{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T05:13:01Z","timestamp":1723698781771},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:00:00Z","timestamp":1676332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:00:00Z","timestamp":1676332800000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00521-023-08307-4","type":"journal-article","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T06:30:56Z","timestamp":1676615456000},"page":"11431-11444","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Physiotherapy-based human activity recognition using deep learning"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-1859-2898","authenticated-orcid":false,"given":"Disha","family":"Deotale","sequence":"first","affiliation":[]},{"given":"Madhushi","family":"Verma","sequence":"additional","affiliation":[]},{"given":"P.","family":"Suresh","sequence":"additional","affiliation":[]},{"given":"Neeraj","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"8307_CR1","doi-asserted-by":"publisher","first-page":"106327","DOI":"10.1016\/j.chb.2020.106327","volume":"108","author":"JS Lim","year":"2020","unstructured":"Lim JS et al (2020) The role of wishful identification, emotional engagement, and parasocial relationships in repeated viewing of live-streaming games: a social cognitive theory perspective. Comput Hum Behav 108:106327","journal-title":"Comput Hum Behav"},{"issue":"3","key":"8307_CR2","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.1109\/COMST.2019.2934489","volume":"22","author":"J Liu","year":"2019","unstructured":"Liu J et al (2019) Wireless sensing for human activity: a survey. IEEE Commun Surv Tutor 22(3):1629\u20131645","journal-title":"IEEE Commun Surv Tutor"},{"issue":"1","key":"8307_CR3","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s10648-019-09514-z","volume":"33","author":"P Goldberg","year":"2021","unstructured":"Goldberg P et al (2021) Attentive or not? Toward a machine learning approach to assessing students\u2019 visible engagement in classroom instruction. Educ Psychol Rev 33(1):27\u201349","journal-title":"Educ Psychol Rev"},{"key":"8307_CR4","volume-title":"IoT sensor-based activity recognition","author":"MAR Ahad","year":"2020","unstructured":"Ahad MAR, Antar AD, Ahmed M (2020) IoT sensor-based activity recognition. Springer"},{"issue":"10","key":"8307_CR5","doi-asserted-by":"publisher","first-page":"3388","DOI":"10.1109\/TPAMI.2020.2981890","volume":"43","author":"K Oksuz","year":"2021","unstructured":"Oksuz K et al (2021) Imbalance problems in object detection: a review. IEEE Trans Pattern Anal Mach Intell 43(10):3388\u20133415","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"41","key":"8307_CR6","doi-asserted-by":"publisher","first-page":"30509","DOI":"10.1007\/s11042-020-09004-3","volume":"79","author":"DR Beddiar","year":"2020","unstructured":"Beddiar DR et al (2020) Vision-based human activity recognition: a survey. Multimed Tools Appl 79(41):30509\u201330555","journal-title":"Multimed Tools Appl"},{"key":"8307_CR7","doi-asserted-by":"crossref","unstructured":"Subasi A et al (2020) Human activity recognition using machine learning methods in a smart healthcare environment. In: Innovation in health informatics. Academic press, pp 123\u2013144","DOI":"10.1016\/B978-0-12-819043-2.00005-8"},{"key":"8307_CR8","doi-asserted-by":"publisher","first-page":"130723","DOI":"10.1016\/j.chemosphere.2021.130723","volume":"280","author":"SM Prasanth","year":"2021","unstructured":"Prasanth SM et al (2021) Application of biomass derived products in Mid-Size automotive industries: a review. Chemosphere 280:130723","journal-title":"Chemosphere"},{"key":"8307_CR9","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"},{"key":"8307_CR10","doi-asserted-by":"publisher","first-page":"13029","DOI":"10.1109\/JSEN.2021.3069927","volume":"21","author":"E Ramanujam","year":"2021","unstructured":"Ramanujam E, Perumal T, Padmavathi S (2021) Human activity recognition with smartphone and wearable sensors using deep learning techniques: a review. IEEE Sens J 21:13029\u201313040","journal-title":"IEEE Sens J"},{"issue":"4","key":"8307_CR11","first-page":"1","volume":"54","author":"K Chen","year":"2021","unstructured":"Chen K et al (2021) Deep learning for sensor-based human activity recognition: overview, challenges, and opportunities. ACM Comput Surv 54(4):1\u201340","journal-title":"ACM Comput Surv"},{"issue":"1","key":"8307_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-017-0432-x","volume":"17","author":"G Biagetti","year":"2018","unstructured":"Biagetti G et al (2018) Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes. Biomed Eng Online 17(1):1\u201318","journal-title":"Biomed Eng Online"},{"key":"8307_CR13","volume-title":"Visual understanding of human activity: towards ambient intelligence in AI-assisted hospitals","author":"S Yeung","year":"2018","unstructured":"Yeung S (2018) Visual understanding of human activity: towards ambient intelligence in AI-assisted hospitals. Stanford University"},{"key":"8307_CR14","doi-asserted-by":"crossref","unstructured":"Reynolds CR, Altmann RA, Allen DN (2021) The problem of bias in psychological assessment. In: Mastering modern psychological testing. Springer, Cham, pp 573\u2013613","DOI":"10.1007\/978-3-030-59455-8_15"},{"key":"8307_CR15","doi-asserted-by":"publisher","first-page":"68985","DOI":"10.1109\/ACCESS.2021.3078184","volume":"9","author":"M Ronald","year":"2021","unstructured":"Ronald M et al (2021) iSPLInception: an inception-ResNet deep learning architecture for human activity recognition. IEEE Access 9:68985\u201369001","journal-title":"IEEE Access"},{"key":"8307_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 et al (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"},{"issue":"7","key":"8307_CR17","doi-asserted-by":"publisher","first-page":"6429","DOI":"10.1109\/JIOT.2020.2985082","volume":"7","author":"X Zhou","year":"2020","unstructured":"Zhou X et al (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":"8307_CR18","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.eswa.2018.03.056","volume":"105","author":"HF Nweke","year":"2018","unstructured":"Nweke HF et al (2018) Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: state of the art and research challenges. Expert Syst Appl 105:233\u2013261","journal-title":"Expert Syst Appl"},{"issue":"11","key":"8307_CR19","doi-asserted-by":"publisher","first-page":"2568","DOI":"10.1109\/TPAMI.2018.2863279","volume":"41","author":"JF Hu","year":"2018","unstructured":"Hu JF et al (2018) Early action prediction by soft regression. IEEE Trans Pattern Anal Mach Intell 41(11):2568\u20132583","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"20","key":"8307_CR20","doi-asserted-by":"publisher","first-page":"15857","DOI":"10.1007\/s00521-018-3889-z","volume":"32","author":"\u00d6 Y\u0131ld\u0131r\u0131m","year":"2020","unstructured":"Y\u0131ld\u0131r\u0131m \u00d6, Baloglu UB, Acharya UR (2020) A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput Appl 32(20):15857\u201315868","journal-title":"Neural Comput Appl"},{"issue":"22","key":"8307_CR21","doi-asserted-by":"publisher","first-page":"13607","DOI":"10.1109\/JSEN.2020.3006386","volume":"20","author":"A Shrestha","year":"2020","unstructured":"Shrestha A et al (2020) Continuous human activity classification from FMCW radar with Bi-LSTM networks. IEEE Sens J 20(22):13607\u201313619","journal-title":"IEEE Sens J"},{"key":"8307_CR22","doi-asserted-by":"publisher","first-page":"85334","DOI":"10.1109\/ACCESS.2021.3088452","volume":"2021","author":"A Gorji","year":"2021","unstructured":"Gorji A et al (2021) On the generalization and reliability of single radar-based human activity recognition. IEEE Access 2021:85334\u201385349","journal-title":"IEEE Access"},{"key":"8307_CR23","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"},{"issue":"3","key":"8307_CR24","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1109\/JSEN.2019.2946095","volume":"20","author":"H Li","year":"2019","unstructured":"Li H et al (2019) Bi-LSTM network for multimodal continuous human activity recognition and fall detection. IEEE Sens J 20(3):1191\u20131201","journal-title":"IEEE Sens J"},{"issue":"3","key":"8307_CR25","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1016\/j.bbe.2020.04.007","volume":"40","author":"M Altuve","year":"2020","unstructured":"Altuve M, PLizarazo P, Villamizar J (2020) Human activity recognition using improved complete ensemble EMD with adaptive noise and long short-term memory neural networks. Biocybern Biomed Eng 40(3):901\u2013909","journal-title":"Biocybern Biomed Eng"},{"key":"8307_CR26","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.cogsys.2020.08.003","volume":"64","author":"S Sreejith","year":"2020","unstructured":"Sreejith S, Nehemiah HK, Kannan A (2020) A classification framework using a diverse intensified strawberry optimized neural network (DISON) for clinical decision-making. Cogn Syst Res 64:98\u2013116","journal-title":"Cogn Syst Res"},{"key":"8307_CR27","doi-asserted-by":"crossref","unstructured":"Chianese A et al (2013) A novel challenge into multimedia cultural heritage: an integrated approach to support cultural information enrichment. In: International conference on signal-image technology and internet-based systems, pp 217\u2013224","DOI":"10.1109\/SITIS.2013.46"},{"key":"8307_CR28","unstructured":"Palma G et al (2013) 3D Non-Local Means denoising via multi-GPU. In: Federated conference on computer science and information systems, pp 495\u2013498"},{"key":"8307_CR29","doi-asserted-by":"publisher","first-page":"2643","DOI":"10.1016\/j.procs.2013.06.001","volume":"18","author":"F Piccialli","year":"2013","unstructured":"Piccialli F, Cuomo S, De Michele P (2013) A regularized MRI image reconstruction based on hessian penalty term on CPU\/GPU systems. Procedia Comput Sci 18:2643\u20132646","journal-title":"Procedia Comput Sci"},{"key":"8307_CR30","doi-asserted-by":"publisher","first-page":"10637","DOI":"10.1007\/s00521-020-04900-z","volume":"33","author":"NQ Kashif","year":"2021","unstructured":"Kashif NQ, Ahmad A, Piccialli F et al (2021) Nature-inspired algorithm-based secure data dissemination framework for smart city networks. Neural Comput Appl 33:10637\u201310656","journal-title":"Neural Comput Appl"},{"issue":"6","key":"8307_CR31","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s00530-020-00677-2","volume":"26","author":"P Verma","year":"2020","unstructured":"Verma P, Sah A, Srivastava R (2020) Deep learning-based multi-modal approach using RGB and skeleton sequences for human activity recognition. Multimed Syst 26(6):671\u2013685","journal-title":"Multimed Syst"},{"issue":"2","key":"8307_CR32","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1109\/JSEN.2020.3015781","volume":"21","author":"T Mahmud","year":"2020","unstructured":"Mahmud T et al (2020) A novel multi-stage training approach for human activity recognition from multimodal wearable sensor data using deep neural network. IEEE Sens J 21(2):1715\u20131726","journal-title":"IEEE Sens J"},{"issue":"7","key":"8307_CR33","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.3390\/s19071716","volume":"19","author":"S Chung","year":"2019","unstructured":"Chung S et al (2019) Sensor data acquisition and multimodal sensor fusion for human activity recognition using deep learning. Sensors 19(7):1716","journal-title":"Sensors"},{"issue":"1","key":"8307_CR34","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/s12652-019-01214-4","volume":"11","author":"O Kerdjidj","year":"2020","unstructured":"Kerdjidj O et al (2020) Fall detection and human activity classification using wearable sensors and compressed sensing. J Ambient Intell Humaniz Comput 11(1):349\u2013361","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"8307_CR35","doi-asserted-by":"crossref","unstructured":"Huang Po-Sen, et al (2014) Kernel methods match deep neural networks on timit. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 205\u2013209","DOI":"10.1109\/ICASSP.2014.6853587"},{"key":"8307_CR36","doi-asserted-by":"publisher","first-page":"114093","DOI":"10.1016\/j.eswa.2020.114093","volume":"166","author":"WS Lima","year":"2021","unstructured":"Lima WS, Bragan\u00e7a HLS, Souto EJP (2021) NOHAR-NOvelty discrete data stream for Human Activity Recognition based on smartphones with inertial sensors. Expert Syst Appl 166:114093","journal-title":"Expert Syst Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08307-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08307-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08307-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:26:54Z","timestamp":1682382414000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08307-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,14]]},"references-count":36,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["8307"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08307-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,14]]},"assertion":[{"value":"28 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}