{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:26:41Z","timestamp":1778257601502,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,6,6]],"date-time":"2019-06-06T00:00:00Z","timestamp":1559779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Technology R&amp;D Program of Hunan Province","award":["2018GK2052"],"award-info":[{"award-number":["2018GK2052"]}]},{"name":"Science and Technology Plan of Hunan","award":["2016TP1003"],"award-info":[{"award-number":["2016TP1003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Human activity recognition (HAR) using deep neural networks has become a hot topic in human\u2013computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of automatic learning, we propose a novel asymmetric residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, while still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of the ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared the results with some conventional and state-of-the-art learning-based methods. We discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets.<\/jats:p>","DOI":"10.3390\/info10060203","type":"journal-article","created":{"date-parts":[[2019,6,7]],"date-time":"2019-06-07T03:56:31Z","timestamp":1559879791000},"page":"203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Asymmetric Residual Neural Network for Accurate Human Activity Recognition"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0163-0007","authenticated-orcid":false,"given":"Jun","family":"Long","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 410083, China"},{"name":"Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1425-4875","authenticated-orcid":false,"given":"Wuqing","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6336-0228","authenticated-orcid":false,"given":"Zhan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9891-6161","authenticated-orcid":false,"given":"Osolo Ian","family":"Raymond","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4177","DOI":"10.3233\/JIFS-169976","article-title":"A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes","volume":"36","author":"Sukor","year":"2019","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1109\/TII.2017.2678522","article-title":"Participatory Sensing for Smart Cities: A Case Study on Transport Trip Quality Measurement","volume":"13","author":"Xiao","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.inffus.2018.01.012","article-title":"Advances in multi-sensor fusion for body sensor networks: Algorithms, architectures, and applications","volume":"45","author":"Fortino","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/JBHI.2017.2700722","article-title":"Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports","volume":"22","author":"Qiu","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.eswa.2016.11.026","article-title":"Playing real-time strategy games by imitating human players\u2019 micromanagement skills based on spatial analysis","volume":"71","author":"Oh","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lisowska, A., O\u2019Neil, A., and Poole, I. (2018, January 19\u201321). Cross-cohort Evaluation of Machine Learning Approaches to Fall Detection from Accelerometer Data. Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018)\u2014Volume 5: HEALTHINF, Funchal, Madeira, Portugal.","DOI":"10.5220\/0006554400770082"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"31314","DOI":"10.3390\/s151229858","article-title":"Physical Human Activity Recognition Using Wearable Sensors","volume":"15","author":"Attal","year":"2015","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2010","journal-title":"SIGKDD Explor."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. (2004, January 21\u201323). Activity Recognition from User-Annotated Acceleration Data. Proceedings of the Pervasive Computing, Second International Conference\u2014PERVASIVE 2004, Vienna, Austria.","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/MPRV.2014.52","article-title":"In-Home Activity Recognition: Bayesian Inference for Hidden Markov Models","volume":"13","author":"Englebienne","year":"2014","journal-title":"IEEE Pervasive Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ramamurthy, S.R., and Roy, N. (2018). Recent trends in machine learning for human activity recognition\u2014A survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 8.","DOI":"10.1002\/widm.1254"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1109\/JIOT.2018.2823084","article-title":"Locomotion Activity Recognition Using Stacked Denoising Autoencoders","volume":"5","author":"Gu","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_13","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shirahama, K., and Grzegorzek, M. (2017). On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition. Electronics, 6.","DOI":"10.3390\/electronics6020044"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1109\/TPAMI.2009.132","article-title":"Visual Word Ambiguity","volume":"32","author":"Veenman","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11209","DOI":"10.1109\/ACCESS.2019.2891894","article-title":"Deep Attention-Guided Hashing","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sarkar, A., Dasgupta, S., Naskar, S.K., and Bandyopadhyay, S. (2018, January 15\u201320). Says Who? Deep Learning Models for Joint Speech Recognition, Segmentation and Diarization. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462375"},{"key":"ref_18","unstructured":"Kumar, A., Irsoy, O., Ondruska, P., Iyyer, M., Bradbury, J., Gulrajani, I., Zhong, V., Paulus, R., and Socher, R. (2016, January 19\u201324). Ask Me Anything: Dynamic Memory Networks for Natural Language Processing. Proceedings of the 33nd International Conference on Machine Learning, New York City, NY, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shi, Z., Zhang, J.A., Xu, R., and Fang, G. (2018, January 9\u201313). Human Activity Recognition Using Deep Learning Networks with Enhanced Channel State Information. Proceedings of the IEEE Global Communications Conference, Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOMW.2018.8644435"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xu, W., Pang, Y., Yang, Y., and Liu, Y. (2018, January 20\u201324). Human Activity Recognition Based On Convolutional Neural Network. Proceedings of the 24th International Conference on Pattern Recognition, Beijing, China.","DOI":"10.1109\/ICPR.2018.8545435"},{"key":"ref_21","unstructured":"Hammerla, N.Y., Halloran, S., and Pl\u00f6tz, T. (2016, January 9\u201315). Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1109\/ACCESS.2016.2557846","article-title":"Learning Human Identity From Motion Patterns","volume":"4","author":"Neverova","year":"2016","journal-title":"IEEE Access"},{"key":"ref_23","unstructured":"Yang, J., Nguyen, M.N., San, P.P., Li, X., and Krishnaswamy, S. (2015, January 25\u201331). Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"56750","DOI":"10.1109\/ACCESS.2018.2873315","article-title":"DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition","volume":"6","author":"Yang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ronao, C.A., and Cho, S. (2015, January 9\u201312). Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors. Proceedings of the Neural Information Processing\u201422nd International Conference, Istanbul, Turkey.","DOI":"10.1007\/978-3-319-26561-2_6"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Milenkoski, M., Trivodaliev, K., Kalajdziski, S., Jovanov, M., and Stojkoska, B.R. (2018, January 21\u201325). Real time human activity recognition on smartphones using LSTM networks. Proceedings of the 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, Opatija, Croatia.","DOI":"10.23919\/MIPRO.2018.8400205"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"26901","DOI":"10.1007\/s11042-018-5893-9","article-title":"Human action recognition based on quaternion spatial-temporal convolutional neural network and LSTM in RGB videos","volume":"77","author":"Meng","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_29","unstructured":"Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques, Morgan Kaufmann. [3rd ed.]."},{"key":"ref_30","unstructured":"Morales, F.J.O., and Roggen, D. (2016). Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, 16."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.neunet.2016.07.006","article-title":"A theory of local learning, the learning channel, and the optimality of backpropagation","volume":"83","author":"Baldi","year":"2016","journal-title":"Neural Netw."},{"key":"ref_32","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_33","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep Sparse Rectifier Neural Networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_34","unstructured":"Pascanu, R., Mikolov, T., and Bengio, Y. (2013, January 16\u201321). On the difficulty of training recurrent neural networks. Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Carreira, J., and Zisserman, A. (2017, January 21\u201326). Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref_36","unstructured":"Feichtenhofer, C., Pinz, A., and Wildes, R.P. (2016, January 5\u201310). Spatiotemporal Residual Networks for Video Action Recognition. Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R.B., Gupta, A., and He, K. (2018, January 18\u201322). Non-Local Neural Networks. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, F., Shirahama, K., Nisar, M.A., K\u00f6ping, L., and Grzegorzek, M. (2018). Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors. Sensors, 18.","DOI":"10.3390\/s18020679"},{"key":"ref_39","unstructured":"Feichtenhofer, C., Fan, H., Malik, J., and He, K. (2018). SlowFast Networks for Video Recognition. arXiv."},{"key":"ref_40","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_41","unstructured":"Micucci, D., Mobilio, M., and Napoletano, P. (2016). UniMiB SHAR: A new dataset for human activity recognition using acceleration data from smartphones. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ba\u00f1os, O., Garc\u00eda, R., Terriza, J.A.H., Damas, M., Pomares, H., Ruiz, I.R., Saez, A., and Villalonga, C. (2014, January 2\u20135). mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications. Proceedings of the Ambient Assisted Living and Daily Activities\u20146th International Work-Conference, Belfast, UK.","DOI":"10.1007\/978-3-319-13105-4_14"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 6\u20139). Creating and benchmarking a new dataset for physical activity monitoring. Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, Heraklion, Greece.","DOI":"10.1145\/2413097.2413148"},{"key":"ref_44","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yang, Z., He, X., Gao, J., Deng, L., and Smola, A.J. (2016, January 27\u201330). Stacked Attention Networks for Image Question Answering. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.10"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/6\/203\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:56:46Z","timestamp":1760187406000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/6\/203"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,6]]},"references-count":45,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["info10060203"],"URL":"https:\/\/doi.org\/10.3390\/info10060203","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,6]]}}}