., Tr\u00f6ster, G., Lukowicz, P., Ferscha, A., and del R. Mill\u00e1n, J. (2010). OPPORTUNITY Deliverable D5.1: Stage 1 Case Study Report and Stage 2 Specification, University of Passau. Technical Report."},{"key":"ref_44","unstructured":"Project, O. (2018, April 20). Activity Recognition Challenge, 2011. Available online: http:\/\/opportunity-project.eu\/challenge."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1016\/j.patrec.2012.12.014","article-title":"The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition","volume":"34","author":"Chavarriaga","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Sagha, H., Digumarti, S.T., del R. Mill\u00e1n, J., Chavarriaga, R., Calatroni, A., Roggen, D., and Tr\u00f6ster, G. (2011, January 9\u201312). Benchmarking classification techniques using the Opportunity human activity dataset. Proceedings of the 2011 IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK, USA.","DOI":"10.1109\/ICSMC.2011.6083628"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Cao, H., Nguyen, M.N., Phua, C., Krishnaswamy, S., and Li, X.L. (2012, January 5\u20138). An Integrated Framework for Human Activity Classification. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370268"},{"key":"ref_48","unstructured":"Webb, G.I. (August, January 31). Decision tree grafting from the all-tests-but-one partition. Proceedings of the 16th International Joint Conference on Artificial Intelligence, Stockholm, Sweden."},{"key":"ref_49","unstructured":"Yang, J.B., Nguyen, M.N., San, P.P., Li, X.L., and Krishnaswamy, S. (2015, January 25\u201331). Deep convolutional neural networks on multichannel time series for human activity recognition. Proceedings of the 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_50","unstructured":"Vinyard, J. (2017, February 23). Efficient Overlapping Windows with Numpy, 2012. Available online: http:\/\/www.johnvinyard.com\/blog\/?p=268."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/BFb0055930","article-title":"Grammatical Evolution: Evolving Programs for an Arbitrary Language","volume":"Volume 1391","author":"Ryan","year":"1998","journal-title":"Proceedings of the 1st European Workshop on Genetic Programming"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1288\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:01:42Z","timestamp":1760194902000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1288"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,23]]},"references-count":51,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["s18041288"],"URL":"https:\/\/doi.org\/10.3390\/s18041288","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,23]]}}}