{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T22:23:12Z","timestamp":1785018192898,"version":"3.55.0"},"reference-count":160,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T00:00:00Z","timestamp":1595376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["Fi799\/10-2, HO2403\/14-2"],"award-info":[{"award-number":["Fi799\/10-2, HO2403\/14-2"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Optimizations in logistics require recognition and analysis of human activities. The potential of sensor-based human activity recognition (HAR) in logistics is not yet well explored. Despite a significant increase in HAR datasets in the past twenty years, no available dataset depicts activities in logistics. This contribution presents the first freely accessible logistics-dataset. In the \u2019Innovationlab Hybrid Services in Logistics\u2019 at TU Dortmund University, two picking and one packing scenarios were recreated. Fourteen subjects were recorded individually when performing warehousing activities using Optical marker-based Motion Capture (OMoCap), inertial measurement units (IMUs), and an RGB camera. A total of 758 min of recordings were labeled by 12 annotators in 474 person-h. All the given data have been labeled and categorized into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The dataset is deployed for solving HAR using deep networks.<\/jats:p>","DOI":"10.3390\/s20154083","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T11:26:01Z","timestamp":1595503561000},"page":"4083","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4827-3206","authenticated-orcid":false,"given":"Friedrich","family":"Niemann","sequence":"first","affiliation":[{"name":"Chair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4915-4070","authenticated-orcid":false,"given":"Christopher","family":"Reining","sequence":"additional","affiliation":[{"name":"Chair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8115-5968","authenticated-orcid":false,"given":"Fernando","family":"Moya Rueda","sequence":"additional","affiliation":[{"name":"Pattern Recognition in Embedded Systems Groups, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nilah Ravi","family":"Nair","sequence":"additional","affiliation":[{"name":"Chair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janine Anika","family":"Steffens","sequence":"additional","affiliation":[{"name":"Chair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7446-7813","authenticated-orcid":false,"given":"Gernot A.","family":"Fink","sequence":"additional","affiliation":[{"name":"Pattern Recognition in Embedded Systems Groups, TU Dortmund University, Otto-Hahn-Str. 16, 44227 Dortmund, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3349-4951","authenticated-orcid":false,"given":"Michael","family":"ten Hompel","sequence":"additional","affiliation":[{"name":"Chair of Materials Handling and Warehousing, TU Dortmund University, Joseph-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2499621","article-title":"A Tutorial on Human Activity Recognition Using Body-Worn Inertial Sensors","volume":"46","author":"Bulling","year":"2014","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Grzeszick, R., Lenk, J.M., Rueda, F.M., Fink, G.A., Feldhorst, S., and ten Hompel, M. (2017, January 21\u201322). Deep Neural Network based Human Activity Recognition for the Order Picking Process. Proceedings of the 4th International Workshop on Sensor-Based Activity Recognition and Interaction, Rostock, Germany.","DOI":"10.1145\/3134230.3134231"},{"key":"ref_4","unstructured":"Roggen, D., Calatroni, A., Nguyen-Dinh, L.V., Chavarriaga, R., Sagha, H., and Digumarti, S.T. (2020, March 20). Activity Recognition Challenge|Opportunity. Available online: http:\/\/www.opportunity-project.eu\/challenge.html."},{"key":"ref_5","unstructured":"Reiss, A. (2020, March 20). UCI Machine Learning Repository: PAMAP2 Physical Activity Monitoring Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/PAMAP2+Physical+Activity+Monitoring."},{"key":"ref_6","unstructured":"(2020, March 20). 2016 Warehouse\/DC Operations Survey: Ready to Confront Complexity. Available online: https:\/\/www.logisticsmgmt.com\/article\/2016_warehouse_dc_operations_survey_ready_to_confront_complexity."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s00501-016-0481-7","article-title":"Manuelle Kommissioniersysteme und die Rolle des Menschen","volume":"161","author":"Zsifkovits","year":"2016","journal-title":"BHM Berg-und H\u00fcttenm\u00e4nnische Monatshefte"},{"key":"ref_8","unstructured":"(2020, March 20). REFA-Time Study. Available online: https:\/\/refa.de\/en\/Int.-global-consulting\/time-studies."},{"key":"ref_9","unstructured":"(2020, March 20). MTM\u2014Methods-Time Measurement: MTM. Available online: https:\/\/mtm.org\/en\/about-mtm\/mtm."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Reining, C., Niemann, F., Moya Rueda, F., Fink, G.A., and ten Hompel, M. (2019). Human Activity Recognition for Production and Logistics\u2014A Systematic Literature Review. Information, 10.","DOI":"10.3390\/info10080245"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Feldhorst, S., Masoudenijad, M., ten Hompel, M., and Fink, G.A. (2016). Motion Classification for Analyzing the Order Picking Process Using Mobile Sensors\u2014General Concepts, Case Studies and Empirical Evaluation, SCITEPRESS\u2014Science and and Technology Publications.","DOI":"10.5220\/0005828407060713"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Moya Rueda, F., Grzeszick, R., Fink, G., Feldhorst, S., and ten Hompel, M. (2018). Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors. Informatics, 5.","DOI":"10.3390\/informatics5020026"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Reining, C., Schlangen, M., Hissmann, L., ten Hompel, M., Moya, F., and Fink, G.A. (2018, January 20\u201321). Attribute Representation for Human Activity Recognition of Manual Order Picking Activities. Proceedings of the 5th International Workshop on Sensor-Based Activity Recognition and Interaction\u2014iWOAR \u201918, Berlin, Germany.","DOI":"10.1145\/3266157.3266214"},{"key":"ref_14","unstructured":"(2020, April 15). General Data Protection Regulation (GDPR). Available online: https:\/\/gdpr.eu\/tag\/gdpr\/."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Venkatapathy, A.K.R., Bayhan, H., Zeidler, F., and ten Hompel, M. (2017, January 3\u20136). Human Machine Synergies in Intra-Logistics: Creating a Hybrid Network for Research and Technologies. Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, Czech Republic.","DOI":"10.15439\/2017F253"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Moya Rueda, F., and Fink, G.A. (2018, January 20\u201324). Learning attribute representation for human activity recognition. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545146"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ronao, C.A., and Cho, S.B. (2015). Deep convolutional neural networks for human activity recognition with smartphone sensors. Conference on Neural Information Processing, Springer.","DOI":"10.1007\/978-3-319-26561-2_6"},{"key":"ref_18","unstructured":"Yang, J., Nguyen, M.N., San, P.P., Li, X., and Krishnaswamy, S. (2015, January 25\u201331). Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Debache, I., Jeantet, L., Chevallier, D., Bergouignan, A., and Sueur, C. (2020). A Lean and Performant Hierarchical Model for Human Activity Recognition Using Body-Mounted Sensors. Sensors, 20.","DOI":"10.3390\/s20113090"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"M\u00fcnzner, S., Schmidt, P., Reiss, A., Hanselmann, M., Stiefelhagen, R., and D\u00fcrichen, R. (2017, January 11\u201315). CNN-Based Sensor Fusion Techniques for Multimodal Human Activity Recognition. Proceedings of the 2017 ACM International Symposium on Wearable Computers, Maui, HI, USA.","DOI":"10.1145\/3123021.3123046"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., and Craddock, I. (2018). A Comprehensive Study of Activity Recognition Using Accelerometers. Informatics, 5.","DOI":"10.20944\/preprints201803.0147.v1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/MPRV.2014.52","article-title":"In-home activity recognition: Bayesian inference for hidden Markov models","volume":"13","author":"Ordonez","year":"2014","journal-title":"IEEE Pervasive Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zeng, M., Nguyen, L.T., Yu, B., Ole J., M., Zhu, J., Wu, P., and Zhang, J. (2014, January 6\u20137). Convolutional neural networks for human activity recognition using mobile sensors. Proceedings of the 6th International Conference on Mobile Computing, Applications and Services, Austin, TX, USA.","DOI":"10.4108\/icst.mobicase.2014.257786"},{"key":"ref_24","unstructured":"Hammerla, N.Y., Halloran, S., and Ploetz, T. (2016). Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv."},{"key":"ref_25","unstructured":"(2020, June 26). ISO\/IEC 19510:2013. Available online: https:\/\/www.iso.org\/cms\/render\/live\/en\/sites\/isoorg\/contents\/data\/standard\/06\/26\/62652.html."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Moya Rueda, F., L\u00fcdtke, S., Schr\u00f6der, M., Yordanova, K., Kirste, T., and Fink, G.A. (2019, January 11\u201315). Combining Symbolic Reasoning and Deep Learning for Human Activity Recognition. Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kyoto, Japan.","DOI":"10.1109\/PERCOMW.2019.8730792"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dombrowski, U., Riechel, C., and Schulze, S. (2011, January 25\u201327). Enforcing Employees Participation in the Factory Planning Process. Proceedings of the 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM), Tampere, Finland.","DOI":"10.1109\/ISAM.2011.5942337"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.procir.2016.01.139","article-title":"Re-Engineering Assembly Line with Lean Techniques","volume":"40","author":"Nguyen","year":"2016","journal-title":"Procedia CIRP"},{"key":"ref_29","unstructured":"(2020, May 29). MbientLab\u2014Wearable Bluetooth 9-Axis IMUs & Environmental Sensors. Available online: https:\/\/mbientlab.com\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3758\/BF03334122","article-title":"The Lateral Preference Inventory for Measurement of Handedness, Footedness, Eyedness, and Earedness: Norms for Young Adults","volume":"31","author":"Coren","year":"1993","journal-title":"Bull. Psychon. Soc."},{"key":"ref_31","first-page":"1","article-title":"A Large-Scale Population Study of Early Life Factors Influencing Left-Handedness","volume":"9","author":"Francks","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1177\/0278364919882089","article-title":"Human Movement and Ergonomics: An Industry-Oriented Dataset for Collaborative Robotics","volume":"38","author":"Maurice","year":"2019","journal-title":"Int. J. Robot. Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Reining, C., Rueda, F.M., ten Hompel, M., and Fink, G.A. (2018, January 9\u201312). Towards a Framework for Semi-Automated Annotation of Human Order Picking Activities Using Motion Capturing. Proceedings of the 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), Poznan, Poland.","DOI":"10.15439\/2018F188"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Nguyen, L.T., Zeng, M., Tague, P., and Zhang, J. (2015, January 7\u201311). I Did Not Smoke 100 Cigarettes Today!: Avoiding False Positives in Real-World Activity Recognition. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, UbiComp: 15, Osaka, Japan.","DOI":"10.1145\/2750858.2804256"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1097\/01241398-199211000-00023","article-title":"Gait Analysis: Normal and Pathological Function","volume":"12","author":"Perry","year":"1992","journal-title":"J. Pediatr. Orthop."},{"key":"ref_36","unstructured":"Bokranz, R., and Landau, K. (2012). Handbuch Industrial Engineering: Produktivit\u00e4tsmanagement mit MTM. Band 1: Konzept, Sch\u00e4ffer-Poeschel. [2., \u00fcberarb. und erw. aufl ed.]. OCLC: 820418782."},{"key":"ref_37","unstructured":"Whittle, M.W. (2007). Gait Analysis: An Introduction, Butterworth-Heinemann. [4th ed.]."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Reining, C., Moya Rueda, F., Niemann, F., Fink, G.A., and ten Hompel, M. (2020, January 23\u201327). Annotation Performance for Multi-Channel Time Series HAR Dataset in Logistics. Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2020), Austin, Texas, USA. in press.","DOI":"10.1109\/PerComWorkshops48775.2020.9156170"},{"key":"ref_39","unstructured":"Moya Rueda, F., and Altermann, E. (2020, June 09). Annotation Tool LARa. Available online: https:\/\/github.com\/wilfer9008\/Annotation_Tool_LARa."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1016\/j.infsof.2013.07.010","article-title":"A systematic review of systematic review process research in software engineering","volume":"55","author":"Kitchenham","year":"2013","journal-title":"Inf. Softw. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.infsof.2008.09.009","article-title":"Systematic literature reviews in software engineering\u2014A systematic literature review","volume":"51","author":"Kitchenham","year":"2009","journal-title":"Inf. Softw. Technol."},{"key":"ref_42","unstructured":"Kitchenham, B. (2004). Procedures for Performing Systematic Reviews, Keele University."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ijpe.2017.04.005","article-title":"Supply chain collaboration for sustainability: A literature review and future research agenda","volume":"194","author":"Chen","year":"2017","journal-title":"Int. J. Prod. Econ."},{"key":"ref_44","first-page":"126","article-title":"Physical activity, exercise, and physical fitness: Definitions and distinctions for health-related research","volume":"100","author":"Caspersen","year":"1985","journal-title":"Public Health Rep."},{"key":"ref_45","unstructured":"Vanrie, J., and Verfaillie, K. (2020, March 20). Action Database. Available online: http:\/\/ppw.kuleuven.be\/english\/research\/lep\/resources\/action."},{"key":"ref_46","unstructured":"Theodoridis, T. (2020, March 20). UCI Machine Learning Repository: Vicon Physical Action Data Set Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Vicon+Physical+Action+Data+Set."},{"key":"ref_47","unstructured":"Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., and Havinga, P.J.M. (2020, March 20). Research | Datasets | Pervasive Systems Group|University of Twente. Available online: http:\/\/www.utwente.nl\/en\/eemcs\/ps\/research\/dataset\/."},{"key":"ref_48","unstructured":"Mandery, C., Terlemez, O., Do, M., Vahrenkamp, N., and Asfour, T. (2020, March 20). KIT Whole-Body Human Motion Database. Available online: http:\/\/motion-database.humanoids.kit.edu\/."},{"key":"ref_49","unstructured":"Jafari, R., Chen, C., and Kehtarnavaz, N. (2020, March 20). UTD Multimodal Human Action Dataset (UTD-MHAD). Available online: http:\/\/personal.utdallas.edu\/~kehtar\/UTD-MHAD.html."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kasebzadeh, P., Hendeby, G., Fritsche, C., Gunnarsson, F., and Gustafsson, F. (2017, January 18\u201321). IMU Dataset for Motion and Device Mode Classification. Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan.","DOI":"10.1109\/IPIN.2017.8115956"},{"key":"ref_51","unstructured":"Sztyler, T. (2020, March 20). Human Activity Recognition. Available online: http:\/\/sensor.informatik.uni-mannheim.de\/#dataset_dailylog."},{"key":"ref_52","unstructured":"Vaizman, Y., Ellis, K., and Lanckriet, G. (2020, March 20). The ExtraSensory Dataset. Available online: http:\/\/extrasensory.ucsd.edu\/."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, C., Lu, C.X., Markham, A., and Trigoni, N. (2020, March 20). Dataset and Methods for Deep Inertial Odometry. Available online: http:\/\/deepio.cs.ox.ac.uk\/.","DOI":"10.1109\/TMC.2019.2960780"},{"key":"ref_54","unstructured":"M\u00fcller, M., R\u00f6der, T., Clausen, M., Kr\u00fcger, B., Weber, A., and Eberhardt, B. (2020, March 20). Motion Database HDM05. Available online: http:\/\/resources.mpi-inf.mpg.de\/HDM05\/."},{"key":"ref_55","unstructured":"Lustrek, M., Kaluza, B., Piltaver, R., Krivec, J., and Vidulin, V. (2020, March 20). UCI Machine Learning Repository: Localization Data for Person Activity Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Localization+Data+for+Person+Activity."},{"key":"ref_56","unstructured":"Ugulino, W., Cardador, D., Vega, K., Velloso, E., Milidi\u00fa, R., and Fuks, H. (2020, March 20). Human Activity Recognition. Available online: http:\/\/groupware.les.inf.puc-rio.br\/har#ixzz2PyRdbAfA."},{"key":"ref_57","unstructured":"Ahmed, D.B. (2020, March 20). DLR\u2014Institut F\u00fcr Kommunikation Und Navigation\u2014Data Set. Available online: http:\/\/www.dlr.de\/kn\/desktopdefault.aspx\/tabid-12705\/22182_read-50785\/."},{"key":"ref_58","unstructured":"Reiss, A., Indlekofer, I., Schmidt, P., and Van Laerhoven, K. (2020, March 20). UCI Machine Learning Repository: PPG-DaLiA Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/PPG-DaLiA."},{"key":"ref_59","unstructured":"De la Torre, F., Hodgins, J., Montano, J., Valcarcel, S., Macey, J., and Forcada, R. (2020, March 20). Quality of Life Grand Challenge | Kitchen Capture. Available online: http:\/\/kitchen.cs.cmu.edu\/."},{"key":"ref_60","unstructured":"Stisen, A., and Blunck, H. (2020, March 20). UCI Machine Learning Repository: Heterogeneity Activity Recognition Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/heterogeneity+activity+recognition."},{"key":"ref_61","unstructured":"Vilarinho, T., Bajer, D.G., Dahl, O.H., Egge, I., Hegdal, S.S., L\u00f8nes, A., Slettevold, J.N., and Weggersen, S.M. (2020, March 20). SINTEF-SIT\/Project_gravity. Available online: http:\/\/github.com\/SINTEF-SIT\/project_gravity."},{"key":"ref_62","unstructured":"Faye, S., Louveton, N., Jafarnejad, S., Kryvchenko, R., and Engel, T. (2020, March 20). An Open Dataset for Human Activity Analysis. Available online: http:\/\/kaggle.com\/sasanj\/human-activity-smart-devices."},{"key":"ref_63","unstructured":"Sztyler, T. (2020, March 20). Human Activity Recognition. Available online: http:\/\/sensor.informatik.uni-mannheim.de\/#dataset_firstvision."},{"key":"ref_64","unstructured":"Mohammed, S., and Gomaa, W. (2020, March 20). HAD-AW Data-Set Benchmark For Human Activity Recognition Using Apple Watch. Available online: http:\/\/www.researchgate.net\/publication\/324136132_HAD-AW_Data-set_Benchmark_For_Human_Activity_Recognition_Using_Apple_Watch."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mandery, C., Terlemez, O., Do, M., Vahrenkamp, N., and Asfour, T. (2015, January 27\u201331). The KIT Whole-Body Human Motion Database. Proceedings of the 2015 International Conference on Advanced Robotics (ICAR), Istanbul, Turkey.","DOI":"10.1109\/ICAR.2015.7251476"},{"key":"ref_66","unstructured":"Vakanski, A., Jun, H.P., Paul, D.R., and Baker, R.T. (2020, March 20). UI\u2014PRMD. Available online: http:\/\/webpages.uidaho.edu\/ui-prmd\/."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Sztyler, T., and Baur, H. (2020, March 20). On-Body Localization of Wearable Devices: An Investigation of Position-Aware Activity Recognition. Available online: http:\/\/publications.wim.uni-mannheim.de\/informatik\/lski\/Sztyler2016Localization.pdf.","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"ref_68","unstructured":"(2020, March 20). Vicon\u2014Nexus. Available online: https:\/\/docs.vicon.com\/display\/Nexus26\/Full+body+modeling+with+Plug-in+Gait."},{"key":"ref_69","unstructured":"Roggen, D., Plotnik, M., and Hausdorff, J. (2020, March 20). UCI Machine Learning Repository: Daphnet Freezing of Gait Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Daphnet+Freezing+of+Gait."},{"key":"ref_70","unstructured":"Maurice, P., Malais\u00e9, A., Ivaldi, S., Rochel, O., Amiot, C., Paris, N., Richard, G.J., and Fritzsche, L. (2020, March 20). AndyData-Lab-onePerson. Available online: http:\/\/zenodo.org\/record\/3254403#.XmDpQahKguV."},{"key":"ref_71","unstructured":"Zhang, W., Liu, Z., Zhou, L., Leung, H., and Chan, A.B. (2020, March 20). Martial Arts, Dancing and Sports Dataset | VISAL. Available online: http:\/\/visal.cs.cityu.edu.hk\/research\/mads\/."},{"key":"ref_72","unstructured":"Trumble, M., Gilbert, A., Malleson, C., Hilton, A., and Collomosse, J. (2020, March 20). Total Capture: 3D Human Pose Estimation Fusing Video and Inertial Sensors. Available online: http:\/\/cvssp.org\/data\/totalcapture\/."},{"key":"ref_73","unstructured":"(2020, March 26). ANVIL: The Video Annotation Research Tool. Available online: http:\/\/www.anvil-software.org\/."},{"key":"ref_74","unstructured":"Bulling, A., Blanke, U., and Schiele, B. (2020, March 20). Andreas-Bulling\/ActRecTut. Available online: http:\/\/github.com\/andreas-bulling\/ActRecTut."},{"key":"ref_75","unstructured":"Sztyler, T. (2020, March 20). Human Activity Recognition. Available online: http:\/\/sensor.informatik.uni-mannheim.de\/#dataset_realworld."},{"key":"ref_76","unstructured":"Zhang, M., and Sawchuk, A.A. (2020, March 20). Human Activities Dataset. Available online: http:\/\/sipi.usc.edu\/had\/."},{"key":"ref_77","unstructured":"(2020, March 26). Figshare. Available online: https:\/\/figshare.com\/."},{"key":"ref_78","unstructured":"(2020, March 26). UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/ml\/index.php."},{"key":"ref_79","unstructured":"(2020, March 26). Zenodo. Available online: https:\/\/zenodo.org\/."},{"key":"ref_80","unstructured":"(2020, March 26). GitHub. Available online: https:\/\/github.com."},{"key":"ref_81","unstructured":"(2020, March 26). Dropbox. Available online: https:\/\/www.dropbox.com\/."},{"key":"ref_82","unstructured":"(2020, March 26). ResearchGate. Available online: https:\/\/www.researchgate.net\/."},{"key":"ref_83","unstructured":"Roggen, D., and Zappi, P. (2020, March 20). Wiki:Dataset [Human Activity\/Context Recognition Datasets]. Available online: http:\/\/har-dataset.org\/doku.php?id=wiki:dataset."},{"key":"ref_84","unstructured":"(2020, March 20). Carnegie Mellon University\u2014CMU Graphics Lab - Motion Capture Library. Available online: http:\/\/mocap.cs.cmu.edu\/."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"625","DOI":"10.3758\/BF03206542","article-title":"Perception of Biological Motion: A Stimulus Set of Human Point-Light Actions","volume":"36","author":"Vanrie","year":"2004","journal-title":"Behav. Res. Methods Instrum. Comput."},{"key":"ref_86","unstructured":"M\u00fcller, M., R\u00f6der, T., Clausen, M., Eberhardt, B., Kr\u00fcger, B., and Weber, A.G. (2020, March 20). Documentation Mocap Database HDM05. Available online: https:\/\/www.researchgate.net\/publication\/231521391_Documentation_Mocap_database_HDM05."},{"key":"ref_87","unstructured":"Yang, A.Y., Giani, A., Giannatonio, R., Gilani, K., Iyengar, S., Kuryloski, P., Seto, E., Seppa, V.P., Wang, C., and Shia, V. (2020, March 20). D-WAR: Distributed Wearable Action Recognition. Available online: http:\/\/people.eecs.berkeley.edu\/~yang\/software\/WAR\/."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Yang, A.Y., Iyengar, S., Kuryloski, P., and Jafari, R. (2008, January 23\u201328). Distributed Segmentation and Classification of Human Actions Using a Wearable Motion Sensor Network. Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Anchorage, AK, USA.","DOI":"10.1109\/CVPRW.2008.4563176"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Forster, K., Roggen, D., and Troster, G. (2009, January 4\u20137). Unsupervised Classifier Self-Calibration through Repeated Context Occurences: Is There Robustness against Sensor Displacement to Gain?. Proceedings of the 2009 International Symposium on Wearable Computers, Linz, Austria.","DOI":"10.1109\/ISWC.2009.12"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Spriggs, E., De La Torre, F., and Hebert, M. (2009, January 20\u201325). Temporal Segmentation and Activity Classification from First-Person Sensing. Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5204354"},{"key":"ref_91","unstructured":"Sigal, L., Balan, A.O., and Black, M.J. (2020, March 20). HumanEva Dataset. Available online: http:\/\/humaneva.is.tue.mpg.de\/datasets_human_1."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s11263-009-0273-6","article-title":"HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion","volume":"87","author":"Sigal","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_93","unstructured":"Sigal, L., Balan, A.O., and Black, M.J. (2020, March 20). HumanEva Dataset. Available online: http:\/\/humaneva.is.tue.mpg.de\/datasets_human_2."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/978-3-642-16917-5_18","article-title":"An Agent-Based Approach to Care in Independent Living","volume":"Volume 6439","author":"Wichert","year":"2010","journal-title":"Ambient Intelligence"},{"key":"ref_95","unstructured":"Essid, S., Lin, X., Gowing, M., Kordelas, G., Aksay, A., Kelly, P., Fillon, T., Zhang, Q., Dielmann, A., and Kitanovski, V. (2020, March 20). 3DLife ACM MM Grand Challenge 2011\u2014Realistic Interaction in Online Virtual Environments. Available online: http:\/\/perso.telecom-paristech.fr\/essid\/3dlife-gc-11\/."},{"key":"ref_96","first-page":"157","article-title":"A Multi-Modal Dance Corpus for Research into Interaction between Humans in Virtual Environments","volume":"7","author":"Essid","year":"2013","journal-title":"J. Multimodal User Interfaces"},{"key":"ref_97","unstructured":"McCall, C., Reddy, K., and Shah, M. (2020, March 20). CRCV | Center for Research in Computer Vision at the University of Central Florida. Available online: http:\/\/www.crcv.ucf.edu\/data\/UCF-iPhone.php."},{"key":"ref_98","unstructured":"McCall, C., Reddy, K., and Shah, M. (2020, March 20). Macro-Class Selection for Hierarchical k-Nn Classification of Inertial Sensor Data. Available online: https:\/\/www.crcv.ucf.edu\/papers\/PECCS_2012.pdf."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Theodoridis, T., and Hu, H. (2007, January 15\u201318). Action Classification of 3D Human Models Using Dynamic ANNs for Mobile Robot Surveillance. Proceedings of the 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO), Sanya, China.","DOI":"10.1109\/ROBIO.2007.4522190"},{"key":"ref_100","unstructured":"Lockhart, J.W., Weiss, G.M., Xue, J.C., Gallagher, S.T., Grosner, A.B., and Pulickal, T.T. (2020, March 20). WISDM Lab: Dataset. Available online: http:\/\/www.cis.fordham.edu\/wisdm\/dataset.php."},{"key":"ref_101","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":"ref_102","unstructured":"Reyes-Ortiz, J.L., Anguita, D., Ghio, A., Oneto, L., and Parra, X. (2020, March 20). UCI Machine Learning Repository: Human Activity Recognition Using Smartphones Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/human+activity+recognition+using+smartphones."},{"key":"ref_103","unstructured":"Anguita, D., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2020, March 20). A Public Domain Dataset for Human Activity Recognition Using Smartphones. Available online: https:\/\/www.elen.ucl.ac.be\/Proceedings\/esann\/esannpdf\/es2013-84.pdf."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Forster, K., Troster, G., Lukowicz, P., Bannach, D., Pirkl, G., and Ferscha, A. (2010, January 15\u201318). Collecting Complex Activity Datasets in Highly Rich Networked Sensor Environments. Proceedings of the 2010 Seventh International Conference on Networked Sensing Systems (INSS), Kassel, Germany.","DOI":"10.1109\/INSS.2010.5573462"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 18\u201322). Introducing a New Benchmarked Dataset for Activity Monitoring. Proceedings of the 2012 16th International Symposium on Wearable Computers, Newcastle, UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Sawchuk, A.A. (2012, January 5\u20138). USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370438"},{"key":"ref_107","unstructured":"Kwapisz, J.R., Weiss, G.M., and Moore, S.A. (2020, March 20). WISDM Lab: Dataset. Available online: http:\/\/www.cis.fordham.edu\/wisdm\/dataset.php."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Lockhart, J.W., Weiss, G.M., Xue, J.C., Gallagher, S.T., Grosner, A.B., and Pulickal, T.T. (2011, January 21). Design Considerations for the WISDM Smart Phone-Based Sensor Mining Architecture. Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, San Diego, CA, USA.","DOI":"10.1145\/2003653.2003656"},{"key":"ref_109","unstructured":"Barshan, B. (2020, March 20). UCI Machine Learning Repository: Daily and Sports Activities Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Daily+and+Sports+Activities."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1093\/comjnl\/bxt075","article-title":"Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units","volume":"57","author":"Barshan","year":"2014","journal-title":"Comput. J."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1109\/TITB.2009.2036165","article-title":"Wearable Assistant for Parkinson\u2019s Disease Patients With the Freezing of Gait Symptom","volume":"14","author":"Bachlin","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Shoaib, M., Scholten, H., and Havinga, P. (2013, January 18\u201321). Towards Physical Activity Recognition Using Smartphone Sensors. Proceedings of the 2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing, Vietri sul Mere, Italy.","DOI":"10.1109\/UIC-ATC.2013.43"},{"key":"ref_113","unstructured":"Medrano, C., Igual, R., Plaza, I., and Castro, M. (2020, March 20). Fall ADL Data | EduQTech. Available online: http:\/\/eduqtech.unizar.es\/en\/fall-adl-data\/."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Medrano, C., Igual, R., Plaza, I., and Castro, M. (2014). Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0094811"},{"key":"ref_115","first-page":"52","article-title":"Wearable Computing: Accelerometers\u2019 Data Classification of Body Postures and Movements","volume":"Volume 7589","author":"Barros","year":"2012","journal-title":"Advances in Artificial Intelligence\u2014SBIA 2012"},{"key":"ref_116","unstructured":"Casale, P., Pujol, O., and Radeva, P. (2020, March 20). UCI Machine Learning Repository: Activity Recognition from Single Chest-Mounted Accelerometer Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Activity+Recognition+from+Single+Chest-Mounted+Accelerometer."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s00779-011-0415-z","article-title":"Personalization and User Verification in Wearable Systems Using Biometric Walking Patterns","volume":"16","author":"Casale","year":"2012","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_118","unstructured":"Banos, O., Toth, M.A., and Amft, O. (2020, March 20). UCI Machine Learning Repository: REALDISP Activity Recognition Dataset Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/REALDISP+Activity+Recognition+Dataset."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"9995","DOI":"10.3390\/s140609995","article-title":"Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition","volume":"14","author":"Banos","year":"2014","journal-title":"Sensors"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"10146","DOI":"10.3390\/s140610146","article-title":"Fusion of Smartphone Motion Sensors for Physical Activity Recognition","volume":"14","author":"Shoaib","year":"2014","journal-title":"Sensors"},{"key":"ref_121","unstructured":"Casale, P. (2020, March 20). UCI Machine Learning Repository: User Identification From Walking Activity Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/User+Identification+From+Walking+Activity."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Shoaib, M., Bosch, S., Incel, O., Scholten, H., and Havinga, P. (2016). Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors. Sensors, 16.","DOI":"10.3390\/s16040426"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T.S., Kj\u00e6rgaard, M.B., Dey, A., Sonne, T., and Jensen, M.M. (2015, January 1\u20134). Smart Devices Are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition. Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, Seoul, Korea.","DOI":"10.1145\/2809695.2809718"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Ahmed, D.B., Frank, K., and Heirich, O. (2015, January 6\u20139). Recognition of Professional Activities with Displaceable Sensors. Proceedings of the 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), Boston, MA, USA.","DOI":"10.1109\/VTCFall.2015.7391112"},{"key":"ref_125","unstructured":"Wojtusch, J., and von Stryk, O. (2020, March 20). HuMoD Database Human Motion Dynamics on Actuation Level. Available online: https:\/\/www.sim.informatik.tu-darmstadt.de\/res\/ds\/humod\/."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Wojtusch, J., and von Stryk, O. (2015, January 3\u20135). HuMoD\u2014A Versatile and Open Database for the Investigation, Modeling and Simulation of Human Motion Dynamics on Actuation Level. Proceedings of the 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), Seoul, Korea.","DOI":"10.1109\/HUMANOIDS.2015.7363534"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Vilarinho, T., Farshchian, B., Bajer, D.G., Dahl, O.H., Egge, I., Hegdal, S.S., Lones, A., Slettevold, J.N., and Weggersen, S.M. (2015, January 26\u201328). A Combined Smartphone and Smartwatch Fall Detection System. Proceedings of the 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, UK.","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.216"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/978-3-540-77690-1_2","article-title":"Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection","volume":"Volume 4913","author":"Verdone","year":"2008","journal-title":"Wireless Sensor Networks"},{"key":"ref_129","unstructured":"Reyes-Ortiz, J.L., Oneto, L., Monson\u00eds, A.S., and Parra, X. (2020, March 20). UCI Machine Learning Repository: Smartphone-Based Recognition of Human Activities and Postural Transitions Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Smartphone-Based+Recognition+of+Human+Activities+and+Postural+Transitions."},{"key":"ref_130","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":"Oneto","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Chen, C., Jafari, R., and Kehtarnavaz, N. (2015, January 27\u201330). UTD-MHAD: A Multimodal Dataset for Human Action Recognition Utilizing a Depth Camera and a Wearable Inertial Sensor. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350781"},{"key":"ref_132","unstructured":"Palumbo, F., Gallicchio, C., Pucci, R., and Micheli, A. (2020, March 20). UCI Machine Learning Repository: Activity Recognition System Based on Multisensor Data Fusion (AReM) Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Activity+Recognition+system+based+on+Multisensor+data+fusion+%28AReM%29."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3233\/AIS-160372","article-title":"Human Activity Recognition Using Multisensor Data Fusion Based on Reservoir Computing","volume":"8","author":"Palumbo","year":"2016","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/978-3-662-53401-4_8","article-title":"Self-Tracking Reloaded: Applying Process Mining to Personalized Health Care from Labeled Sensor Data","volume":"Volume 9930","author":"Koutny","year":"2016","journal-title":"Transactions on Petri Nets and Other Models of Concurrency XI"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MPRV.2017.3971131","article-title":"Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches","volume":"16","author":"Vaizman","year":"2017","journal-title":"IEEE Pervasive Comput."},{"key":"ref_136","unstructured":"V\u00f6gele, A., and Kr\u00fcger, B. (2020, March 20). HDM12 Dance - Documentation on a Data Base of Tango Motion Capture. Available online: http:\/\/cg.cs.uni-bonn.de\/en\/publications\/paper-details\/voegele-2016-HDM12\/."},{"key":"ref_137","unstructured":"Davis, K.A., and Owusu, E.B. (2020, March 20). UCI Machine Learning Repository: Smartphone Dataset for Human Activity Recognition (HAR) in Ambient Assisted Living (AAL) Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Smartphone+Dataset+for+Human+Activity+Recognition+%28HAR%29+in+Ambient+Assisted+Living+%28AAL%29."},{"key":"ref_138","unstructured":"Casilari, E., and A.Santoyo-Ram\u00f3n, J. (2020, March 20). UMAFall: Fall Detection Dataset (Universidad de Malaga). Available online: http:\/\/figshare.com\/articles\/UMA_ADL_FALL_Dataset_zip\/4214283."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.procs.2017.06.110","article-title":"UMAFall: A Multisensor Dataset for the Research on Automatic Fall Detection","volume":"110","author":"Casilari","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_140","unstructured":"Faye, S., Louveton, N., Jafarnejad, S., Kryvchenko, R., and Engel, T. (2020, March 20). An Open Dataset for Human Activity Analysis Using Smart Devices. Available online: https:\/\/hal.archives-ouvertes.fr\/hal-01586802."},{"key":"ref_141","unstructured":"Kasebzadeh, P., Hendeby, G., Fritsche, C., Gunnarsson, F., and Gustafsson, F. (2020, March 20). Parinaz Kasebzadeh: Research. Available online: http:\/\/users.isy.liu.se\/rt\/parka23\/research.html."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.imavis.2017.02.002","article-title":"Martial Arts, Dancing and Sports Dataset: A Challenging Stereo and Multi-View Dataset for 3D Human Pose Estimation","volume":"61","author":"Zhang","year":"2017","journal-title":"Image Vis. Comput."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Vakanski, A., Jun, H.p., Paul, D., and Baker, R. (2018). A Data Set of Human Body Movements for Physical Rehabilitation Exercises. Data, 3.","DOI":"10.3390\/data3010002"},{"key":"ref_144","unstructured":"Sucerquia, A., L\u00f3pez, J.D., and Vargas-Bonilla, J.F. (2020, March 20). SisFall | SISTEMIC. Available online: http:\/\/sistemic.udea.edu.co\/en\/investigacion\/proyectos\/english-falls\/."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., L\u00f3pez, J., and Vargas-Bonilla, J. (2017). SisFall: A Fall and Movement Dataset. Sensors, 17.","DOI":"10.3390\/s17010198"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Trumble, M., Gilbert, A., Malleson, C., Hilton, A., and Collomosse, J. (2017). Total Capture: 3D Human Pose Estimation Fusing Video and Inertial Sensors. Br. Mach. Vis. Assoc.","DOI":"10.5244\/C.31.14"},{"key":"ref_147","unstructured":"Micucci, D., Mobilio, M., and Napoletano, P. (2020, March 20). UniMiB SHAR. Available online: http:\/\/www.sal.disco.unimib.it\/technologies\/unimib-shar\/."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Micucci, D., Mobilio, M., and Napoletano, P. (2017). UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones. Appl. Sci., 7.","DOI":"10.20944\/preprints201706.0033.v1"},{"key":"ref_149","unstructured":"Martinez-Villase\u00f1or, L., Ponce, H., Brieva, J., Moya-Albor, E., N\u00fa\u00f1ez Mart\u00ednez, J., and Pe\u00f1afort Asturiano, C. (2020, March 20). HAR-UP. Available online: http:\/\/sites.google.com\/up.edu.mx\/har-up\/."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Villase\u00f1or, L., Ponce, H., Brieva, J., Moya-Albor, E., N\u00fa\u00f1ez Mart\u00ednez, J., and Pe\u00f1afort Asturiano, C. (2019). UP-Fall Detection Dataset: A Multimodal Approach. Sensors, 19.","DOI":"10.3390\/s19091988"},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Ashry, S., Elbasiony, R., and Gomaa, W. (2018). An LSTM-Based Descriptor for Human Activities Recognition Using IMU Sensors, SCITEPRESS\u2014Science and Technology Publications.","DOI":"10.5220\/0006902405040511"},{"key":"ref_152","unstructured":"Chereshnev, R., and Kert\u00e9sz-Farkas, A. (2020, March 20). Romanchereshnev\/HuGaDB. Available online: http:\/\/github.com\/romanchereshnev\/HuGaDB."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/978-3-319-73013-4_12","article-title":"HuGaDB: Human Gait Database for Activity Recognition from Wearable Inertial Sensor Networks","volume":"Volume 10716","author":"Ignatov","year":"2018","journal-title":"Analysis of Images, Social Networks and Texts"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Chen, C., Zhao, P., Lu, C.X., Wang, W., Markham, A., and Trigoni, N. (2020, March 20). OxIOD: The Dataset for Deep Inertial Odometry. Available online: https:\/\/www.researchgate.net\/publication\/327789960_OxIOD_The_Dataset_for_Deep_Inertial_Odometry.","DOI":"10.1109\/TMC.2019.2960780"},{"key":"ref_155","unstructured":"Turan, A., and Barshan, B. (2020, March 20). UCI Machine Learning Repository: Simulated Falls and Daily Living Activities Data Set Data Set. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Simulated+Falls+and+Daily+Living+Activities+Data+Set."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"10691","DOI":"10.3390\/s140610691","article-title":"Detecting Falls with Wearable Sensors Using Machine Learning Techniques","volume":"14","author":"Barshan","year":"2014","journal-title":"Sensors"},{"key":"ref_157","unstructured":"Tits, M., Laraba, S., Caulier, E., Tilmanne, J., and Dutoit, T. (2020, March 20). UMONS-TAICHI. Available online: http:\/\/github.com\/numediart\/UMONS-TAICHI."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1016\/j.dib.2018.05.088","article-title":"UMONS-TAICHI: A Multimodal Motion Capture Dataset of Expertise in Taijiquan Gestures","volume":"19","author":"Tits","year":"2018","journal-title":"Data Brief"},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Reiss, A., Indlekofer, I., Schmidt, P., and Van Laerhoven, K. (2019). Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks. Sensors, 19.","DOI":"10.3390\/s19143079"},{"key":"ref_160","unstructured":"Niemann, F., Reining, C., Moya Rueda, F., Nair, N.R., Steffens, J.A., Fink, G.A., and ten Hompel, M. (2020, June 01). Logistic Activity Recognition Challenge (LARa)\u2014A Motion Capture and Inertial Measurement Dataset. Available online: https:\/\/doi.org\/10.5281\/zenodo.3862782."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/15\/4083\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:50:48Z","timestamp":1760176248000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/15\/4083"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,22]]},"references-count":160,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20154083"],"URL":"https:\/\/doi.org\/10.3390\/s20154083","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,22]]}}}