乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T13:44:38Z","timestamp":1744638278098,"version":"3.37.3"},"reference-count":236,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["2020R1A6A1A03038540"],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["2019-0-00136"],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1016\/j.patcog.2020.107561","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:53:31Z","timestamp":1595465611000},"page":"107561","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":419,"special_numbering":"C","title":["Sensor-based and vision-based human activity recognition: A comprehensive survey"],"prefix":"10.1016","volume":"108","author":[{"given":"L.","family":"Minh Dang","sequence":"first","affiliation":[]},{"given":"Kyungbok","family":"Min","sequence":"additional","affiliation":[]},{"given":"Hanxiang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Md.","family":"Jalil Piran","sequence":"additional","affiliation":[]},{"given":"Cheol","family":"Hee Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5576-8960","authenticated-orcid":false,"given":"Hyeonjoon","family":"Moon","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2020.107561_bib0001","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.neucom.2015.08.096","article-title":"From action to activity: sensor-based activity recognition","volume":"181","author":"Liu","year":"2016","journal-title":"Neurocomputing"},{"key":"10.1016\/j.patcog.2020.107561_bib0002","series-title":"Advances in Computational Intelligence Systems","first-page":"341","article-title":"Vision based human activity recognition: a review","author":"Bux","year":"2017"},{"key":"10.1016\/j.patcog.2020.107561_bib0003","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.eswa.2019.04.005","article-title":"Face image manipulation detection based on a convolutional neural network","volume":"129","author":"Dang","year":"2019","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"10.1016\/j.patcog.2020.107561_bib0004","first-page":"22","article-title":"Video based human activity detection, recognition and classification of actions using SVM","volume":"6","author":"Jagadeesh","year":"2019","journal-title":"Trans. Mach. Learn. Artif.Intell."},{"key":"10.1016\/j.patcog.2020.107561_bib0005","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"6047","article-title":"AVA: A video dataset of spatio-temporally localized atomic visual actions","author":"Gu","year":"2018"},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0006","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1080\/10447318.2017.1331536","article-title":"Monitoring for awareness and reflection in ubiquitous learning environments","volume":"34","author":"Mu\u00f1oz-Crist\u00f3bal","year":"2018","journal-title":"Int. J. Hum.\u2013Comput.Interact."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0007","first-page":"11","article-title":"Joint amplitude and frequency analysis of tremor activity.","volume":"39","author":"Foerster","year":"1999","journal-title":"Electromyogr. Clin. Neurophysiol."},{"key":"10.1016\/j.patcog.2020.107561_bib0008","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.sigpro.2017.08.016","article-title":"Skeleton embedded motion body partition for human action recognition using depth sequences","volume":"143","author":"Ji","year":"2018","journal-title":"Signal Process."},{"key":"10.1016\/j.patcog.2020.107561_bib0009","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.patcog.2016.08.003","article-title":"Robust human activity recognition from depth video using spatiotemporal multi-fused features","volume":"61","author":"Jalal","year":"2017","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0010","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.eswa.2017.03.052","article-title":"Fuzzy system based human behavior recognition by combining behavior prediction and recognition","volume":"81","author":"Batchuluun","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patcog.2020.107561_bib0011","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.patcog.2017.08.009","article-title":"Hand action detection from ego-centric depth sequences with error-correcting hough transform","volume":"72","author":"Xu","year":"2017","journal-title":"Pattern Recognit."},{"issue":"12","key":"10.1016\/j.patcog.2020.107561_bib0012","doi-asserted-by":"crossref","first-page":"3941","DOI":"10.1007\/s00521-016-2294-8","article-title":"Deep learning in vision-based static hand gesture recognition","volume":"28","author":"Oyedotun","year":"2017","journal-title":"Neural Comput. Appl."},{"issue":"2\u20134","key":"10.1016\/j.patcog.2020.107561_bib0013","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1007\/s11263-016-0957-7","article-title":"Beyond temporal pooling: recurrence and temporal convolutions for gesture recognition in video","volume":"126","author":"Pigou","year":"2018","journal-title":"Int. J. Comput. Vis."},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0014","doi-asserted-by":"crossref","first-page":"e0124414","DOI":"10.1371\/journal.pone.0124414","article-title":"Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients","volume":"10","author":"Capela","year":"2015","journal-title":"PLoS ONE"},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0015","first-page":"5","article-title":"Sensors, vision and networks: from video surveillance to activity recognition and health monitoring","volume":"11","author":"Prati","year":"2019","journal-title":"J. Ambient Intell. Smart Environ."},{"issue":"9","key":"10.1016\/j.patcog.2020.107561_bib0016","doi-asserted-by":"crossref","first-page":"3900","DOI":"10.5958\/0974-360X.2018.00715.1","article-title":"Internet of things based ambient assisted living for elderly people health monitoring","volume":"11","author":"Sankar","year":"2018","journal-title":"Res. J. Pharm. Technol."},{"key":"10.1016\/j.patcog.2020.107561_bib0017","doi-asserted-by":"crossref","first-page":"5262","DOI":"10.1109\/ACCESS.2017.2684913","article-title":"Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering","volume":"5","author":"Zdravevski","year":"2017","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0018","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1109\/JIOT.2018.2846359","article-title":"A hybrid hierarchical framework for gym physical activity recognition and measurement using wearable sensors","volume":"6","author":"Qi","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.patcog.2020.107561_bib0019","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.patrec.2019.06.029","article-title":"Granger-causality: an efficient single user movement recognition using a smartphone accelerometer sensor","volume":"125","author":"Aviles-Cruz","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patcog.2020.107561_bib0020","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1111\/obr.12506","article-title":"Smart approaches for assessing free-living energy expenditure following identification of types of physical activity","volume":"18","author":"Plasqui","year":"2017","journal-title":"Obes. Rev."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0021","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s10586-017-0977-2","article-title":"Wearable sensor devices for early detection of alzheimer disease using dynamic time warping algorithm","volume":"21","author":"Varatharajan","year":"2018","journal-title":"Cluster Comput."},{"issue":"5","key":"10.1016\/j.patcog.2020.107561_bib0022","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1080\/02640414.2018.1521769","article-title":"Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance","volume":"37","author":"Cust","year":"2019","journal-title":"J. Sports Sci."},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0023","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1145\/3158645","article-title":"Activity recognition with evolving data streams: areview","volume":"51","author":"Abdallah","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"10.1016\/j.patcog.2020.107561_bib0024","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.imavis.2017.01.010","article-title":"Going deeper into action recognition: a survey","volume":"60","author":"Herath","year":"2017","journal-title":"Image Vis. Comput."},{"issue":"5","key":"10.1016\/j.patcog.2020.107561_bib0025","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1109\/TPAMI.2016.2565479","article-title":"Super normal vector for human activity recognition with depth cameras","volume":"39","author":"Yang","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2020.107561_bib0026","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4236\/etsn.2017.61001","article-title":"Rfid systems in healthcare settings and activity of daily living in smart homes: a review","volume":"6","author":"Alsinglawi","year":"2017","journal-title":"E-Health Telecommun. Syst. Netw."},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0027","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","article-title":"A survey on human activity recognition using wearable sensors","volume":"15","author":"Lara","year":"2012","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0028","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/JSEN.2016.2628346","article-title":"A survey on activity detection and classification using wearable sensors","volume":"17","author":"Cornacchia","year":"2017","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.patcog.2020.107561_bib0029","first-page":"1","article-title":"Human activity recognition in egocentric video using hog, gist and color features","author":"Kumar","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.patcog.2020.107561_bib0030","series-title":"Advances in Soft Computing and Machine Learning in Image Processing","first-page":"277","article-title":"Suspicious and violent activity detection of humans using hog features and SVM classifier in surveillance videos","author":"Roy","year":"2018"},{"key":"10.1016\/j.patcog.2020.107561_bib0031","series-title":"Emerging Research in Electronics, Computer Science and Technology","first-page":"753","article-title":"Anomaly detection in surveillance video using pose estimation","author":"Thyagarajmurthy","year":"2019"},{"issue":"6","key":"10.1016\/j.patcog.2020.107561_sbref0032","doi-asserted-by":"crossref","DOI":"10.1177\/1550147719853987","article-title":"A concise review on sensor signal acquisition and transformation applied to human activity recognition and human\u2013robot interaction","volume":"15","author":"Mart\u00ednez-Villase\u00f1or","year":"2019","journal-title":"International Journal of Distributed Sensor Networks"},{"key":"10.1016\/j.patcog.2020.107561_bib0033","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patcog.2018.07.028","article-title":"Asymmetric 3d convolutional neural networks for action recognition","volume":"85","author":"Yang","year":"2019","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0034","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.patcog.2018.01.025","article-title":"A novel random forests based class incremental learning method for activity recognition","volume":"78","author":"Hu","year":"2018","journal-title":"Pattern Recognit."},{"issue":"6","key":"10.1016\/j.patcog.2020.107561_bib0035","doi-asserted-by":"crossref","first-page":"6955","DOI":"10.1007\/s11042-017-4614-0","article-title":"Action recognition based on hierarchical dynamic bayesian network","volume":"77","author":"Xiao","year":"2018","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_sbref0036","doi-asserted-by":"crossref","DOI":"10.1177\/1550147716683687","article-title":"Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models","volume":"13","author":"Ronao","year":"2017","journal-title":"International Journal of Distributed Sensor Networks"},{"issue":"15","key":"10.1016\/j.patcog.2020.107561_bib0037","doi-asserted-by":"crossref","first-page":"6369","DOI":"10.1109\/JSEN.2018.2845749","article-title":"Activity recognition for incomplete spinal cord injury subjects using hidden Markov models","volume":"18","author":"Sok","year":"2018","journal-title":"IEEE Sens. J."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0038","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s10044-016-0570-y","article-title":"The joint use of sequence features combination and modified weighted SVM for improving daily activity recognition","volume":"21","author":"Abidine","year":"2018","journal-title":"Pattern Anal. Appl."},{"issue":"9","key":"10.1016\/j.patcog.2020.107561_bib0039","doi-asserted-by":"crossref","first-page":"2027","DOI":"10.1177\/1045389X18758183","article-title":"Infrared\u2013ultrasonic sensor fusion for support vector machine\u2013based fall detection","volume":"29","author":"Chen","year":"2018","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"10.1016\/j.patcog.2020.107561_bib0040","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.eswa.2017.12.020","article-title":"The use of machine learning algorithms in recommender systems: a systematic review","volume":"97","author":"Portugal","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patcog.2020.107561_bib0041","doi-asserted-by":"crossref","first-page":"103377","DOI":"10.1016\/j.finel.2019.103377","article-title":"A novel data-driven nonlinear solver for solid mechanics using time series forecasting","volume":"171","author":"Nguyen","year":"2020","journal-title":"Finite Elem. Anal. Des."},{"key":"10.1016\/j.patcog.2020.107561_bib0042","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.patcog.2017.07.013","article-title":"Human action recognition in RGB-D videos using motion sequence information and deep learning","volume":"72","author":"Ijjina","year":"2017","journal-title":"Pattern Recognit."},{"issue":"23","key":"10.1016\/j.patcog.2020.107561_bib0043","doi-asserted-by":"crossref","first-page":"9718","DOI":"10.1109\/JSEN.2018.2866806","article-title":"Multi-resident activity recognition in a smart home using RGB activity image and DCNN","volume":"18","author":"Tan","year":"2018","journal-title":"IEEE Sens. J."},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0044","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/MCI.2018.2840738","article-title":"Recent trends in deep learning based natural language processing","volume":"13","author":"Young","year":"2018","journal-title":"IEEE Comput Intell Mag"},{"key":"10.1016\/j.patcog.2020.107561_bib0045","series-title":"2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)","first-page":"779","article-title":"A two formal languages based model for representing human activities","author":"Angeleas","year":"2016"},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0046","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/TAES.2018.2799758","article-title":"Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities","volume":"54","author":"Seyfio\u011flu","year":"2018","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"10.1016\/j.patcog.2020.107561_bib0047","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.cma.2019.05.052","article-title":"A novel analysis-prediction approach for geometrically nonlinear problems using group method of data handling","volume":"354","author":"Nguyen","year":"2019","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"10.1016\/j.patcog.2020.107561_bib0048","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.inffus.2017.10.006","article-title":"A survey on deep learning for big data","volume":"42","author":"Zhang","year":"2018","journal-title":"Inform. Fusion"},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0049","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1109\/COMST.2018.2844341","article-title":"Deep learning for IoT big data and streaming analytics: a survey","volume":"20","author":"Mohammadi","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0050","doi-asserted-by":"crossref","first-page":"4405","DOI":"10.1007\/s11042-015-3177-1","article-title":"A survey of depth and inertial sensor fusion for human action recognition","volume":"76","author":"Chen","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.patcog.2020.107561_bib0051","doi-asserted-by":"crossref","first-page":"60736","DOI":"10.1109\/ACCESS.2019.2913393","article-title":"Robust human activity recognition using multimodal feature-level fusion","volume":"7","author":"Ehatisham-Ul-Haq","year":"2019","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0052","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1109\/TCSVT.2018.2818407","article-title":"Recognizing distractions for assistive driving by tracking body parts","volume":"29","author":"Billah","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0053","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/TIV.2016.2571067","article-title":"Looking at humans in the age of self-driving and highly automated vehicles","volume":"1","author":"Ohn-Bar","year":"2016","journal-title":"IEEE Trans. Intell. Veh."},{"key":"10.1016\/j.patcog.2020.107561_bib0054","series-title":"2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","first-page":"5660","article-title":"Hybrid approach for human activity recognition by ubiquitous robots","author":"Mojarad","year":"2018"},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0055","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1109\/THMS.2016.2641388","article-title":"From activity recognition to intention recognition for assisted living within smart homes","volume":"47","author":"Rafferty","year":"2017","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0056","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s00779-014-0824-x","article-title":"Dynamic sensor event segmentation for real-time activity recognition in a smart home context","volume":"19","author":"Wan","year":"2015","journal-title":"Pers. Ubiquitous Comput."},{"issue":"7","key":"10.1016\/j.patcog.2020.107561_bib0057","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1002\/tee.22901","article-title":"Group activity recognition with an interaction force based on low-level features","volume":"14","author":"Wateosot","year":"2019","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0058","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.dcan.2015.02.006","article-title":"A review on radio based activity recognition","volume":"1","author":"Wang","year":"2015","journal-title":"Digit. Commun. Netw."},{"issue":"6","key":"10.1016\/j.patcog.2020.107561_bib0059","article-title":"A first stage comparative survey on vision-based human activity recognition","volume":"24","author":"Tsitsoulis","year":"2013","journal-title":"J. AI Tools"},{"key":"10.1016\/j.patcog.2020.107561_bib0060","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.eswa.2016.06.011","article-title":"A survey on using domain and contextual knowledge for human activity recognition in video streams","volume":"63","author":"Onofri","year":"2016","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0061","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.bbe.2017.04.004","article-title":"Physical activity recognition by smartphones, a survey","volume":"37","author":"Morales","year":"2017","journal-title":"Biocybern. Biomed. Eng."},{"key":"10.1016\/j.patcog.2020.107561_bib0062","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.cviu.2018.04.007","article-title":"RGB-D-based human motion recognition with deep learning: a survey","volume":"171","author":"Wang","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0063","first-page":"e1254","article-title":"Recent trends in machine learning for human activity recognition\u2013a survey","volume":"8","author":"Ramasamy Ramamurthy","year":"2018","journal-title":"Wiley Interdiscip. Rev: Data Min. Knowl. Discov."},{"key":"10.1016\/j.patcog.2020.107561_bib0064","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","article-title":"Deep learning for sensor-based activity recognition: a survey","volume":"119","author":"Wang","year":"2019","journal-title":"Pattern Recognit. Lett."},{"issue":"8","key":"10.1016\/j.patcog.2020.107561_bib0065","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.1016\/j.patcog.2015.03.006","article-title":"Semantic human activity recognition: a literature review","volume":"48","author":"Ziaeefard","year":"2015","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0066","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1016\/j.neucom.2015.07.085","article-title":"Transition-aware human activity recognition using smartphones","volume":"171","author":"Reyes-Ortiz","year":"2016","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0067","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/TPAMI.2018.2874455","article-title":"Learning compact features for human activity recognition via probabilistic first-take-all","volume":"42","author":"Ye","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"10.1016\/j.patcog.2020.107561_bib0068","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10916-018-0948-z","article-title":"Human activity recognition from body sensor data using deep learning","volume":"42","author":"Hassan","year":"2018","journal-title":"J. Med. Syst."},{"key":"10.1016\/j.patcog.2020.107561_bib0069","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.future.2017.11.029","article-title":"A robust human activity recognition system using smartphone sensors and deep learning","volume":"81","author":"Hassan","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.patcog.2020.107561_bib0070","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/j.asoc.2017.09.027","article-title":"Real-time human activity recognition from accelerometer data using convolutional neural networks","volume":"62","author":"Ignatov","year":"2018","journal-title":"Appl. Soft Comput."},{"issue":"5","key":"10.1016\/j.patcog.2020.107561_bib0071","doi-asserted-by":"crossref","first-page":"053024","DOI":"10.1117\/1.JEI.25.5.053024","article-title":"Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning","volume":"25","author":"Zhao","year":"2016","journal-title":"J. Electron. Imaging"},{"issue":"7","key":"10.1016\/j.patcog.2020.107561_bib0072","doi-asserted-by":"crossref","first-page":"768","DOI":"10.3390\/electronics8070768","article-title":"A survey on internet of things and cloud computing for healthcare","volume":"8","author":"Dang","year":"2019","journal-title":"Electronics"},{"key":"10.1016\/j.patcog.2020.107561_bib0073","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.patcog.2017.12.024","article-title":"Efficient dense labelling of human activity sequences from wearables using fully convolutional networks","volume":"78","author":"Yao","year":"2018","journal-title":"Pattern Recognit."},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0074","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/JBHI.2017.2734803","article-title":"Automatic recognition of activities of daily living utilizing insole-based and wrist-worn wearable sensors","volume":"22","author":"Hegde","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"5","key":"10.1016\/j.patcog.2020.107561_bib0075","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1109\/JSAC.2017.2679658","article-title":"Device-free human activity recognition using commercial WiFi devices","volume":"35","author":"Wang","year":"2017","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"10.1016\/j.patcog.2020.107561_bib0076","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.jnca.2017.12.010","article-title":"Device-free human localization and tracking with UHF passive RFID tags: a data-driven approach","volume":"104","author":"Ruan","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"10.1016\/j.patcog.2020.107561_bib0077","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.compag.2018.01.008","article-title":"Monitoring drinking behavior in bucket-fed dairy calves using an ear-attached tri-axial accelerometer: a pilot study","volume":"145","author":"Roland","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.patcog.2020.107561_bib0078","first-page":"2","article-title":"When RFID meets deep learning: exploring cognitive intelligence for activity identification","author":"Fan","year":"2019","journal-title":"IEEE Wirel. Commun."},{"issue":"2018","key":"10.1016\/j.patcog.2020.107561_bib0079","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.jbi.2018.09.002","article-title":"Examining sensor-based physical activity recognition and monitoring for healthcare using internet of things: a systematic review","volume":"87","author":"Qi","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.patcog.2020.107561_bib0080","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.neucom.2018.08.033","article-title":"Recognizing multi-resident activities in non-intrusive sensor-based smart homes by formal concept analysis","volume":"318","author":"Hao","year":"2018","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0081","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12652-015-0294-7","article-title":"Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments","volume":"7","author":"Roy","year":"2016","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"issue":"9","key":"10.1016\/j.patcog.2020.107561_bib0082","doi-asserted-by":"crossref","first-page":"2966","DOI":"10.3390\/s18092966","article-title":"A device-independent efficient actigraphy signal-encoding system for applications in monitoring daily human activities and health","volume":"18","author":"Athavale","year":"2018","journal-title":"Sensors"},{"key":"10.1016\/j.patcog.2020.107561_bib0083","series-title":"International Conference on Augmented Cognition","first-page":"416","article-title":"Posture based recognition of the visual focus of attention for adaptive mobile information systems","author":"Westhoven","year":"2016"},{"key":"10.1016\/j.patcog.2020.107561_bib0084","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.inffus.2017.09.014","article-title":"Geometrical kinematic modeling on human motion using method of multi-sensor fusion","volume":"41","author":"Xu","year":"2018","journal-title":"Inf. Fusion"},{"issue":"6","key":"10.1016\/j.patcog.2020.107561_bib0085","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.3390\/s19061480","article-title":"Continuous analysis of running mechanics by means of an integrated INS\/GPS device","volume":"19","author":"Davidson","year":"2019","journal-title":"Sensors"},{"issue":"8","key":"10.1016\/j.patcog.2020.107561_bib0086","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.3390\/s17081738","article-title":"Simultaneous indoor tracking and activity recognition using pyroelectric infrared sensors","volume":"17","author":"Luo","year":"2017","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0087","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3390\/safety5010007","article-title":"Intent to adopt location sharing for logging safety applications","volume":"5","author":"Wempe","year":"2019","journal-title":"Safety"},{"key":"10.1016\/j.patcog.2020.107561_bib0088","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.pmcj.2016.04.007","article-title":"Real-time fluid intake gesture recognition based on batteryless UHF RFID technology","volume":"34","author":"Jayatilaka","year":"2017","journal-title":"Pervasive Mob. Comput."},{"issue":"5","key":"10.1016\/j.patcog.2020.107561_bib0089","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/JIOT.2015.2511805","article-title":"PAWS: Passive human activity recognition based on WiFi ambient signals","volume":"3","author":"Gu","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.patcog.2020.107561_bib0090","first-page":"1","article-title":"Posture transition analysis with barometers: contribution to accelerometer-based algorithms","author":"Rodr\u00edguez-Mart\u00edn","year":"2018","journal-title":"Neural Comput. Appl."},{"issue":"11","key":"10.1016\/j.patcog.2020.107561_bib0091","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1109\/TNSRE.2016.2532844","article-title":"Wearable barometric pressure sensor to improve postural transition recognition of mobility-impaired stroke patients","volume":"24","author":"Masse","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0092","first-page":"693","article-title":"Unobtrusive activity recognition of elderly people living alone using anonymous binary sensors and DCNN","volume":"23","author":"Gochoo","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0093","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/JBHI.2014.2313473","article-title":"Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people","volume":"19","author":"Chernbumroong","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0094","doi-asserted-by":"crossref","first-page":"81","DOI":"10.3390\/ijgi7030081","article-title":"A generalized model for indoor location estimation using environmental sound from human activity recognition","volume":"7","author":"Galv\u00e1n-Tejada","year":"2018","journal-title":"ISPRS Int. J. Geoinf."},{"key":"10.1016\/j.patcog.2020.107561_bib0095","series-title":"International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing","first-page":"675","article-title":"Complex activity recognition using polyphonic sound event detection","author":"Kang","year":"2018"},{"issue":"13","key":"10.1016\/j.patcog.2020.107561_bib0096","doi-asserted-by":"crossref","first-page":"5413","DOI":"10.1109\/JSEN.2018.2834739","article-title":"Log-likelihood clustering-enabled passive RF sensing for residential activity recognition","volume":"18","author":"Li","year":"2018","journal-title":"IEEE Sens. J."},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0097","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1109\/TMC.2018.2841905","article-title":"HuMAn: complex activity recognition with multi-modal multi-positional body sensing","volume":"18","author":"Bharti","year":"2019","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"10","key":"10.1016\/j.patcog.2020.107561_bib0098","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.3390\/app7101101","article-title":"UniMiB SHAR: A dataset for human activity recognition using acceleration data from smartphones","volume":"7","author":"Micucci","year":"2017","journal-title":"Appl. Sci."},{"key":"10.1016\/j.patcog.2020.107561_bib0099","series-title":"2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)","first-page":"1","article-title":"On-body localization of wearable devices: an investigation of position-aware activity recognition","author":"Sztyler","year":"2016"},{"key":"10.1016\/j.patcog.2020.107561_bib0100","series-title":"Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems","first-page":"127","article-title":"Smart devices are different: assessing and mitigatingmobile sensing heterogeneities for activity recognition","author":"Stisen","year":"2015"},{"key":"10.1016\/j.patcog.2020.107561_bib0101","series-title":"2015 23rd European Signal Processing Conference (EUSIPCO)","first-page":"2306","article-title":"Daily activity recognition based on DNN using environmental sound and acceleration signals","author":"Hayashi","year":"2015"},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0102","doi-asserted-by":"crossref","first-page":"S6","DOI":"10.1186\/1475-925X-14-S2-S6","article-title":"Design, implementation and validation of a novel open framework for agile development of mobile health applications","volume":"14","author":"Banos","year":"2015","journal-title":"Biomed. Eng. Online"},{"key":"10.1016\/j.patcog.2020.107561_bib0103","series-title":"2013 IEEE International Conference on RFID (RFID)","first-page":"191","article-title":"Sensor enabled wearable RFID technology for mitigating the risk of falls near beds","author":"Torres","year":"2013"},{"key":"10.1016\/j.patcog.2020.107561_bib0104","series-title":"2013 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (Esann)","first-page":"24","article-title":"A public domain dataset for human activity recognition using smartphones.","author":"Anguita","year":"2013"},{"issue":"15","key":"10.1016\/j.patcog.2020.107561_bib0105","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1016\/j.patrec.2012.12.014","article-title":"The opportunity challenge: a benchmark database for on-body sensor-based activity recognition","volume":"34","author":"Chavarriaga","year":"2013","journal-title":"Pattern Recognit. Lett."},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0106","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SigKDD Explor. Newsl."},{"key":"10.1016\/j.patcog.2020.107561_bib0107","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.cviu.2016.10.010","article-title":"Detecting anomalous events in videos by learning deep representations of appearance and motion","volume":"156","author":"Xu","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"issue":"12","key":"10.1016\/j.patcog.2020.107561_bib0108","doi-asserted-by":"crossref","first-page":"5115","DOI":"10.1109\/JSEN.2018.2830743","article-title":"Vision-based human action classification using adaptive boosting algorithm","volume":"18","author":"Zerrouki","year":"2018","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.patcog.2020.107561_bib0109","series-title":"2011 International Conference on Computer Vision","first-page":"2556","article-title":"HMDB: A large video database for human motion recognition","author":"Kuehne","year":"2011"},{"key":"10.1016\/j.patcog.2020.107561_bib0110","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.patcog.2017.10.033","article-title":"Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition","volume":"76","author":"Nunez","year":"2018","journal-title":"Pattern Recognit."},{"issue":"13","key":"10.1016\/j.patcog.2020.107561_bib0111","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.1049\/iet-ipr.2019.0030","article-title":"Human activity recognition using 2d skeleton data and supervised machine learning","volume":"13","author":"Ghazal","year":"2019","journal-title":"IET Image Proc."},{"key":"10.1016\/j.patcog.2020.107561_bib0112","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1155\/2016\/4351435","article-title":"A human activity recognition system using skeleton data from RGBD sensors","volume":"2016","author":"Cippitelli","year":"2016","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.patcog.2020.107561_bib0113","unstructured":"J. Carreira, E. Noland, C. Hillier, A. Zisserman, A short note on the kinetics-700 human action dataset, arXiv:1907.06987(2019)."},{"key":"10.1016\/j.patcog.2020.107561_bib0114","series-title":"IEEE Conference on Computer Vision and Pattern Recognition","article-title":"NTU RGB+D: A large scale dataset for 3d human activity analysis","author":"Shahroudy","year":"2016"},{"key":"10.1016\/j.patcog.2020.107561_bib0115","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"8668","article-title":"HACS: Human action clips and segments dataset for recognition and temporal localization","author":"Zhao","year":"2019"},{"key":"10.1016\/j.patcog.2020.107561_bib0116","first-page":"1","article-title":"Moments in time dataset: one million videos for event understanding","author":"Monfort","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2\u20134","key":"10.1016\/j.patcog.2020.107561_bib0117","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s11263-017-1013-y","article-title":"Every moment counts: dense detailed labeling of actions in complex videos","volume":"126","author":"Yeung","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.patcog.2020.107561_bib0118","series-title":"2017 IEEE International Conference on Computer Vision (ICCV)","first-page":"5843","article-title":"The something something video database for learning and evaluating visual common sense","author":"Goyal","year":"2017"},{"key":"10.1016\/j.patcog.2020.107561_bib0119","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7396","article-title":"Actor and observer: Joint modeling of first and third-person videos","author":"Sigurdsson","year":"2018"},{"key":"10.1016\/j.patcog.2020.107561_bib0120","unstructured":"P. Weinzaepfel, X. Martin, C. Schmid, Human action localization with sparse spatial supervision, arXiv:1605.05197(2016)."},{"key":"10.1016\/j.patcog.2020.107561_bib0121","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"961","article-title":"ActivityNet: A large-scale video benchmark for human activity understanding","author":"Heilbron","year":"2015"},{"key":"10.1016\/j.patcog.2020.107561_bib0122","series-title":"2015 IEEE International Conference on Image Processing (ICIP)","first-page":"168","article-title":"UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor","author":"Chen","year":"2015"},{"key":"10.1016\/j.patcog.2020.107561_bib0123","series-title":"Proceedings of the IEEE conference on Computer Vision and Pattern Recognition","first-page":"1725","article-title":"Large-scale video classification with convolutional neural networks","author":"Karpathy","year":"2014"},{"key":"10.1016\/j.patcog.2020.107561_bib0124","series-title":"2013 IEEE Workshop on Applications of Computer Vision (WACV)","first-page":"53","article-title":"Berkeley MHAD: A comprehensive multimodal human action database","author":"Ofli","year":"2013"},{"issue":"8","key":"10.1016\/j.patcog.2020.107561_bib0125","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1177\/0278364913478446","article-title":"Learning human activities and object affordances from RGB-D videos","volume":"32","author":"Koppula","year":"2013","journal-title":"Int. J. Rob. Res."},{"key":"10.1016\/j.patcog.2020.107561_bib0126","unstructured":"K. Soomro, A.R. Zamir, M. Shah, UCF101: A dataset of 101 human actions classes from videos in the wild, arXiv:1212.0402(2012)."},{"key":"10.1016\/j.patcog.2020.107561_bib0127","series-title":"2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","article-title":"Two-person interaction detection using body-pose features and multiple instance learning","author":"Yun","year":"2012"},{"key":"10.1016\/j.patcog.2020.107561_bib0128","series-title":"2009 IEEE International Conference on Computer Vision (ICCV)","first-page":"2","article-title":"Spatio-temporal relationship match: video structure comparison for recognition of complex human activities.","volume":"vol.\u00a01","author":"Ryoo","year":"2009"},{"key":"10.1016\/j.patcog.2020.107561_bib0129","series-title":"IEEE Conference on Computer Vision & Pattern Recognition","article-title":"Actions in context","author":"Marsza\u0142ek","year":"2009"},{"key":"10.1016\/j.patcog.2020.107561_bib0130","unstructured":"M. M\u00fcller, T. R\u00f6der, M. Clausen, B. Eberhardt, B. Kr\u00fcger, A. Weber, Documentation mocap database HDM05(2007)."},{"key":"10.1016\/j.patcog.2020.107561_bib0131","series-title":"European Conference on Computer Vision","first-page":"510","article-title":"Hollywood in homes: crowdsourcing data collection for activity understanding","author":"Sigurdsson","year":"2016"},{"key":"10.1016\/j.patcog.2020.107561_bib0132","series-title":"2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","first-page":"1","article-title":"From smart to deep: Robust activity recognition on smartwatches using deep learning","author":"Bhattacharya","year":"2016"},{"key":"10.1016\/j.patcog.2020.107561_bib0133","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.orgel.2016.08.025","article-title":"All-inkjet-printed low-pass filters with adjustable cutoff frequency consisting of resistors, inductors and transistors for sensor applications","volume":"38","author":"Castro","year":"2016","journal-title":"Org. Electron."},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0134","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/TMM.2016.2625276","article-title":"Utility-driven adaptive preprocessing for screen content video compression","volume":"19","author":"Wang","year":"2016","journal-title":"IEEE Trans. Multimed."},{"issue":"6\u20138","key":"10.1016\/j.patcog.2020.107561_bib0135","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1007\/s00371-018-1556-0","article-title":"Hand joints-based gesture recognition for noisy dataset using nested interval unscented Kalman filter with LSTM network","volume":"34","author":"Ma","year":"2018","journal-title":"Vis. Comput."},{"issue":"12","key":"10.1016\/j.patcog.2020.107561_bib0136","doi-asserted-by":"crossref","first-page":"7257","DOI":"10.1007\/s11042-015-2643-0","article-title":"Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer","volume":"75","author":"Ignatov","year":"2016","journal-title":"Multimed. Tools Appl."},{"issue":"9","key":"10.1016\/j.patcog.2020.107561_bib0137","doi-asserted-by":"crossref","first-page":"3198","DOI":"10.1109\/JSEN.2016.2519679","article-title":"A triaxial accelerometer-based human activity recognition via EEMD-based features and game-theory-based feature selection","volume":"16","author":"Wang","year":"2016","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.patcog.2020.107561_bib0138","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.future.2018.09.055","article-title":"A semantics-based approach to sensor data segmentation in real-time activity recognition","volume":"93","author":"Triboan","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.patcog.2020.107561_bib0139","series-title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","first-page":"1533","article-title":"Deep, convolutional, and recurrent models for human activity recognition using wearables","author":"Hammerla","year":"2016"},{"key":"10.1016\/j.patcog.2020.107561_bib0140","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.pmcj.2016.09.009","article-title":"Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer","volume":"38","author":"Noor","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"10.1016\/j.patcog.2020.107561_bib0141","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.inffus.2019.06.014","article-title":"Imaging and fusing time series for wearable sensor-based human activity recognition","volume":"53","author":"Qin","year":"2020","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2020.107561_bib0142","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.engappai.2018.01.004","article-title":"A wavelet tensor fuzzy clustering scheme for multi-sensor human activity recognition","volume":"70","author":"He","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.patcog.2020.107561_bib0143","doi-asserted-by":"crossref","first-page":"8682","DOI":"10.1109\/ACCESS.2017.2705644","article-title":"Sensor fault classification based on support vector machine and statistical time-domain features","volume":"5","author":"Jan","year":"2017","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0144","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1109\/JIOT.2018.2823084","article-title":"Locomotion activity recognition using stacked denoising autoencoders","volume":"5","author":"Gu","year":"2018","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0145","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1109\/TVT.2016.2555986","article-title":"Device-free simultaneous wireless localization and activity recognition with wavelet feature","volume":"66","author":"Wang","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.patcog.2020.107561_bib0146","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/5245373","article-title":"Activity feature solving based on TF-IDF for activity recognition in smart homes","volume":"2019","author":"Guo","year":"2019","journal-title":"Complexity"},{"key":"10.1016\/j.patcog.2020.107561_bib0147","series-title":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","first-page":"1064","article-title":"Stacked generalization with wrapper-based feature selection for human activity recognition","author":"Bhavan","year":"2018"},{"key":"10.1016\/j.patcog.2020.107561_bib0148","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.neucom.2017.08.050","article-title":"A genetic programming approach for feature selection in highly dimensional skewed data","volume":"273","author":"Viegas","year":"2018","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0149","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s00371-015-1066-2","article-title":"A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector","volume":"32","author":"Dawn","year":"2016","journal-title":"Vis. Comput."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0150","doi-asserted-by":"crossref","first-page":"013106","DOI":"10.1117\/1.OE.57.1.013106","article-title":"Moving target segmentation using Markov random field-based evaluation metric in infrared videos","volume":"57","author":"Sun","year":"2018","journal-title":"Opt. Eng."},{"key":"10.1016\/j.patcog.2020.107561_bib0151","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.patcog.2017.09.040","article-title":"A deep convolutional neural network for video sequence background subtraction","volume":"76","author":"Babaee","year":"2018","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0152","doi-asserted-by":"crossref","first-page":"107140","DOI":"10.1016\/j.patcog.2019.107140","article-title":"Human activity recognition from UAV-captured video sequences","volume":"100","author":"Mliki","year":"2019","journal-title":"Pattern Recognit."},{"issue":"5","key":"10.1016\/j.patcog.2020.107561_bib0153","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1109\/LSP.2016.2544778","article-title":"Background subtraction using illumination-invariant structural complexity","volume":"23","author":"Kim","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.patcog.2020.107561_bib0154","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.compeleceng.2016.05.017","article-title":"Texture-based self-adaptive moving object detection technique for complex scenes","volume":"70","author":"Goyal","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.patcog.2020.107561_bib0155","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.ins.2016.04.049","article-title":"Statistical feature bag based background subtraction for local change detection","volume":"366","author":"Subudhi","year":"2016","journal-title":"Inf. Sci."},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0156","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/TMC.2015.2418775","article-title":"Real-time and robust compressive background subtraction for embedded camera networks","volume":"15","author":"Shen","year":"2016","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"9","key":"10.1016\/j.patcog.2020.107561_bib0157","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1109\/TCSVT.2017.2711659","article-title":"WeSamBE: A weight-sample-based method for background subtraction","volume":"28","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0158","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/LSP.2015.2498839","article-title":"Detection of moving objects using fuzzy color difference histogram based background subtraction","volume":"23","author":"Panda","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0159","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1109\/TFUZZ.2016.2566811","article-title":"Pixel modeling using histograms based on fuzzy partitions for dynamic background subtraction","volume":"25","author":"Zeng","year":"2017","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"7","key":"10.1016\/j.patcog.2020.107561_bib0160","doi-asserted-by":"crossref","first-page":"92","DOI":"10.3390\/jimaging4070092","article-title":"Background subtraction based on a new fuzzy mixture of gaussians for moving object detection","volume":"4","author":"Darwich","year":"2018","journal-title":"J. Imaging"},{"issue":"17","key":"10.1016\/j.patcog.2020.107561_bib0161","doi-asserted-by":"crossref","first-page":"23023","DOI":"10.1007\/s11042-017-5460-9","article-title":"End-to-end video background subtraction with 3d convolutional neural networks","volume":"77","author":"Sakkos","year":"2018","journal-title":"Multimed. Tools Appl."},{"issue":"6","key":"10.1016\/j.patcog.2020.107561_bib0162","doi-asserted-by":"crossref","first-page":"78","DOI":"10.3390\/jimaging4060078","article-title":"Analytics of deep neural network-based background subtraction","volume":"4","author":"Minematsu","year":"2018","journal-title":"J. Imaging"},{"issue":"7","key":"10.1016\/j.patcog.2020.107561_bib0163","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1109\/TPAMI.2017.2732350","article-title":"Robust online matrix factorization for dynamic background subtraction","volume":"40","author":"Yong","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"10.1016\/j.patcog.2020.107561_bib0164","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1109\/TPAMI.2017.2717828","article-title":"Spatiotemporal GMM for background subtraction with superpixel hierarchy","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"10.1016\/j.patcog.2020.107561_bib0165","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1109\/TCSVT.2017.2697972","article-title":"Background subtraction using spatio-temporal group sparsity recovery","volume":"28","author":"Liu","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2020.107561_bib0166","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.patcog.2017.09.009","article-title":"Real-time nonparametric background subtraction with tracking-based foreground update","volume":"74","author":"Berj\u00f3n","year":"2018","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0167","first-page":"1","article-title":"Human activity recognition via optical flow: decomposing activities into basic actions","author":"Ladjailia","year":"2019","journal-title":"Neural Comput. Appl."},{"issue":"12","key":"10.1016\/j.patcog.2020.107561_bib0168","doi-asserted-by":"crossref","first-page":"9692","DOI":"10.1109\/TIE.2018.2881943","article-title":"Activity recognition using temporal optical flow convolutional features and multi-layer LSTM","volume":"66","author":"Ullah","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"21","key":"10.1016\/j.patcog.2020.107561_bib0169","doi-asserted-by":"crossref","first-page":"30599","DOI":"10.1007\/s11042-018-6425-3","article-title":"Depth based enlarged temporal dimension of 3d deep convolutional network for activity recognition","volume":"78","author":"Singh","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.patcog.2020.107561_bib0170","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1016\/j.patcog.2016.07.024","article-title":"Mining intricate temporal rules for recognizing complex activities of daily living under uncertainty","volume":"60","author":"Liu","year":"2016","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0171","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.engappai.2018.08.014","article-title":"A review of state-of-the-art techniques for abnormal human activity recognition","volume":"77","author":"Dhiman","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"6","key":"10.1016\/j.patcog.2020.107561_bib0172","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.1007\/s42835-019-00278-8","article-title":"Vision-based human activity recognition system using depth silhouettes: a smart home system for monitoring the residents","volume":"14","author":"Kim","year":"2019","journal-title":"J. Electr. Eng. Technol."},{"key":"10.1016\/j.patcog.2020.107561_bib0173","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neucom.2015.03.097","article-title":"Recognizing human actions using novel space-time volume binary patterns","volume":"173","author":"Baumann","year":"2016","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0174","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s00138-014-0652-z","article-title":"Local polynomial space\u2013time descriptors for action classification","volume":"27","author":"Kihl","year":"2016","journal-title":"Mach. Vis. Appl."},{"issue":"10","key":"10.1016\/j.patcog.2020.107561_bib0175","doi-asserted-by":"crossref","first-page":"12645","DOI":"10.1007\/s11042-016-3630-9","article-title":"Sparse coding-based space-time video representation for action recognition","volume":"76","author":"Fu","year":"2017","journal-title":"Multimed. Tools Appl."},{"issue":"10","key":"10.1016\/j.patcog.2020.107561_bib0176","doi-asserted-by":"crossref","first-page":"2123","DOI":"10.1109\/TPAMI.2015.2505295","article-title":"Multimodal multipart learning for action recognition in depth videos","volume":"38","author":"Shahroudy","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0177","doi-asserted-by":"crossref","first-page":"60","DOI":"10.18201\/ijisae.2019151257","article-title":"Human activity recognition on real time and offline dataset","volume":"7","author":"Kale","year":"2019","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"issue":"5","key":"10.1016\/j.patcog.2020.107561_bib0178","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1080\/21681163.2017.1298472","article-title":"Activity representation by SURF-based templates","volume":"6","author":"Ahad","year":"2018","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Visual."},{"key":"10.1016\/j.patcog.2020.107561_bib0179","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.compeleceng.2016.06.004","article-title":"Human action recognition using fusion of features for unconstrained video sequences","volume":"70","author":"Patel","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.patcog.2020.107561_bib0180","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1016\/j.patcog.2017.12.007","article-title":"Motion analysis: action detection, recognition and evaluation based on motion capture data","volume":"76","author":"Patrona","year":"2018","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0181","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.patcog.2018.02.011","article-title":"Structured dynamic time warping for continuous hand trajectory gesture recognition","volume":"80","author":"Tang","year":"2018","journal-title":"Pattern Recognit."},{"issue":"10","key":"10.1016\/j.patcog.2020.107561_bib0182","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.1109\/TMM.2019.2903455","article-title":"Multi-person pose estimation using bounding box constraint and LSTM","volume":"21","author":"Li","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.patcog.2020.107561_bib0183","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.patcog.2017.06.006","article-title":"Generation of human depth images with body part labels for complex human pose recognition","volume":"71","author":"Nishi","year":"2017","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0184","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.patcog.2015.09.005","article-title":"Combining motion and appearance cues for anomaly detection","volume":"51","author":"Zhang","year":"2016","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0185","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.patcog.2017.02.028","article-title":"Towards complex activity recognition using a bayesian network-based probabilistic generative framework","volume":"68","author":"Liu","year":"2017","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0186","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.patcog.2018.04.022","article-title":"Learning structures of interval-based bayesian networks in probabilistic generative model for human complex activity recognition","volume":"81","author":"Liu","year":"2018","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0187","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.autcon.2016.08.015","article-title":"Smartphone-based construction workers\u2019 activity recognition and classification","volume":"71","author":"Akhavian","year":"2016","journal-title":"Autom. Constr."},{"key":"10.1016\/j.patcog.2020.107561_bib0188","series-title":"Proceedings of the 14th International Conference on Advances in Mobile Computing and Multi Media","first-page":"288","article-title":"Accelerometer based gait recognition using adapted gaussian mixture models","author":"Muaaz","year":"2016"},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0189","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s00521-016-2401-x","article-title":"Text classification based on deep belief network and softmax regression","volume":"29","author":"Jiang","year":"2018","journal-title":"Neural Comput. Appl."},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0190","doi-asserted-by":"crossref","first-page":"189","DOI":"10.3390\/s16020189","article-title":"Recognition of human activities using continuous autoencoders with wearable sensors","volume":"16","author":"Wang","year":"2016","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0191","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1007\/s11042-018-6273-1","article-title":"Stacked sparse autoencoder and history of binary motion image for human activity recognition","volume":"78","author":"Gnouma","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.patcog.2020.107561_bib0192","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.eswa.2017.06.011","article-title":"Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders","volume":"87","author":"Khan","year":"2017","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0193","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s12984-019-0486-z","article-title":"Design, development, and evaluation of a local sensor-based gait phase recognition system using a logistic model decision tree for orthosis-control","volume":"16","author":"Farah","year":"2019","journal-title":"J. Neuroeng. Rehabil."},{"key":"10.1016\/j.patcog.2020.107561_bib0194","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.patrec.2017.05.004","article-title":"A human activity recognition framework using max-min features and key poses with differential evolution random forests classifier","volume":"99","author":"Nunes","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patcog.2020.107561_bib0195","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.sigpro.2014.08.038","article-title":"Coupled hidden conditional random fields for RGB-D human action recognition","volume":"112","author":"Liu","year":"2015","journal-title":"Signal Process."},{"key":"10.1016\/j.patcog.2020.107561_bib0196","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.eswa.2018.04.017","article-title":"Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm","volume":"107","author":"Tharwat","year":"2018","journal-title":"Expert Syst. Appl."},{"issue":"11","key":"10.1016\/j.patcog.2020.107561_bib0197","doi-asserted-by":"crossref","first-page":"2568","DOI":"10.1109\/TPAMI.2018.2863279","article-title":"Early action prediction by soft regression","volume":"41","author":"Hu","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0198","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1007\/s10044-018-0688-1","article-title":"An implementation of optimized framework for action classification using multilayers neural network on selected fused features","volume":"22","author":"Khan","year":"2019","journal-title":"Pattern Anal. Appl."},{"key":"10.1016\/j.patcog.2020.107561_bib0199","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.patrec.2018.04.035","article-title":"Combining CNN streams of RGB-D and skeletal data for human activity recognition","volume":"115","author":"Khaire","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patcog.2020.107561_bib0200","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"1444","article-title":"Adaptive RNN tree for large-scale human action recognition","author":"Li","year":"2017"},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0201","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s10015-017-0422-x","article-title":"Deep recurrent neural network for mobile human activity recognition with high throughput","volume":"23","author":"Inoue","year":"2018","journal-title":"Artif. Life Rob."},{"issue":"14","key":"10.1016\/j.patcog.2020.107561_bib0202","doi-asserted-by":"crossref","first-page":"15839","DOI":"10.1007\/s11042-016-3872-6","article-title":"Robust visual tracking based on generative and discriminative model collaboration","volume":"76","author":"Dou","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.patcog.2020.107561_bib0203","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.patrec.2016.01.013","article-title":"A naive bayes baseline for early gesture recognition","volume":"73","author":"Escalante","year":"2016","journal-title":"Pattern Recognit. Lett."},{"issue":"2","key":"10.1016\/j.patcog.2020.107561_bib0204","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1007\/s00521-017-3028-2","article-title":"A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition","volume":"31","author":"Mustafa","year":"2019","journal-title":"Neural Comput. Appl."},{"issue":"6","key":"10.1016\/j.patcog.2020.107561_bib0205","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MIM.2016.7777649","article-title":"Human activity monitoring based on hidden Markov models using a smartphone","volume":"19","author":"San-Segundo","year":"2016","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"10.1016\/j.patcog.2020.107561_bib0206","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1146\/annurev-statistics-031017-100325","article-title":"Finite mixture models","volume":"6","author":"McLachlan","year":"2019","journal-title":"Annu. Rev. Stat. Appl."},{"key":"10.1016\/j.patcog.2020.107561_bib0207","doi-asserted-by":"crossref","first-page":"106458","DOI":"10.1016\/j.compeleceng.2019.106458","article-title":"Real-time anomaly detection based on long short-term memory and gaussian mixture model","volume":"79","author":"Ding","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.patcog.2020.107561_bib0208","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.procs.2016.02.095","article-title":"Analysis of k-means and k-medoids algorithm for big data","volume":"78","author":"Arora","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.patcog.2020.107561_bib0209","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.humov.2014.11.013","article-title":"Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification","volume":"40","author":"Biswas","year":"2015","journal-title":"Hum. Mov. Sci."},{"key":"10.1016\/j.patcog.2020.107561_bib0210","series-title":"Neural Networks: Tricks of the Trade","first-page":"599","article-title":"A practical guide to training restricted Boltzmann machines","author":"Hinton","year":"2012"},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0211","doi-asserted-by":"crossref","first-page":"1878","DOI":"10.1109\/TIP.2017.2781299","article-title":"Stacked denoising tensor auto-encoder for action recognition with spatiotemporal corruptions","volume":"27","author":"Jia","year":"2018","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0212","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s10100-017-0479-6","article-title":"A framework for sensitivity analysis of decision trees","volume":"26","author":"Kami\u0144ski","year":"2018","journal-title":"Cent. Eur. J. Oper. Res."},{"issue":"8","key":"10.1016\/j.patcog.2020.107561_bib0213","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1109\/TPAMI.2016.2519021","article-title":"Nonparametric feature matching based conditional random fields for gesture recognition from multi-modal video","volume":"38","author":"Chang","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0214","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s11517-015-1351-2","article-title":"Hierarchical multi-class SVM with ELM kernel for epileptic eeg signal classification","volume":"54","author":"Murugavel","year":"2016","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.patcog.2020.107561_bib0215","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.neucom.2015.08.112","article-title":"Efficient kNN classification algorithm for big data","volume":"195","author":"Deng","year":"2016","journal-title":"Neurocomputing"},{"key":"10.1016\/j.patcog.2020.107561_bib0216","doi-asserted-by":"crossref","DOI":"10.1155\/2018\/7068349","article-title":"Deep learning for computer vision: a brief review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"issue":"17","key":"10.1016\/j.patcog.2020.107561_bib0217","doi-asserted-by":"crossref","first-page":"7941","DOI":"10.1007\/s00500-018-3424-2","article-title":"An efficient hybrid multilayer perceptron neural network with grasshopper optimization","volume":"23","author":"Heidari","year":"2019","journal-title":"Soft Comput."},{"key":"10.1016\/j.patcog.2020.107561_bib0218","series-title":"Advances in Neural Information Processing Systems","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012"},{"issue":"6","key":"10.1016\/j.patcog.2020.107561_bib0219","doi-asserted-by":"crossref","first-page":"5379","DOI":"10.1109\/TVT.2019.2908425","article-title":"Driver activity recognition for intelligent vehicles: a deep learning approach","volume":"68","author":"Xing","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0220","first-page":"114","article-title":"Recognition of human hand activities based on a single wrist IMU using recurrent neural networks","volume":"6","author":"Rivera","year":"2017","journal-title":"Int. J. Pharma Med. Biol. Sci."},{"key":"10.1016\/j.patcog.2020.107561_bib0221","doi-asserted-by":"crossref","DOI":"10.1155\/2018\/7316954","article-title":"Deep residual Bidir-LSTM for human activity recognition using wearable sensors","volume":"2018","author":"Zhao","year":"2018","journal-title":"Math. Prob. Eng."},{"issue":"1","key":"10.1016\/j.patcog.2020.107561_bib0222","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3390\/s16010115","article-title":"Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition","volume":"16","author":"Ord\u00f3\u00f1ez","year":"2016","journal-title":"Sensors"},{"issue":"4","key":"10.1016\/j.patcog.2020.107561_bib0223","first-page":"788","article-title":"User-independent recognition of sports activities from a single Wrist-Worn accelerometer: a template-matching-based approach","volume":"63","author":"Margarito","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"5","key":"10.1016\/j.patcog.2020.107561_bib0224","doi-asserted-by":"crossref","first-page":"051004","DOI":"10.1117\/1.JEI.24.5.051004","article-title":"Multiview human activity recognition system based on spatiotemporal template for video surveillance system","volume":"24","author":"Kushwaha","year":"2015","journal-title":"J. Electron. Imaging"},{"issue":"11","key":"10.1016\/j.patcog.2020.107561_bib0225","doi-asserted-by":"crossref","first-page":"4901","DOI":"10.1109\/TAP.2016.2598199","article-title":"Human activity classification based on dynamic time warping of an on-body creeping wave signal","volume":"64","author":"Li","year":"2016","journal-title":"IEEE Trans. Antennas Propag."},{"key":"10.1016\/j.patcog.2020.107561_bib0226","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/j.eswa.2018.08.041","article-title":"Seeded transfer learning for regression problems with deep learning","volume":"115","author":"Salaken","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patcog.2020.107561_bib0227","doi-asserted-by":"crossref","DOI":"10.1155\/2018\/6163475","article-title":"HuAc: Human activity recognition using crowdsourced WiFi signals and skeleton data","volume":"2018","author":"Guo","year":"2018","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"10.1016\/j.patcog.2020.107561_bib0228","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"961","article-title":"ActivityNet: A large-scale video benchmark for human activity understanding","author":"Caba Heilbron","year":"2015"},{"key":"10.1016\/j.patcog.2020.107561_bib0229","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.patcog.2019.04.025","article-title":"Human trajectory prediction in crowded scene using social-affinity long short-term memory","volume":"93","author":"Pei","year":"2019","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2020.107561_bib0230","first-page":"1","article-title":"Highly refined human action recognition model to handle intraclass variability & interclass similarity","author":"Akila","year":"2019","journal-title":"Multimed. Tools Appl."},{"issue":"3","key":"10.1016\/j.patcog.2020.107561_bib0231","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1007\/s11263-015-0851-8","article-title":"Recognizing fine-grained and composite activities using hand-centric features and script data","volume":"119","author":"Rohrbach","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.patcog.2020.107561_bib0232","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.pmcj.2019.01.006","article-title":"Wireless non-invasive motion tracking of functional behavior","volume":"54","author":"Zhang","year":"2019","journal-title":"Pervasive Mob. Comput."},{"issue":"14","key":"10.1016\/j.patcog.2020.107561_bib0233","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.3390\/s19143113","article-title":"Non-invasive ambient intelligence in real life: dealing with noisy patterns to help older people","volume":"19","author":"Ant\u00f3n","year":"2019","journal-title":"Sensors"},{"issue":"9","key":"10.1016\/j.patcog.2020.107561_bib0234","doi-asserted-by":"crossref","first-page":"2064","DOI":"10.3390\/s17092064","article-title":"A novel energy-efficient approach for human activity recognition","volume":"17","author":"Zheng","year":"2017","journal-title":"Sensors"},{"key":"10.1016\/j.patcog.2020.107561_bib0235","doi-asserted-by":"crossref","first-page":"9893","DOI":"10.1109\/ACCESS.2018.2890675","article-title":"InnoHAR: A deep neural network for complex human activity recognition","volume":"7","author":"Xu","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.patcog.2020.107561_bib0236","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1016\/j.cma.2019.01.011","article-title":"NURBS-based postbuckling analysis of functionally graded carbon nanotube-reinforced composite shells","volume":"347","author":"Nguyen","year":"2019","journal-title":"Comput. Methods Appl. Mech. Eng."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320320303642?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320320303642?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T15:19:44Z","timestamp":1656083984000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320320303642"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12]]},"references-count":236,"alternative-id":["S0031320320303642"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2020.107561","relation":{},"ISSN":["0031-3203"],"issn-type":[{"type":"print","value":"0031-3203"}],"subject":[],"published":{"date-parts":[[2020,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Sensor-based and vision-based human activity recognition: A comprehensive survey","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2020.107561","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107561"}}