{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T01:30:03Z","timestamp":1778808603010,"version":"3.51.4"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T00:00:00Z","timestamp":1588204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T00:00:00Z","timestamp":1588204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>A challenging key aspect of modelling and recognising human activity is to design a model that can deal with the uncertainty in human behaviour. Several machine learning and deep learning techniques are employed to model the Activity of Daily Living (ADL) representing \nthe human activity. This paper proposes an enhanced Fuzzy Finite State Machine (FFSM) model by combining the classical FFSM with Long Short-Term Memory (LSTM) neural network and Convolutional Neural Network (CNN). The learning capability in the LSTM and CNN allows the system to learn the relationship in the temporal human activity data and to identify the parameters of the rule-based system as building blocks of the FFSM through time steps in the learning mode. The learned parameters are then used for generating the fuzzy rules that govern the transitions between the system\u2019s states representing activities. The proposed enhanced FFSMs were tested and evaluated using two different datasets; a real dataset collected by our research group and a public dataset collected from CASAS smart home project. Using LSTM-FFSM, the experimental results achieved <jats:inline-formula><jats:alternatives><jats:tex-math>$$95.7\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>95.7<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and <jats:inline-formula><jats:alternatives><jats:tex-math>$$97.6\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>97.6<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> for the first dataset and the second dataset, respectively. Once CNN-FFSM was applied to both datasets, the obtained results were <jats:inline-formula><jats:alternatives><jats:tex-math>$$94.2\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>94.2<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and <jats:inline-formula><jats:alternatives><jats:tex-math>$$99.3\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>99.3<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, respectively.<\/jats:p>","DOI":"10.1007\/s12652-020-01917-z","type":"journal-article","created":{"date-parts":[[2020,4,30]],"date-time":"2020-04-30T12:02:42Z","timestamp":1588248162000},"page":"6077-6091","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Enhanced fuzzy finite state machine for human activity modelling and recognition"],"prefix":"10.1007","volume":"11","author":[{"given":"Gadelhag","family":"Mohmed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad","family":"Lotfi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Pourabdollah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"key":"1917_CR1","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.pmcj.2016.05.003","volume":"34","author":"AN Aicha","year":"2017","unstructured":"Aicha AN, Englebienne G, Kr\u00f6se B (2017) Unsupervised visit detection in smart homes. Pervasive Mobile Comput 34:157\u2013167","journal-title":"Pervasive Mobile Comput"},{"key":"1917_CR2","doi-asserted-by":"crossref","unstructured":"Alvarez-Alvarez A, Trivino G, Cord\u00f3n O (2011) Body posture recognition by means of a genetic fuzzy finite state machine. In: IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), pp 60\u201365","DOI":"10.1109\/GEFS.2011.5949493"},{"key":"1917_CR3","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1109\/TFUZZ.2011.2171973","volume":"20","author":"A Alvarez-Alvarez","year":"2012","unstructured":"Alvarez-Alvarez A, Trivino G, Cordon O (2012) Human gait modeling using a genetic fuzzy finite state machine. IEEE Trans Fuzzy Syst 20:205\u2013223","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1917_CR4","doi-asserted-by":"crossref","unstructured":"Ambres O, Trivino G (2012) Gait quality monitoring using an arbitrarily oriented smartphone. In: International Workshop on Ambient Assisted Living, Springer. pp 224\u2013231","DOI":"10.1007\/978-3-642-35395-6_31"},{"key":"1917_CR5","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, New York, pp 216\u2013223","DOI":"10.1007\/978-3-642-35395-6_30"},{"key":"1917_CR6","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.artmed.2019.01.005","volume":"94","author":"D Arifoglu","year":"2019","unstructured":"Arifoglu D, Bouchachia A (2019) Detection of abnormal behaviour for dementia sufferers using convolutional neural networks. Artif Intell Med 94:88\u201395","journal-title":"Artif Intell Med"},{"key":"1917_CR7","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.neucom.2016.05.110","volume":"230","author":"A Benmansour","year":"2017","unstructured":"Benmansour A, Bouchachia A, Feham M (2017) Modeling interaction in multi-resident activities. Neurocomputing 230:133\u2013142","journal-title":"Neurocomputing"},{"key":"1917_CR8","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1016\/j.engappai.2010.05.001","volume":"23","author":"V Bombardier","year":"2010","unstructured":"Bombardier V, Schmitt E (2010) Fuzzy rule classifier: capability for generalization in wood color recognition. Eng Appl Artif Intell 23:978\u2013988","journal-title":"Eng Appl Artif Intell"},{"key":"1917_CR9","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1109\/TSMCC.2012.2198883","volume":"42","author":"L Chen","year":"2012","unstructured":"Chen L, Hoey J, Nugent CD, Cook DJ, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C (Applications and Reviews) 42:790\u2013808","journal-title":"IEEE Trans Syst Man Cybern Part C (Applications and Reviews)"},{"key":"1917_CR10","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1016\/j.patcog.2007.10.022","volume":"41","author":"PC Chung","year":"2008","unstructured":"Chung PC, Liu CD (2008) A daily behavior enabled hidden markov model for human behavior understanding. Pattern Recognit 41:1572\u20131580","journal-title":"Pattern Recognit"},{"key":"1917_CR11","first-page":"1","volume":"2010","author":"DJ Cook","year":"2010","unstructured":"Cook DJ (2010) Learning setting-generalized activity models for smart spaces. IEEE Intell Syst 2010:1","journal-title":"IEEE Intell Syst"},{"key":"1917_CR12","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/MC.2012.328","volume":"46","author":"DJ Cook","year":"2013","unstructured":"Cook DJ, Crandall AS, Thomas BL, Krishnan NC (2013) Casas: a smart home in a box. Computer 46:62\u201369","journal-title":"Computer"},{"key":"1917_CR13","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1109\/TFUZZ.2016.2574915","volume":"25","author":"Y Deng","year":"2017","unstructured":"Deng Y, Ren Z, Kong Y, Bao F, Dai Q (2017) A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans Fuzzy Syst 25:1006\u20131012","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1917_CR14","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1093\/molbev\/msy224","volume":"36","author":"L Flagel","year":"2018","unstructured":"Flagel L, Brandvain Y, Schrider DR (2018) The unreasonable effectiveness of convolutional neural networks in population genetic inference. Mol Biol Evol 36:220\u2013238","journal-title":"Mol Biol Evol"},{"key":"1917_CR15","first-page":"693","volume":"23","author":"M Gochoo","year":"2019","unstructured":"Gochoo M, Tan TH, Liu SH, Jean FR, Alnajjar FS, Huang SC (2019) Unobtrusive activity recognition of elderly people living alone using anonymous binary sensors and dcnn. IEEE J Biomed Health Inform 23:693\u2013702","journal-title":"IEEE J Biomed Health Inform"},{"key":"1917_CR16","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. Future Gen Comput Syst 81:307\u2013313","journal-title":"Future Gen Comput Syst"},{"key":"1917_CR17","doi-asserted-by":"crossref","unstructured":"Jenckel M, Parkala SS, Bukhari SS, Dengel A (2018) Impact of training lstm-rnn with fuzzy ground truth. In: ICPRAM, IEEE. pp 388\u2013393","DOI":"10.5220\/0006592703880393"},{"key":"1917_CR18","doi-asserted-by":"crossref","unstructured":"Khemchandani R, Sharma S (2017) Robust parametric twin support vector machine and its application in human activity recognition. In: Proceedings of International Conference on Computer Vision and Image Processing, Springer, Ne York. pp 193\u2013203","DOI":"10.1007\/978-981-10-2104-6_18"},{"key":"1917_CR19","unstructured":"Kong Y, Fu Y (2018) Human action recognition and prediction: a survey. arXiv preprint arXiv:1806.11230"},{"key":"1917_CR20","doi-asserted-by":"crossref","unstructured":"Langensiepen C, Lotfi A, Puteh S (2014) Activities recognition and worker profiling in the intelligent office environment using a fuzzy finite state machine. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), pp. 873\u2013880","DOI":"10.1109\/FUZZ-IEEE.2014.6891825"},{"key":"1917_CR21","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s12652-010-0043-x","volume":"3","author":"A Lotfi","year":"2012","unstructured":"Lotfi A, Langensiepen C, Mahmoud SM, Akhlaghinia MJ (2012) Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour. J Ambient Intell Humaniz Comput 3:205\u2013218","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1917_CR22","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s12652-017-0598-x","volume":"10","author":"LP Malasinghe","year":"2019","unstructured":"Malasinghe LP, Ramzan N, Dahal K (2019) Remote patient monitoring: a comprehensive study. J Ambient Intell Humaniz Comput 10:57\u201376","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1917_CR23","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.eswa.2018.07.068","volume":"114","author":"J Medina-Quero","year":"2018","unstructured":"Medina-Quero J, Zhang S, Nugent C, Espinilla M (2018) Ensemble classifier of long short-term memory with fuzzy temporal windows on binary sensors for activity recognition. Expert Syst Appl 114:441\u2013453","journal-title":"Expert Syst Appl"},{"key":"1917_CR24","doi-asserted-by":"crossref","unstructured":"Mohmed G, Lotfi A, Langensiepen C, Pourabdollah A (2018a) Clustering-based fuzzy finite state machine for human activity recognition. In: UK Workshop on Computational Intelligence, Springer. Springer, New York, pp 264\u2013275","DOI":"10.1007\/978-3-319-97982-3_22"},{"key":"1917_CR25","doi-asserted-by":"publisher","first-page":"110","DOI":"10.3390\/technologies6040110","volume":"6","author":"G Mohmed","year":"2018","unstructured":"Mohmed G, Lotfi A, Pourabdollah A (2018b) Human activities recognition based on neuro-fuzzy finite state machine. Technologies 6:110","journal-title":"Technologies"},{"key":"1917_CR26","doi-asserted-by":"crossref","unstructured":"Mohmed G, Lotfi A, Pourabdollah A (2019) Long short-term memory fuzzy finite state machine for human activity modelling. In: Proceedings of the 12th ACM International Conference on Pervasive Technologies Related to Assistive Environments, Association for Computing Machinery, New York, NY, USA. p 561\u2013567","DOI":"10.1145\/3316782.3322781"},{"key":"1917_CR27","doi-asserted-by":"crossref","unstructured":"Raeiszadeh M, Tahayori H, Visconti A (2019) Discovering varying patterns of normal and interleaved adls in smart homes. Applied Intelligence 1\u201314","DOI":"10.1007\/s10489-019-01493-6"},{"key":"1917_CR28","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1109\/TKDE.2010.148","volume":"23","author":"P Rashidi","year":"2010","unstructured":"Rashidi P, Cook DJ, Holder LB, Schmitter-Edgecombe M (2010) Discovering activities to recognize and track in a smart environment. IEEE Trans Knowl Data Eng 23:527\u2013539","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1917_CR29","doi-asserted-by":"publisher","first-page":"3287","DOI":"10.1007\/s12652-018-1058-y","volume":"10","author":"K Sridhar","year":"2019","unstructured":"Sridhar K, Baskar S, Shakeel PM, Dhulipala VS (2019) Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Humaniz Comput 10:3287\u20133295","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"1917_CR30","doi-asserted-by":"publisher","first-page":"1250028","DOI":"10.1142\/S0129065712500281","volume":"22","author":"K Subramanian","year":"2012","unstructured":"Subramanian K, Suresh S (2012) Human action recognition using meta-cognitive neuro-fuzzy inference system. Int J Neural Syst 22:1250028","journal-title":"Int J Neural Syst"},{"key":"1917_CR31","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1016\/j.procs.2018.07.298","volume":"126","author":"N Tran","year":"2018","unstructured":"Tran N, Nguyen T, Nguyen BM, Nguyen G (2018) A multivariate fuzzy time series resource forecast model for clouds using lstm and data correlation analysis. Procedia Comput Sci 126:636\u2013645","journal-title":"Procedia Comput Sci"},{"key":"1917_CR32","doi-asserted-by":"crossref","unstructured":"Trinh H, Fan Q, Jiyan P, Gabbur P, Miyazawa S, Pankanti S (2011) Detecting human activities in retail surveillance using hierarchical finite state machine. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 1337\u20131340","DOI":"10.1109\/ICASSP.2011.5946659"},{"key":"1917_CR33","unstructured":"Unal FA, Khan E (1994) A fuzzy finite state machine implementation based on a neural fuzzy system. In: Proceedings of the Third IEEE World Congress on Computational Intelligence, pp 1749\u20131754"},{"key":"1917_CR34","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1109\/21.199466","volume":"22","author":"LX Wang","year":"1992","unstructured":"Wang LX, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22:1414\u20131427","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"1917_CR35","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1109\/TITB.2012.2196440","volume":"16","author":"Z Wang","year":"2012","unstructured":"Wang Z, Jiang M, Hu Y, 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 16:691\u2013699","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"1917_CR36","doi-asserted-by":"crossref","unstructured":"Wongpatikaseree K, Ikeda M, Buranarach M, Supnithi T, Lim AO, Tan Y (2012) Activity recognition using context-aware infrastructure ontology in smart home domain. In: 2012 Seventh International Conference on Knowledge, Information and Creativity Support Systems, IEEE, pp 50\u201357","DOI":"10.1109\/KICSS.2012.26"},{"key":"1917_CR37","doi-asserted-by":"crossref","unstructured":"Yulita IN, Fanany MI, Arymurthy AM (2017a) Fuzzy clustering and bidirectional long short-term memory for sleep stages classification. In: International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT), pp 11\u201316","DOI":"10.1109\/ICSIIT.2017.44"},{"key":"1917_CR38","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.procs.2017.10.042","volume":"116","author":"IN Yulita","year":"2017","unstructured":"Yulita IN, Fanany MI, Arymuthy AM (2017b) Bi-directional long short-term memory using quantized data of deep belief networks for sleep stage classification. Procedia Comput Sci 116:530\u2013538","journal-title":"Procedia Comput Sci"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-020-01917-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-020-01917-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-020-01917-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T23:45:04Z","timestamp":1619739904000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-020-01917-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,30]]},"references-count":38,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["1917"],"URL":"https:\/\/doi.org\/10.1007\/s12652-020-01917-z","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,30]]},"assertion":[{"value":"14 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}