{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:28:28Z","timestamp":1760956108247,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s11042-021-11363-4","type":"journal-article","created":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T22:02:33Z","timestamp":1630533753000},"page":"36159-36182","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A hybrid CNN and BLSTM network for human complex activity recognition with multi-feature fusion"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2555-343X","authenticated-orcid":false,"given":"Ruohong","family":"Huan","sequence":"first","affiliation":[]},{"given":"Ziwei","family":"Zhan","sequence":"additional","affiliation":[]},{"given":"Luoqi","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Kaikai","family":"Chi","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Ronghua","family":"Liang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,1]]},"reference":[{"key":"11363_CR1","doi-asserted-by":"crossref","unstructured":"Blanke U, Schiele B (2009) Daily routine recognition through activity spotting. In Int Symp Loc Context Aware Springer 192\u2013206","DOI":"10.1007\/978-3-642-01721-6_12"},{"key":"11363_CR2","doi-asserted-by":"crossref","unstructured":"Ciabattoni L, Foresi G, Monteri\u00f9 A, Pagnotta DP, Romeo L, Spalazzi L, De Cesare A (2018) Complex activity recognition system based on cascade classifiers and wearable device data. In\u00a02018 IEEE Int Conf Consum Electron (ICCE) IEEE 1\u20132","DOI":"10.1109\/ICCE.2018.8326283"},{"key":"11363_CR3","doi-asserted-by":"crossref","unstructured":"Cvetkovi\u0107 B, Mirchevska V, Janko V, Lu\u0161trek M (2015) Recognition of high-level activities with a smartphone. In Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp 1453\u20131461","DOI":"10.1145\/2800835.2801616"},{"key":"11363_CR4","doi-asserted-by":"crossref","unstructured":"Damirchi H, Khorrambakht R, Taghirad H (2020) Arc-net: Activity recognition through capsules. arXiv preprin","DOI":"10.1109\/ICMLA51294.2020.00215"},{"key":"11363_CR5","doi-asserted-by":"crossref","unstructured":"Dernbach S, Das B, Krishnan NC, Thomas BL, Cook DJ (2012) Simple and complex activity recognition through smart phones. In\u00a02012\u00a08th Int Conf Intell Environ IEEE 214\u2013221","DOI":"10.1109\/IE.2012.39"},{"key":"11363_CR6","doi-asserted-by":"crossref","unstructured":"Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In Proc IEEE Conf Comput Vis Pattern Recognit 2625\u20132634","DOI":"10.21236\/ADA623249"},{"key":"11363_CR7","doi-asserted-by":"crossref","unstructured":"Edel M, K\u00f6ppe E (2016) Binarized-blstm-rnn based human activity recognition. In\u00a02016 Int Conf Indoor Position Indoor Navig (IPIN) IEEE 1\u20137","DOI":"10.1109\/IPIN.2016.7743581"},{"key":"11363_CR8","doi-asserted-by":"crossref","unstructured":"Friday NH, Al-garadi MA, Mujtaba G, Alo UR, Waqas A (2018) Deep learning fusion conceptual frameworks for complex human activity recognition using mobile and wearable sensors. In\u00a02018 Int Conf Comput Math Eng Technol (iCoMET) IEEE 1\u20137","DOI":"10.1109\/ICOMET.2018.8346364"},{"key":"11363_CR9","doi-asserted-by":"crossref","unstructured":"Gacav C, Benligiray B, Topal C (2016) Sequential forward feature selection for facial expression recognition. In\u00a02016 24th Signal Process Commun Appl Conf (SIU) IEEE 1481\u20131484","DOI":"10.1109\/SIU.2016.7496031"},{"key":"11363_CR10","doi-asserted-by":"crossref","unstructured":"Gil-Mart\u00edn M, San-Segundo R, Fern\u00e1ndez-Mart\u00ednez F, de C\u00f3rdoba R (2020) Human activity recognition adapted to the type of movement. Comput Elect Eng 88:106822","DOI":"10.1016\/j.compeleceng.2020.106822"},{"issue":"2","key":"11363_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3090076","volume":"1","author":"Y Guan","year":"2017","unstructured":"Guan Y, Pl\u00f6tz T (2017) Ensembles of deep lstm learners for activity recognition using wearables. Proc ACM Interact Mob Wearable Ubiquitous Technol 1(2):1\u201328","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"issue":"3","key":"11363_CR12","doi-asserted-by":"publisher","first-page":"5687","DOI":"10.3390\/s140305687","volume":"14","author":"JJ Guiry","year":"2014","unstructured":"Guiry JJ, Van de Ven P, Nelson J (2014) Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices. Sensors 14(3):5687\u20135701","journal-title":"Sensors"},{"key":"11363_CR13","doi-asserted-by":"crossref","unstructured":"Ha S, Choi S (2016) Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In\u00a02016 Int Joint Conf Neural Netw (IJCNN) IEEE 381\u2013388","DOI":"10.1109\/IJCNN.2016.7727224"},{"key":"11363_CR14","unstructured":"Hammerla NY, Halloran S, Pl\u00f6tz T (2016) Deep, convolutional, and recurrent models for human activity recognition using wearables. In Proc 25th Int Joint Conf Artif Intell AAAI Press IJCAI\u201916 1533\u20131540"},{"key":"11363_CR15","doi-asserted-by":"crossref","unstructured":"Haque MN, Tonmoy MTH, Mahmud S, Ali AA, Khan MAH, Shoyaib M (2019) Gru-based attention mechanism for human activity recognition. In\u00a02019 1st Int Conf Adv Sci Eng Robot Technol (ICASERT) IEEE 1\u20136","DOI":"10.1109\/ICASERT.2019.8934659"},{"key":"11363_CR16","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez F, Su\u00e1rez LF, Villamizar J, Altuve M (2019) Human activity recognition on smartphones using a bidirectional lstm network. In\u00a02019 XXII Symp Image Signal Proc Artif Vis (STSIVA) IEEE 1\u20135","DOI":"10.1109\/STSIVA.2019.8730249"},{"key":"11363_CR17","doi-asserted-by":"crossref","unstructured":"Huynh T, Schiele B (2005) Analyzing features for activity recognition. In Proceedings of the 2005\u00a0Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-Aware Services: Usages and Technologies 159\u2013163","DOI":"10.1145\/1107548.1107591"},{"key":"11363_CR18","doi-asserted-by":"crossref","unstructured":"Huynh T, Fritz M, Schiele B (2008) Discovery of activity patterns using topic models. In Proc 10th Int Conf Ubiquitous Comput 10\u201319","DOI":"10.1145\/1409635.1409638"},{"issue":"1","key":"11363_CR19","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/LGRS.2015.2491329","volume":"13","author":"Y Kim","year":"2015","unstructured":"Kim Y, Moon T (2015) Human detection and activity classification based on micro-doppler signatures using deep convolutional neural networks. IEEE Geosci Remote Sens Lett 13(1):8\u201312","journal-title":"IEEE geoscience and remote sensing letters"},{"key":"11363_CR20","unstructured":"Lee SM, Yoon SM, Cho H (2017) Human activity recognition from accelerometer data using convolutional neural network. In\u00a02017 IEEE Int Conf Big Data Smart Comput (BigComp) IEEE 131\u2013134"},{"key":"11363_CR21","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.knosys.2015.09.024","volume":"90","author":"L Liu","year":"2015","unstructured":"Liu L, Peng Y, Liu M, Huang Z (2015) Sensor-based human activity recognition system with a multilayered model using time series shapelets. Knowl Based Syst 90:138\u2013152","journal-title":"Knowledge-Based Systems"},{"key":"11363_CR22","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.ins.2016.01.020","volume":"340","author":"L Liu","year":"2016","unstructured":"Liu L, Peng Y, Wang S, Liu M, Huang Z (2016) Complex activity recognition using time series pattern dictionary learned from ubiquitous sensors. Inf Sci 340:41\u201357","journal-title":"Information Sciences"},{"key":"11363_CR23","doi-asserted-by":"crossref","unstructured":"Liu J, Shahroudy A, Xu D, Wang G (2016b) Spatio-temporal lstm with trust gates for 3d human action recognition. In Eur Conf Comput Vis Springer 816\u2013833","DOI":"10.1007\/978-3-319-46487-9_50"},{"key":"11363_CR24","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.neucom.2019.06.051","volume":"362","author":"M Lv","year":"2019","unstructured":"Lv M, Xu W, Chen T (2019) A hybrid deep convolutional and recurrent neural network for complex activity recognition using multimodal sensors. Neurocomputing 362:33\u201340","journal-title":"Neurocomputing"},{"key":"11363_CR25","doi-asserted-by":"crossref","unstructured":"Mobark M, Chuprat S, Mantoro T (2017) Improving the accuracy of complex activities recognition using accelerometer-embedded mobile phone classifiers. In\u00a02017 2nd Int Conf Inf Comput (ICIC) IEEE 1\u20135","DOI":"10.1109\/IAC.2017.8280606"},{"key":"11363_CR26","doi-asserted-by":"crossref","unstructured":"Murahari VS, Pl\u00f6tz T (2018) On attention models for human activity recognition. In Proc\u00a02018 ACM Int Symp Wearable Comput 100\u2013103","DOI":"10.1145\/3267242.3267287"},{"key":"11363_CR27","doi-asserted-by":"crossref","unstructured":"M\u00fcnzner S, Schmidt P, Reiss A, Hanselmann M, Stiefelhagen R, Durichen R (2017) CNN-based sensor fusion techniques for multimodal human activity recognition. In Proc\u00a02017 ACM Int Symp Wearable Comput 158\u2013165","DOI":"10.1145\/3123021.3123046"},{"issue":"1","key":"11363_CR28","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/s16010115","volume":"16","author":"FJ Ord\u00f3\u00f1ez","year":"2016","unstructured":"Ord\u00f3\u00f1ez FJ, Roggen D (2016) Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1):115","journal-title":"Sensors"},{"issue":"6","key":"11363_CR29","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.1109\/TBME.2016.2604856","volume":"64","author":"L Peng","year":"2016","unstructured":"Peng L, Chen L, Wu X, Guo H, Chen G (2016) Hierarchical complex activity representation and recognition using topic model and classifier level fusion. IEEE Trans Biomed Eng 64(6):1369\u20131379","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"7","key":"11363_CR30","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TMC.2018.2863292","volume":"18","author":"L Peng","year":"2018","unstructured":"Peng L, Chen L, Wu M, Chen G (2018) Complex activity recognition using acceleration, vital sign, and location data. IEEE Trans Mob Comput 18(7):1488\u20131498","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"11363_CR31","doi-asserted-by":"crossref","unstructured":"Reiss A, Hendeby G, Stricker D (2013) Confidence based multiclass adaboost for physical activity monitoring. In Proc\u00a02013 Int Symp Wearable Comput 13\u201320","DOI":"10.1145\/2493988.2494325"},{"issue":"2","key":"11363_CR32","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3390\/informatics5020026","volume":"5","author":"FM Rueda","year":"2018","unstructured":"Rueda FM, Grzeszick R, Fink GA, Feldhorst S, Hompel MT (2018) Convolutional neural networks for human activity recognition using body-worn sensors. Informatics 5(2):26","journal-title":"Informatics"},{"key":"11363_CR33","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In Adv Neural Inf Process Syst 3856\u20133866"},{"issue":"6","key":"11363_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2490832","volume":"20","author":"S Saguna","year":"2013","unstructured":"Saguna S, Zaslavsky A, Chakraborty D (2013) Complex activity recognition using context-driven activity theory and activity signatures. ACM Trans Comput Hum Interact (TOCHI) 20(6):1\u201334","journal-title":"ACM Transactions on Computer-Human Interaction (TOCHI)"},{"key":"11363_CR35","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.pmcj.2014.05.007","volume":"15","author":"J Seiter","year":"2014","unstructured":"Seiter J, Amft O, Rossi M, Tr\u00f6ster G (2014) Discovery of activity composites using topic models: An analysis of unsupervised methods. Pervasive Mob Comput 15:215\u2013227","journal-title":"Pervasive and Mobile Computing"},{"issue":"4","key":"11363_CR36","doi-asserted-by":"publisher","first-page":"426","DOI":"10.3390\/s16040426","volume":"16","author":"M Shoaib","year":"2016","unstructured":"Shoaib M, Bosch S, Incel OD, Scholten H, Havinga PJ (2016) Complex human activity recognition using smartphone and wrist-worn motion sensors. Sensors 16(4):426","journal-title":"Sensors"},{"issue":"2","key":"11363_CR37","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1007\/s11036-019-01445-x","volume":"25","author":"S Wan","year":"2020","unstructured":"Wan S, Qi L, Xu X, Tong C, Gu Z (2020) Deep learning models for real-time human activity recognition with smartphones. Mob Netw Appl 25(2):743\u2013755","journal-title":"Mobile Networks and Applications"},{"key":"11363_CR38","doi-asserted-by":"publisher","first-page":"56750","DOI":"10.1109\/ACCESS.2018.2873315","volume":"6","author":"Z Yang","year":"2018","unstructured":"Yang Z, Raymond OI, Zhang C, Wan Y, Long J (2018) Dfternet: Towards 2-bit dynamic fusion networks for accurate human activity recognition. IEEE Access 6:56750\u201356764","journal-title":"IEEE Access"},{"key":"11363_CR39","doi-asserted-by":"crossref","unstructured":"Yu T, Chen J, Yan N, Liu X (2018a) A multi-layer parallel lstm network for human activity recognition with smartphone sensors. In\u00a02018 10th Int Conf Wireless Commun Signal Process (WCSP) IEEE 1\u20136","DOI":"10.1109\/WCSP.2018.8555945"},{"key":"11363_CR40","doi-asserted-by":"crossref","unstructured":"Yu S, Qin L, Yin Q (2018b) A c-lstm neural network for human activity recognition using wearables. In\u00a02018 Int Symp Sens Instrum IoT Era (ISSI) IEEE 1\u20136","DOI":"10.1109\/ISSI.2018.8538129"},{"key":"11363_CR41","doi-asserted-by":"crossref","unstructured":"Zeng M, Gao H, Yu T, Mengshoel OJ, Langseth H, Lane I, Liu X (2018) Understanding and improving recurrent networks for human activity recognition by continuous attention. In Proc\u00a02018 ACM Int Symp Wearable Comput 56\u201363","DOI":"10.1145\/3267242.3267286"},{"key":"11363_CR42","doi-asserted-by":"crossref","unstructured":"Zheng Z, Shi L, Wang C, Sun L, Pan G (2019) Lstm with uniqueness attention for human activity recognition. In Int Conf Artif Neural Netw Springer 498\u2013509","DOI":"10.1007\/978-3-030-30508-6_40"},{"issue":"7","key":"11363_CR43","doi-asserted-by":"publisher","first-page":"2983","DOI":"10.1109\/TIP.2016.2548241","volume":"25","author":"Z Zuo","year":"2016","unstructured":"Zuo Z, Shuai B, Wang G, Liu X, Wang X, Wang B, Chen Y (2016) Learning contextual dependence with convolutional hierarchical recurrent neural networks. IEEE Trans Image Proces 25(7):2983\u20132996","journal-title":"IEEE Transactions on Image Processing"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11363-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11363-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11363-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T20:57:45Z","timestamp":1638565065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11363-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,1]]},"references-count":43,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["11363"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11363-4","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,9,1]]},"assertion":[{"value":"2 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}