{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T20:30:16Z","timestamp":1779136216024,"version":"3.51.4"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s12652-022-03752-w","type":"journal-article","created":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T05:02:42Z","timestamp":1645160562000},"page":"12021-12033","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Personalized human activity recognition using deep learning and edge-cloud architecture"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5152-8636","authenticated-orcid":false,"given":"Luay","family":"Alawneh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahmoud","family":"Al-Ayyoub","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziad A.","family":"Al-Sharif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Shatnawi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"3752_CR1","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M et al (2016) Tensorflow: a system for large-scale machine learning. In: 12th USENIX symposium on operating systems design and implementation (OSDI 16), pp 265\u2013283"},{"key":"3752_CR2","doi-asserted-by":"crossref","first-page":"2364","DOI":"10.1016\/j.procs.2020.03.289","volume":"167","author":"P Agarwal","year":"2020","unstructured":"Agarwal P, Alam M (2020) A lightweight deep learning model for human activity recognition on edge devices. Procedia Comput Sci 167:2364\u20132373","journal-title":"Procedia Comput Sci"},{"issue":"9","key":"3752_CR3","doi-asserted-by":"crossref","first-page":"2530","DOI":"10.1109\/TBME.2019.2963816","volume":"67","author":"A Akbari","year":"2020","unstructured":"Akbari A, Jafari R (2020) Personalizing activity recognition models through quantifying different types of uncertainty using wearable sensors. IEEE Trans Biomed Eng 67(9):2530\u20132541","journal-title":"IEEE Trans Biomed Eng"},{"key":"3752_CR4","doi-asserted-by":"crossref","first-page":"10565","DOI":"10.1007\/s12652-020-02865-4","volume":"12","author":"L Alawneh","year":"2021","unstructured":"Alawneh L, Alsarhan T, Al-Zinati M, Al-Ayyoub M, Jararweh Y, Lu H (2021) Enhancing human activity recognition using deep learning and time series augmented data. J Ambient Intell Human Comput 12:10565\u201310580","journal-title":"J Ambient Intell Human Comput"},{"key":"3752_CR5","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, vol 3, p 3"},{"key":"3752_CR6","doi-asserted-by":"crossref","unstructured":"Anjana W, Nirmalie W (2020) Personalised meta-learning for human activity recognition with few-data. In: International conference on innovative techniques and applications of artificial intelligence. Springer, pp 79\u201393","DOI":"10.1007\/978-3-030-63799-6_6"},{"key":"3752_CR7","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.sysarc.2019.02.009","volume":"98","author":"Y Ashkan","year":"2019","unstructured":"Ashkan Y, Caleb F, Tam N, Krishna K, Fatemeh J, Amirreza N, Jian K, Jue JP (2019) All one needs to know about fog computing and related edge computing paradigms: a complete survey. J Syst Archit 98:289\u2013330","journal-title":"J Syst Archit"},{"key":"3752_CR8","doi-asserted-by":"crossref","unstructured":"Bisong E (2019) Google colaboratory. In: Building machine learning and deep learning models on Google Cloud platform. Springer, pp 59\u201364","DOI":"10.1007\/978-1-4842-4470-8_7"},{"issue":"3","key":"3752_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2499621","volume":"46","author":"A Bulling","year":"2014","unstructured":"Bulling A, Blanke U, Schiele B (2014) A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv (CSUR) 46(3):1\u201333","journal-title":"ACM Comput Surv (CSUR)"},{"key":"3752_CR10","doi-asserted-by":"crossref","unstructured":"Cao Y, Chen S, Hou P, Brown D (2015) Fast: a fog computing assisted distributed analytics system to monitor fall for stroke mitigation. In: 2015 IEEE international conference on networking, architecture and storage (NAS). IEEE, pp 2\u201311","DOI":"10.1109\/NAS.2015.7255196"},{"key":"3752_CR11","doi-asserted-by":"crossref","unstructured":"Casale P, Pujol O, Radeva P (2011) Human activity recognition from accelerometer data using a wearable device. In: Iberian conference on pattern recognition and image analysis. Springer, pp 289\u2013296","DOI":"10.1007\/978-3-642-21257-4_36"},{"issue":"7","key":"3752_CR12","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1109\/TNNLS.2015.2441735","volume":"27","author":"X Chang","year":"2015","unstructured":"Chang X, Nie F, Wang S, Yang Y, Zhou X, Zhang C (2015) Compound rank-$$k$$ projections for bilinear analysis. IEEE Trans Neural Netw Learn Syst 27(7):1502\u20131513","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"3752_CR13","doi-asserted-by":"crossref","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 Trans Ind Inform 13(6):3070\u20133080","journal-title":"IEEE Trans Ind Inform"},{"issue":"5","key":"3752_CR14","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1109\/TNNLS.2019.2927224","volume":"31","author":"K Chen","year":"2019","unstructured":"Chen K, Yao L, Zhang D, Wang X, Chang X, Nie F (2019) A semisupervised recurrent convolutional attention model for human activity recognition. IEEE Trans Neural Netw Learn Syst 31(5):1747\u20131756","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3752_CR15","doi-asserted-by":"crossref","unstructured":"Chen Y, Zhong K, Zhang J, Sun Q, Zhao X (2016) Lstm networks for mobile human activity recognition. In: 2016 International conference on artificial intelligence: technologies and applications. Atlantis Press, pp 50\u201353","DOI":"10.2991\/icaita-16.2016.13"},{"key":"3752_CR16","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.engappai.2018.08.014","volume":"77","author":"C Dhiman","year":"2019","unstructured":"Dhiman C, Vishwakarma DK (2019) A review of state-of-the-art techniques for abnormal human activity recognition. Eng Appl Artif Intell 77:21\u201345","journal-title":"Eng Appl Artif Intell"},{"key":"3752_CR17","doi-asserted-by":"crossref","unstructured":"Chun B-G, Ihm S, Maniatis P, Naik M, Patti A (2011) Clonecloud: elastic execution between mobile device and cloud. In: Proceedings of the sixth conference on computer systems, pp 301\u2013314","DOI":"10.1145\/1966445.1966473"},{"issue":"2","key":"3752_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3266142","volume":"19","author":"F Concone","year":"2019","unstructured":"Concone F, Re GL, Morana M (2019) A fog-based application for human activity recognition using personal smart devices. ACM Trans Internet Technol (TOIT) 19(2):1\u201320","journal-title":"ACM Trans Internet Technol (TOIT)"},{"key":"3752_CR19","doi-asserted-by":"crossref","unstructured":"Daniel WK (2014) Challenges on privacy and reliability in cloud computing security. In: 2014 international conference on information science, electronics and electrical engineering, vol 2. IEEE, pp 1181\u20131187","DOI":"10.1109\/InfoSEEE.2014.6947857"},{"issue":"4","key":"3752_CR20","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"HI Fawaz","year":"2019","unstructured":"Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller P-A (2019) Deep learning for time series classification: a review. Data Min Knowl Discov 33(4):917\u2013963","journal-title":"Data Min Knowl Discov"},{"key":"3752_CR21","doi-asserted-by":"crossref","first-page":"32066","DOI":"10.1109\/ACCESS.2020.2973425","volume":"8","author":"A Ferrari","year":"2020","unstructured":"Ferrari A, Micucci D, Mobilio M, Napoletano P (2020) On the personalization of classification models for human activity recognition. IEEE Access 8:32066\u201332079","journal-title":"IEEE Access"},{"key":"3752_CR22","first-page":"115","volume":"3","author":"FA Gers","year":"2002","unstructured":"Gers FA, Schraudolph NN, Schmidhuber J (2002) Learning precise timing with lstm recurrent networks. J Mach Learn Res 3:115\u2013143","journal-title":"J Mach Learn Res"},{"issue":"1","key":"3752_CR23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-13993-7","volume":"11","author":"N Golestani","year":"2020","unstructured":"Golestani N, Moghaddam M (2020) Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks. Nat Commun 11(1):1\u201311","journal-title":"Nat Commun"},{"key":"3752_CR24","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.future.2016.09.006","volume":"75","author":"R Gravina","year":"2017","unstructured":"Gravina R, Ma C, Pace P, Aloi G, Russo W, Li W, Fortino G (2017) Cloud-based activity-aaservice cyber-physical framework for human activity monitoring in mobility. Future Gener Comput Syst 75:158\u2013171","journal-title":"Future Gener Comput Syst"},{"key":"3752_CR25","unstructured":"Han J, Bhanu B (2005) Human activity recognition in thermal infrared imagery. In: 2005 IEEE Computer Society conference on computer vision and pattern recognition (CVPR\u201905)-workshops. IEEE, p 17"},{"key":"3752_CR26","doi-asserted-by":"crossref","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. Future Gener Comput Syst 81:307\u2013313","journal-title":"Future Gener Comput Syst"},{"issue":"8","key":"3752_CR27","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"3752_CR28","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/S0925-2312(01)00706-8","volume":"50","author":"M H\u00fcsken","year":"2003","unstructured":"H\u00fcsken M, Stagge P (2003) Recurrent neural networks for time series classification. Neurocomputing 50:223\u2013235","journal-title":"Neurocomputing"},{"key":"3752_CR29","doi-asserted-by":"crossref","unstructured":"Jayaraman PP, Gomes JB, Nguyen HL, Abdallah ZS, Krishnaswamy S, Zaslavsky A (2014) Cardap: a scalable energy-efficient context aware distributed mobile data analytics platform for the fog. In: East European conference on advances in databases and information systems. Springer, pp 192\u2013206","DOI":"10.1007\/978-3-319-10933-6_15"},{"key":"3752_CR30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2021.04.062","volume":"575","author":"S Jha","year":"2021","unstructured":"Jha S, Schiemer M, Zambonelli F, Ye J (2021) Continual learning in sensor-based human activity recognition: an empirical benchmark analysis. Inf Sci 575:1\u201321","journal-title":"Inf Sci"},{"key":"3752_CR31","doi-asserted-by":"crossref","unstructured":"Ketkar N (2017) Introduction to keras. In: Deep learning with Python. Springer, pp 97\u2013111","DOI":"10.1007\/978-1-4842-2766-4_7"},{"issue":"1","key":"3752_CR32","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MPRV.2010.7","volume":"9","author":"E Kim","year":"2009","unstructured":"Kim E, Helal S, Cook D (2009) Human activity recognition and pattern discovery. IEEE Pervasive Comput 9(1):48\u201353","journal-title":"IEEE Pervasive Comput"},{"key":"3752_CR33","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"3752_CR34","doi-asserted-by":"crossref","unstructured":"Lehrig S, Eikerling H, Becker S (2015) Scalability, elasticity, and efficiency in cloud computing: a systematic literature review of definitions and metrics. In: Proceedings of the 11th international ACM SIGSOFT conference on quality of software architectures, pp 83\u201392","DOI":"10.1145\/2737182.2737185"},{"issue":"12","key":"3752_CR35","doi-asserted-by":"crossref","first-page":"6073","DOI":"10.1109\/TNNLS.2018.2817538","volume":"29","author":"Z Li","year":"2018","unstructured":"Li Z, Nie F, Chang X, Nie L, Zhang H, Yang Y (2018) Rank-constrained spectral clustering with flexible embedding. IEEE Trans Neural Netw Learn Syst 29(12):6073\u20136082","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"12","key":"3752_CR36","doi-asserted-by":"crossref","first-page":"6323","DOI":"10.1109\/TNNLS.2018.2829867","volume":"29","author":"Z Li","year":"2018","unstructured":"Li Z, Nie F, Chang X, Yang Y, Zhang C, Sebe N (2018) Dynamic affinity graph construction for spectral clustering using multiple features. IEEE Trans Neural Netw Learn Syst 29(12):6323\u20136332","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"9","key":"3752_CR37","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.3390\/rs11091068","volume":"11","author":"X Li","year":"2019","unstructured":"Li X, He Y, Jing X (2019) A survey of deep learning-based human activity recognition in radar. Remote Sens 11(9):1068","journal-title":"Remote Sens"},{"key":"3752_CR38","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.patcog.2018.12.010","volume":"88","author":"Z Li","year":"2019","unstructured":"Li Z, Yao L, Chang X, Zhan K, Sun J, Zhang H (2019) Zero-shot event detection via event-adaptive concept relevance mining. Pattern Recogn 88:595\u2013603","journal-title":"Pattern Recogn"},{"issue":"6","key":"3752_CR39","doi-asserted-by":"crossref","first-page":"4788","DOI":"10.1109\/TIE.2018.2864702","volume":"66","author":"C-L Liu","year":"2018","unstructured":"Liu C-L, Hsaio W-H, Yao-Chung T (2018) Time series classification with multivariate convolutional neural network. IEEE Trans Ind Electron 66(6):4788\u20134797","journal-title":"IEEE Trans Ind Electron"},{"key":"3752_CR40","doi-asserted-by":"crossref","unstructured":"Lockhart JW, Pulickal T, Weiss GM (2012) Applications of mobile activity recognition. In: Proceedings of the 2012 ACM conference on ubiquitous computing, pp 1054\u20131058","DOI":"10.1145\/2370216.2370441"},{"key":"3752_CR41","doi-asserted-by":"crossref","unstructured":"Maheshwari S, Raychaudhuri D, Seskar I, Bronzino F (2018) Scalability and performance evaluation of edge cloud systems for latency constrained applications. In: 2018 IEEE\/ACM symposium on edge computing (SEC). IEEE, pp 286\u2013299","DOI":"10.1109\/SEC.2018.00028"},{"issue":"4","key":"3752_CR42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3432230","volume":"4","author":"A Mazankiewicz","year":"2020","unstructured":"Mazankiewicz A, B\u00f6hm K, Berg\u00e9s M (2020) Incremental real-time personalization in human activity recognition using domain adaptive batch normalization. Proc ACM Interact Mobile Wearable Ubiquitous Technol 4(4):1\u201320","journal-title":"Proc ACM Interact Mobile Wearable Ubiquitous Technol"},{"key":"3752_CR43","first-page":"64","volume":"5","author":"LR Medsker","year":"2001","unstructured":"Medsker LR, Jain LC (2001) Recurrent neural networks. Des Appl 5:64\u201367","journal-title":"Des Appl"},{"key":"3752_CR44","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.procs.2017.09.066","volume":"114","author":"N Mehdiyev","year":"2017","unstructured":"Mehdiyev N, Lahann J, Emrich A, Enke D, Fettke P, Loos P (2017) Time series classification using deep learning for process planning: a case from the process industry. Procedia Comput Sci 114:242\u2013249","journal-title":"Procedia Comput Sci"},{"issue":"10","key":"3752_CR45","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.3390\/app7101101","volume":"7","author":"D Micucci","year":"2017","unstructured":"Micucci D, Mobilio M, Napoletano P (2017) Unimib shar: a dataset for human activity recognition using acceleration data from smartphones. Appl Sci 7(10):1101","journal-title":"Appl Sci"},{"key":"3752_CR46","unstructured":"Nwankpa CE, Ijomah W, Gachagan A, Marshall S (2021) Activation functions: comparison of trends in practice and research for deep learning. In: 2nd International Conference on Computational Sciences and Technology, pp 124\u2013133"},{"key":"3752_CR47","doi-asserted-by":"crossref","unstructured":"Ogbuabor G, La R (2018) Human activity recognition for healthcare using smartphones. In: Proceedings of the 2018 10th international conference on machine learning and computing, pp 41\u201346","DOI":"10.1145\/3195106.3195157"},{"key":"3752_CR48","unstructured":"Pl\u00f6tz T, Hammerla NY, Olivier PL (2011) Feature learning for activity recognition in ubiquitous computing. In: Twenty-second international joint conference on artificial intelligence"},{"issue":"4","key":"3752_CR49","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1109\/5326.983933","volume":"31","author":"R Polikar","year":"2001","unstructured":"Polikar R, Upda L, Upda SS, Honavar V (2001) Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans Syst Man Cybern Part C (Appl Rev) 31(4):497\u2013508","journal-title":"IEEE Trans Syst Man Cybern Part C (Appl Rev)"},{"issue":"1","key":"3752_CR50","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/JBHI.2016.2633287","volume":"21","author":"D Ravi","year":"2016","unstructured":"Ravi D, Wong C, Lo B, Yang G-Z (2016) A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE J Biomed Health Inform 21(1):56\u201364","journal-title":"IEEE J Biomed Health Inform"},{"issue":"6","key":"3752_CR51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3362031","volume":"52","author":"J Ren","year":"2019","unstructured":"Ren J, Zhang D, He S, Zhang Y, Li T (2019) A survey on end-edge-cloud orchestrated network computing paradigms: transparent computing, mobile edge computing, fog computing, and cloudlet. ACM Comput Surv (CSUR) 52(6):1\u201336","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"4","key":"3752_CR52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3447582","volume":"54","author":"P Ren","year":"2021","unstructured":"Ren P, Xiao Y, Chang X, Huang P-Y, Li Z, Chen X, Wang X (2021) A comprehensive survey of neural architecture search: challenges and solutions. ACM Comput Surv (CSUR) 54(4):1\u201334","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"2\u20133","key":"3752_CR53","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.cviu.2006.07.006","volume":"104","author":"N Robertson","year":"2006","unstructured":"Robertson N, Reid I (2006) A general method for human activity recognition in video. Comput Vis Image Underst 104(2\u20133):232\u2013248","journal-title":"Comput Vis Image Underst"},{"key":"3752_CR54","doi-asserted-by":"crossref","unstructured":"Rokni SA, Nourollahi M, Ghasemzadeh H (2018) Personalized human activity recognition using convolutional neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.12185"},{"key":"3752_CR55","doi-asserted-by":"crossref","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":"3752_CR56","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.pmcj.2016.01.004","volume":"30","author":"S-S Rub\u00e9n","year":"2016","unstructured":"Rub\u00e9n S-S, Jaime L-T, Beatriz M-G, Pardo JM (2016) Segmenting human activities based on hmms using smartphone inertial sensors. Pervasive Mobile Comput 30:84\u201396","journal-title":"Pervasive Mobile Comput"},{"key":"3752_CR57","doi-asserted-by":"crossref","unstructured":"Sadiq S, Nirmalie W, Stewart M, and Kay C (2017) KNN sampling for personalised human activity recognition. In: International conference on case-based reasoning. Springer, pp 330\u2013344","DOI":"10.1007\/978-3-319-61030-6_23"},{"issue":"1","key":"3752_CR58","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2017.9","volume":"50","author":"M Satyanarayanan","year":"2017","unstructured":"Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30\u201339","journal-title":"Computer"},{"key":"3752_CR59","unstructured":"Shanhe Y, Cheng L, Qun L (2015) A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 workshop on mobile big data, pp 37\u201342"},{"key":"3752_CR60","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. D 404:132306","journal-title":"Phys. D"},{"key":"3752_CR61","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: Proceedings of the 27th international conference on neural information processing systems, pp 568\u2013576"},{"issue":"1","key":"3752_CR62","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"3752_CR63","doi-asserted-by":"crossref","first-page":"133509","DOI":"10.1109\/ACCESS.2019.2941836","volume":"7","author":"Z Tahmina","year":"2019","unstructured":"Tahmina Z, Scully PJ, Niels P, Casson AJ, Ozanyan KB (2019) Design and implementation of a convolutional neural network on an edge computing smartphone for human activity recognition. IEEE Access 7:133509\u2013133520","journal-title":"IEEE Access"},{"key":"3752_CR64","doi-asserted-by":"crossref","unstructured":"Vepakomma P, De D, Das SK, Bhansali S (2015) A-wristocracy: deep learning on wrist-worn sensing for recognition of user complex activities. In: 2015 IEEE 12th International conference on wearable and implantable body sensor networks (BSN). IEEE, pp 1\u20136","DOI":"10.1109\/BSN.2015.7299406"},{"key":"3752_CR65","doi-asserted-by":"crossref","unstructured":"Vu TH, Dang A, Dung L, Wang J-C (2017) Self-gated recurrent neural networks for human activity recognition on wearable devices. In: Proceedings of the on thematic workshops of ACM multimedia 2017, pp 179\u2013185","DOI":"10.1145\/3126686.3126764"},{"issue":"2","key":"3752_CR66","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/s11036-019-01445-x","volume":"25","author":"S Wan","year":"2020","unstructured":"Wan S, Qi L, Xiaolong X, Tong C, Zonghua G (2020) Deep learning models for real-time human activity recognition with smartphones. Mobile Netw Appl 25(2):743\u2013755","journal-title":"Mobile Netw Appl"},{"issue":"4","key":"3752_CR67","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1109\/TITB.2012.2196440","volume":"16","author":"Z Wang","year":"2012","unstructured":"Wang Z, Jiang M, Yaohua H, Li H (2012) An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors. IEEE Trans Inf Technol Biomed 16(4):691\u2013699","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"3752_CR68","doi-asserted-by":"crossref","unstructured":"Wang Z, Yan W, Oates T (2017) Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 international joint conference on neural networks (IJCNN). IEEE, pp 1578\u20131585","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"3752_CR69","doi-asserted-by":"crossref","first-page":"56855","DOI":"10.1109\/ACCESS.2020.2982225","volume":"8","author":"K Xia","year":"2020","unstructured":"Xia K, Huang J, Wang H (2020) LSTM-CNN architecture for human activity recognition. IEEE Access 8:56855\u201356866","journal-title":"IEEE Access"},{"issue":"7","key":"3752_CR70","doi-asserted-by":"crossref","first-page":"6429","DOI":"10.1109\/JIOT.2020.2985082","volume":"7","author":"Z Xiaokang","year":"2020","unstructured":"Xiaokang Z, Wei L, Kevin I, Kai W, Hao W, Yang LT, Qun J (2020) Deep-learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet Things J 7(7):6429\u20136438","journal-title":"IEEE Internet Things J"},{"key":"3752_CR71","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1109\/TIP.2020.3037518","volume":"30","author":"D Yuan","year":"2020","unstructured":"Yuan D, Chang X, Huang P-Y, Liu Q, He Z (2020) Self-supervised deep correlation tracking. IEEE Trans Image Process 30:976\u2013985","journal-title":"IEEE Trans Image Process"},{"key":"3752_CR72","doi-asserted-by":"crossref","unstructured":"Zebin T, Sperrin M, Peek N, Casson AJ (2018) Human activity recognition from inertial sensor time-series using batch normalized deep lstm recurrent networks. In: 2018 40th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 1\u20134","DOI":"10.1109\/EMBC.2018.8513115"},{"key":"3752_CR73","doi-asserted-by":"crossref","unstructured":"Zhou X, Belkin M (2014) Semi-supervised learning. In: Academic press library in signal processing, vol 1, Elsevier, pp 1239\u20131269","DOI":"10.1016\/B978-0-12-396502-8.00022-X"},{"key":"3752_CR74","doi-asserted-by":"crossref","first-page":"12350","DOI":"10.1109\/JIOT.2021.3063504","volume":"8(15)","author":"J Zhu","year":"2021","unstructured":"Zhu J, Lou X, Ye W (2021) Lightweight deep learning model in mobile edge computing for radar-based human activity recognition. IEEE Int Things J 8(15):12350\u201312359","journal-title":"IEEE Int Things J"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-03752-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-022-03752-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-03752-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T16:24:56Z","timestamp":1690215896000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-022-03752-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,18]]},"references-count":74,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["3752"],"URL":"https:\/\/doi.org\/10.1007\/s12652-022-03752-w","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,18]]},"assertion":[{"value":"16 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}