{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T19:55:38Z","timestamp":1777665338170,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,6]],"date-time":"2017-11-06T00:00:00Z","timestamp":1509926400000},"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>Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.<\/jats:p>","DOI":"10.3390\/s17112556","type":"journal-article","created":{"date-parts":[[2017,11,6]],"date-time":"2017-11-06T11:39:38Z","timestamp":1509968378000},"page":"2556","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":406,"title":["Deep Recurrent Neural Networks for Human Activity Recognition"],"prefix":"10.3390","volume":"17","author":[{"given":"Abdulmajid","family":"Murad","sequence":"first","affiliation":[{"name":"Department of Information Communication Engineering, Chosun University, 375 Susuk-dong, Dong-gu, Gwangju 501-759, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1143-8281","authenticated-orcid":false,"given":"Jae-Young","family":"Pyun","sequence":"additional","affiliation":[{"name":"Department of Information Communication Engineering, Chosun University, 375 Susuk-dong, Dong-gu, Gwangju 501-759, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sundermeyer, M., Schl\u00fcter, R., and Ney, H. (2012, January 9\u201313). LSTM Neural Networks for Language Modeling. Proceedings of the Thirteenth Annual Conference of the International Speech Communication Association, Portland, OR, USA.","DOI":"10.21437\/Interspeech.2012-65"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yao, L., Cho, K., Ballas, N., Pa\u00ed, C., and Courville, A. (2015, January 7\u201313). Describing Videos by Exploiting Temporal Structure. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.512"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks, Springer. Studies in Computational Intelligence.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_5","unstructured":"Pl\u00f6tz, T., Hammerla, N.Y., and Olivier, P. (2011, January 16\u201322). Feature Learning for Activity Recognition in Ubiquitous Computing. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain."},{"key":"ref_6","unstructured":"Alsheikh, M.A., Selim, A., Niyato, D., Doyle, L., Lin, S., and Tan, H.-P. (2016, January 12). Deep Activity Recognition Models with Triaxial Accelerometers. Proceedings of the AAAI Workshop: Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Phoenix, AZ, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zeng, M., Nguyen, L.T., Yu, B., Mengshoel, O.J., Zhu, J., Wu, P., and Zhang, J. (2014, January 6\u20137). Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors. Proceedings of the 6th International Conference on Mobile Computing, Applications and Services, Austin, TX, USA.","DOI":"10.4108\/icst.mobicase.2014.257786"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Xue, Y. (2015, January 20). A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China.","DOI":"10.1109\/SMC.2015.263"},{"key":"ref_9","unstructured":"Hessen, H.-O., and Tessem, A.J. (2015). Human Activity Recognition with Two Body-Worn Accelerometer Sensors. [Master\u2019s Thesis, Norwegian University of Science and Technology]."},{"key":"ref_10","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 Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ravi, D., Wong, C., Lo, B., and Yang, G.-Z. (2016, January 14\u201317). Deep learning for human activity recognition: A resource efficient implementation on low-power devices. Proceedings of the IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), San Francisco, CA, USA.","DOI":"10.1109\/BSN.2016.7516235"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/29.21701","article-title":"Phoneme recognition using time-delay neural networks","volume":"37","author":"Waibel","year":"1989","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fan, Y., Qian, Y., Xie, F., and Soong, F.K. (2014, January 14\u201318). TTS synthesis with bidirectional LSTM based Recurrent Neural Networks. Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association, Singapore.","DOI":"10.21437\/Interspeech.2014-443"},{"key":"ref_15","unstructured":"Kremer, S., and Kolen, J. (2001). Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term Dependencies. Field Guide to Dynamical Recurrent Networks, Wiley-IEEE Press."},{"key":"ref_16","first-page":"226","article-title":"Combining classifiers","volume":"2","author":"Kittler","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_18","unstructured":"Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., and Macherey, K. (2016). Google\u2019s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. CoRR."},{"key":"ref_19","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A Public Domain Dataset for Human Activity Recognition Using Smartphones. Proceedings of the European Symposium on Artificial Neural Networks, Bruges, Belgium."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Sawchuk, A.A. (2012, January 5\u20138). USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370438"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1109\/TITB.2009.2036165","article-title":"Wearable assistant for Parkinson\u2019s disease patients with the freezing of gait symptom","volume":"14","author":"Bachlin","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., and Tr\u00f6ster, G. (2008). Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection. Wireless Sensor Networks, Springer.","DOI":"10.1007\/978-3-540-77690-1_2"},{"key":"ref_24","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Optimization for Training Deep Models. Deep Learning, The MIT Press."},{"key":"ref_25","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (1970, September 13). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pham, V., Bluche, T., Kermorvant, C., and Louradour, J. (2014, January 1\u20134). Dropout Improves Recurrent Neural Networks for Handwriting Recognition. Proceedings of the 14th International Conference on Frontiers in Handwriting Recognition, Crete, Greece.","DOI":"10.1109\/ICFHR.2014.55"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jiang, W. (2015, January 26\u201330). Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks. Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia.","DOI":"10.1145\/2733373.2806333"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chandan Kumar, R., Bharadwaj, S.S., Sumukha, B.N., and George, K. (2016, January 12\u201313). Human activity recognition in cognitive environments using sequential ELM. Proceedings of the Second International Conference on Cognitive Computing and Information Processing, Mysuru, India.","DOI":"10.1109\/CCIP.2016.7802880"},{"key":"ref_30","first-page":"34","article-title":"Yuhuang Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework","volume":"2015","author":"Zheng","year":"2015","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Prakash Reddy Vaka, B.B. (2015). A Pervasive Middleware for Activity Recognition with Smartphones. [Master\u2019s Thesis, University of Missouri].","DOI":"10.1109\/PERCOMW.2015.7134073"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hammerla, N., and Kirkham, R. (2013, January 9\u201312). On Preserving Statistical Characteristics of Accelerometry Data using their Empirical Cumulative Distribution. Proceedings of the 2013 International Symposium on Wearable Computers, Zurich, Switzerland.","DOI":"10.1145\/2493988.2494353"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/JBHI.2016.2633287","article-title":"A deep learning approach to on-node sensor data analytics for mobile or wearable devices","volume":"21","author":"Ravi","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/11\/2556\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:15Z","timestamp":1760208495000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/11\/2556"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,6]]},"references-count":33,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2017,11]]}},"alternative-id":["s17112556"],"URL":"https:\/\/doi.org\/10.3390\/s17112556","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,11,6]]}}}