{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T20:53:14Z","timestamp":1778878394772,"version":"3.51.4"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T00:00:00Z","timestamp":1577664000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T00:00:00Z","timestamp":1577664000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mobile Netw Appl"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s11036-019-01445-x","type":"journal-article","created":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T18:02:22Z","timestamp":1577728942000},"page":"743-755","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":504,"title":["Deep Learning Models for Real-time Human Activity Recognition with Smartphones"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9081","authenticated-orcid":false,"given":"Shaohua","family":"Wan","sequence":"first","affiliation":[]},{"given":"Lianyong","family":"Qi","sequence":"additional","affiliation":[]},{"given":"Xiaolong","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Zonghua","family":"Gu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,30]]},"reference":[{"key":"1445_CR1","unstructured":"Gao Z, Xuan H, Zhang H, Wan S, Choo KR (2019) Adaptive fusion and category-level dictionary learning model for multi-view human action recognition. IEEE Internet of Things Journal, pp 1\u20131"},{"key":"1445_CR2","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.patrec.2019.03.021","volume":"123","author":"S Ding","year":"2019","unstructured":"Ding S, Qu S, Xi Y, Sangaiah AK, Wan S (2019) Image caption generation with high-level image features. Pattern Recognition Letters 123:89\u201395","journal-title":"Pattern Recognition Letters"},{"key":"1445_CR3","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.future.2018.04.064","volume":"87","author":"H Gao","year":"2018","unstructured":"Gao H, Huang W, Yang X, Duan Y, Yin Y (2018) Toward service selection for workflow reconfiguration: An interface-based computing solution. Futur Gener Comput Syst 87:298\u2013311","journal-title":"Futur Gener Comput Syst"},{"key":"1445_CR4","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.eswa.2018.03.008","volume":"103","author":"Y Xu","year":"2018","unstructured":"Xu Y, Yin J, Huang J, Yin Y (2018) Hierarchical topic modeling with automatic knowledge mining. Expert Syst Appl 103:106\u2013117","journal-title":"Expert Syst Appl"},{"key":"1445_CR5","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.future.2018.10.054","volume":"93","author":"S Ding","year":"2019","unstructured":"Ding S, Qu S, Xi Y, Wan S (2019) A long video caption generation algorithm for big video data retrieval. Futur Gener Comput Syst 93:583\u2013595","journal-title":"Futur Gener Comput Syst"},{"key":"1445_CR6","doi-asserted-by":"publisher","first-page":"106861","DOI":"10.1016\/j.comnet.2019.106861","volume":"162","author":"R Zhang","year":"2019","unstructured":"Zhang R, Xie P, Wang C, Liu G, Wan S (2019) Classifying transportation mode and speed from trajectory data via deep multi-scale learning. Computer Networks 162:106861","journal-title":"Computer Networks"},{"key":"1445_CR7","doi-asserted-by":"publisher","first-page":"11700","DOI":"10.1109\/ACCESS.2017.2715322","volume":"5","author":"H Gao","year":"2017","unstructured":"Gao H, Duan Y, Miao H, Yin Y (2017) An approach to data consistency checking for the dynamic replacement of service process. IEEE Access 5:11700\u201311711","journal-title":"IEEE Access"},{"key":"1445_CR8","doi-asserted-by":"publisher","first-page":"41337","DOI":"10.1109\/ACCESS.2018.2857703","volume":"6","author":"L He","year":"2018","unstructured":"He L, Chen C, Zhang T, Zhu H, Wan S (2018) Wearable depth camera: monocular depth estimation via sparse optimization under weak supervision. IEEE Access 6:41337\u201341345","journal-title":"IEEE Access"},{"key":"1445_CR9","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.eswa.2016.04.032","volume":"59","author":"CA Ronao","year":"2016","unstructured":"Ronao CA, Cho S-B (2016) Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst Appl 59:235\u2013244","journal-title":"Expert Syst Appl"},{"key":"1445_CR10","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.future.2017.11.029","volume":"81","author":"MM Hassan","year":"2018","unstructured":"Hassan MM, Uddin MZ, Mohamed A, Almogren A (2018) A robust human activity recognition system using smartphone sensors and deep learning. Futur Gener Comput Syst 81:307\u2013313","journal-title":"Futur Gener Comput Syst"},{"key":"1445_CR11","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1016\/j.asoc.2017.09.027","volume":"62","author":"A Ignatov","year":"2018","unstructured":"Ignatov A (2018) Real-time human activity recognition from accelerometer data using convolutional neural networks. Appl Soft Comput 62:915\u2013922","journal-title":"Appl Soft Comput"},{"key":"1445_CR12","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.future.2019.05.035","volume":"100","author":"L Wang","year":"2019","unstructured":"Wang L, Zhen H, Fang X, Wan S, Ding W, Guo Y (2019) A unified two-parallel-branch deep neural network for joint gland contour and segmentation learning. Futur Gener Comput Syst 100:316\u2013324","journal-title":"Futur Gener Comput Syst"},{"key":"1445_CR13","doi-asserted-by":"crossref","unstructured":"Nweke HF, Teh YW, Al-Garadi MA, Alo UR (2018) Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications","DOI":"10.1016\/j.eswa.2018.03.056"},{"key":"1445_CR14","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.patcog.2018.08.006","volume":"85","author":"D-G Lee","year":"2019","unstructured":"Lee D-G, Lee S-W (2019) Prediction of partially observed human activity based on pre-trained deep representation. Pattern Recogn 85:198\u2013206","journal-title":"Pattern Recogn"},{"key":"1445_CR15","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.patcog.2018.07.030","volume":"85","author":"Y Yang","year":"2019","unstructured":"Yang Y, Hou C, Lang Y, Guan D, Huang D, Xu J (2019) Open-set human activity recognition based on micro-doppler signatures. Pattern Recogn 85:60\u201369","journal-title":"Pattern Recogn"},{"key":"1445_CR16","doi-asserted-by":"crossref","unstructured":"Saini R, Kumar P, Roy PP, Dogra DP (2018) A novel framework of continuous human-activity recognition using kinect. Neurocomputing","DOI":"10.1016\/j.neucom.2018.05.042"},{"issue":"1","key":"1445_CR17","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/JSYST.2016.2610188","volume":"12","author":"MUS Khan","year":"2018","unstructured":"Khan MUS, Abbas A, Ali M, Jawad M, Khan SU, Li K, Zomaya AY (2018) On the correlation of sensor location and human activity recognition in body area networks (bans). IEEE Syst J 12(1):82\u201391","journal-title":"IEEE Syst J"},{"key":"1445_CR18","doi-asserted-by":"crossref","unstructured":"Chen Z, Zhang L, Cao Z, Guo J (2018) Distilling the knowledge from handcrafted features for human activity recognition. IEEE Transactions on Industrial Informatics","DOI":"10.1109\/TII.2018.2789925"},{"issue":"6","key":"1445_CR19","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1109\/TMC.2017.2761744","volume":"17","author":"S Khalifa","year":"2018","unstructured":"Khalifa S, Lan G, Hassan M, Seneviratne A, Das SK (2018) Harke: Human activity recognition from kinetic energy harvesting data in wearable devices. IEEE Trans Mob Comput 17(6):1353\u20131368","journal-title":"IEEE Trans Mob Comput"},{"key":"1445_CR20","doi-asserted-by":"crossref","unstructured":"Lv M, Chen L, Chen T, Chen G (2018) Bi-view semi-supervised learning based semantic human activity recognition using accelerometers. IEEE Transactions on Mobile Computing","DOI":"10.1109\/TMC.2018.2793913"},{"key":"1445_CR21","doi-asserted-by":"crossref","unstructured":"Cheng W, Erfani SM, Zhang R, Kotagiri R (2018) Learning datum-wise sampling frequency for energy-efficient human activity recognition. In: AAAI","DOI":"10.1609\/aaai.v32i1.11862"},{"key":"1445_CR22","doi-asserted-by":"crossref","unstructured":"Rokni SA, Nourollahi M, Ghasemzadeh H (2018) Personalized human activity recognition using convolutional neural networks. arXiv:1801.08252","DOI":"10.1609\/aaai.v32i1.12185"},{"key":"1445_CR23","doi-asserted-by":"crossref","unstructured":"Yin Y, Chen L, Xu Y, Wan J, Zhang H, Mai Z (2019) Qos prediction for service recommendation with deep feature learning in edge computing environment. Mobile Networks and Applications, pp 1\u201311","DOI":"10.1007\/s11036-019-01241-7"},{"key":"1445_CR24","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/j.future.2018.12.039","volume":"94","author":"Z Gao","year":"2019","unstructured":"Gao Z, Wang D, Wan S, Zhang H, Wang Y (2019) Cognitive-inspired class-statistic matching with triple-constrain for camera free 3d object retrieval. Futur Gener Comput Syst 94:641\u2013653","journal-title":"Futur Gener Comput Syst"},{"key":"1445_CR25","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.future.2018.08.007","volume":"91","author":"S Wan","year":"2019","unstructured":"Wan S, Zhao Y, Wang T, Gu Z, Abbasi QH, Choo K-KR (2019) Multi-dimensional data indexing and range query processing via voronoi diagram for internet of things. Futur Gener Comput Syst 91:382\u2013391","journal-title":"Futur Gener Comput Syst"},{"key":"1445_CR26","first-page":"1","volume":"99","author":"H Gao","year":"2018","unstructured":"Gao H, Mao S, Huang W, Yang X (2018) Applying probabilistic model checking to financial production risk evaluation and control: A case study of alibaba\u2019s yu\u2019e bao. IEEE Transactions on Computational Social Systems 99:1\u201311","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"1445_CR27","doi-asserted-by":"crossref","unstructured":"Chen Y, Deng S, Ma H, Yin J (2019) Deploying data-intensive applications with multiple services components on edge. Mobile Networks and Applications, pp 1\u201316","DOI":"10.1007\/s11036-019-01245-3"},{"key":"1445_CR28","unstructured":"Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL (2013) A Public Domain Dataset for Human Activity Recognition Using Smartphones. In: Esann"},{"key":"1445_CR29","unstructured":"Ghio A, Oneto L (2014) Byte the bullet: Learning on real-world computing architectures. In: ESANN"},{"key":"1445_CR30","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.pmcj.2016.09.009","volume":"38","author":"MHM Noor","year":"2017","unstructured":"Noor MHM, Salcic Z, Kevin I, Wang K (2017) Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer. Pervasive and Mobile Computing 38:41\u201359","journal-title":"Pervasive and Mobile Computing"},{"key":"1445_CR31","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neucom.2013.05.044","volume":"126","author":"Y-S Lee","year":"2014","unstructured":"Lee Y-S, Cho S-B (2014) Activity recognition with android phone using mixture-of-experts co-trained with labeled and unlabeled data. Neurocomputing 126:106\u2013115","journal-title":"Neurocomputing"},{"issue":"6","key":"1445_CR32","doi-asserted-by":"publisher","first-page":"3070","DOI":"10.1109\/TII.2017.2712746","volume":"13","author":"Z Chen","year":"2017","unstructured":"Chen Z, Zhu Q, Soh YC, Zhang L (2017) Robust human activity recognition using smartphone sensors via ct-pca and online svm. IEEE Transactions on Industrial Informatics 13(6):3070\u2013 3080","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"1445_CR33","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.jpdc.2017.05.007","volume":"118","author":"L Cao","year":"2018","unstructured":"Cao L, Wang Y, Zhang B, Jin Q, Vasilakos AV (2018) Gchar: An efficient group-based context\u2014aware human activity recognition on smartphone. Journal of Parallel and Distributed Computing 118:67\u201380","journal-title":"Journal of Parallel and Distributed Computing"},{"key":"1445_CR34","doi-asserted-by":"crossref","unstructured":"Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: International Conference on Pervasive Computing. Springer, pp 1\u201317","DOI":"10.1007\/978-3-540-24646-6_1"},{"issue":"5","key":"1445_CR35","doi-asserted-by":"publisher","first-page":"e130","DOI":"10.2196\/jmir.2208","volume":"14","author":"W Wu","year":"2012","unstructured":"Wu W, Dasgupta S, Ramirez EE, Peterson C, Norman GJ (2012) Classification accuracies of physical activities using smartphone motion sensors. Journal of Medical Internet Research 14(5):e130","journal-title":"Journal of Medical Internet Research"},{"key":"1445_CR36","doi-asserted-by":"crossref","unstructured":"Zhao Y, Li H, Wan S, Sekuboyina A, Hu X, Tetteh G, Piraud M, Menze B (2019) Knowledge-aided convolutional neural network for small organ segmentation. IEEE Journal of Biomedical and Health Informatics","DOI":"10.1109\/JBHI.2019.2891526"},{"key":"1445_CR37","doi-asserted-by":"publisher","unstructured":"Ding S, Qu S, Xi Y, Wan S (2019) Stimulus-driven and concept-driven analysis for image caption generation, Neurocomputing. https:\/\/doi.org\/10.1016\/j.neucom.2019.04.095","DOI":"10.1016\/j.neucom.2019.04.095"},{"key":"1445_CR38","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.future.2019.01.012","volume":"96","author":"X Xu","year":"2019","unstructured":"Xu X, Xue Y, Qi L, Yuan Y, Zhang X, Umer T, Wan S (2019) An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles. Future Generation Computer Systems 96:89\u2013100","journal-title":"Future Generation Computer Systems"},{"key":"1445_CR39","doi-asserted-by":"crossref","unstructured":"Li W, Liu X, Liu J, Chen P, Wan S, Cui X (2019) On improving the accuracy with auto-encoder on conjunctivitis. Applied Soft Computing, p 105489","DOI":"10.1016\/j.asoc.2019.105489"},{"key":"1445_CR40","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1016\/j.neucom.2015.07.085","volume":"171","author":"J-L Reyes-Ortiz","year":"2016","unstructured":"Reyes-Ortiz J-L, Oneto L, Sam\u00e0 A, Parra X, Anguita D (2016) Transition-aware human activity recognition using smartphones. Neurocomputing 171:754\u2013767","journal-title":"Neurocomputing"},{"issue":"2","key":"1445_CR41","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1145\/1964897.1964918","volume":"12","author":"JR Kwapisz","year":"2011","unstructured":"Kwapisz JR, Weiss GM, Moore SA (2011) Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter 12(2):74\u201382","journal-title":"ACM SigKDD Explorations Newsletter"},{"key":"1445_CR42","doi-asserted-by":"crossref","unstructured":"Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL (2012) Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: International workshop on ambient assisted living, Springer, pp 216\u2013 223","DOI":"10.1007\/978-3-642-35395-6_30"},{"key":"1445_CR43","doi-asserted-by":"publisher","unstructured":"Wan S, Gu Z, Ni Q (2019) Cognitive computing and wireless communications on the edge for healthcare service robots. Computer Communications. https:\/\/doi.org\/10.1016\/j.comcom.2019.10.012","DOI":"10.1016\/j.comcom.2019.10.012"},{"key":"1445_CR44","doi-asserted-by":"publisher","first-page":"3095","DOI":"10.1109\/ACCESS.2017.2676168","volume":"5","author":"Y Chen","year":"2017","unstructured":"Chen Y, Shen C (2017) Performance analysis of smartphone-sensor behavior for human activity recognition. IEEE Access 5:3095\u20133110","journal-title":"IEEE Access"}],"container-title":["Mobile Networks and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-019-01445-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11036-019-01445-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-019-01445-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T14:49:41Z","timestamp":1665326981000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11036-019-01445-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,30]]},"references-count":44,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["1445"],"URL":"https:\/\/doi.org\/10.1007\/s11036-019-01445-x","relation":{},"ISSN":["1383-469X","1572-8153"],"issn-type":[{"value":"1383-469X","type":"print"},{"value":"1572-8153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,30]]},"assertion":[{"value":"30 December 2019","order":1,"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 would like to declare that there are no conflicts of interest with any third party.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}}]}}