{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:18:10Z","timestamp":1777889890560,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T00:00:00Z","timestamp":1593388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["12\/RC\/2289-P2"],"award-info":[{"award-number":["12\/RC\/2289-P2"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["13\/RC\/2077-CONNECT"],"award-info":[{"award-number":["13\/RC\/2077-CONNECT"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001588","name":"Enterprise Ireland","doi-asserted-by":"publisher","award":["IR20140024"],"award-info":[{"award-number":["IR20140024"]}],"id":[{"id":"10.13039\/501100001588","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independent performance, whether a model trained with data from many users achieves better subject-independent performance than one trained with data from a single person, and whether one trained with data from a single specific target user performs better for that user than one trained with data from many. To those ends, we compare four popular machine learning algorithms\u2019 subject-dependent and subject-independent performances across eight datasets using three different personalisation\u2013generalisation approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). We further consider three different ways to construct such an ensemble: unweighted,    \u03ba   -weighted, and baseline-feature-weighted. Our analysis shows that PSMs outperform PIMs by 43.5% in terms of their subject-dependent performances, whereas PIMs outperform PSMs by 55.9% and    \u03ba   -weighted EPSMs\u2014the best-performing EPSM type\u2014by 16.4% in terms of the subject-independent performance.<\/jats:p>","DOI":"10.3390\/s20133647","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T11:17:17Z","timestamp":1593429437000},"page":"3647","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2163-2168","authenticated-orcid":false,"given":"Sebastian","family":"Scheurer","sequence":"first","affiliation":[{"name":"Insight Centre for Data Analytics, School of Computer Science and Information Technology, University College Cork, T12 XF62 Cork, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7752-2240","authenticated-orcid":false,"given":"Salvatore","family":"Tedesco","sequence":"additional","affiliation":[{"name":"Tyndall National Institute, University College Cork, T12 R5CP Cork, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5522-2597","authenticated-orcid":false,"given":"Brendan","family":"O\u2019Flynn","sequence":"additional","affiliation":[{"name":"Insight Centre for Data Analytics, School of Computer Science and Information Technology, University College Cork, T12 XF62 Cork, Ireland"},{"name":"Tyndall National Institute, University College Cork, T12 R5CP Cork, Ireland"},{"name":"CONNECT Centre for Future Networks and Communications, Tyndall National Institute, University College Cork, T12 R5CP Cork, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenneth N.","family":"Brown","sequence":"additional","affiliation":[{"name":"Insight Centre for Data Analytics, School of Computer Science and Information Technology, University College Cork, T12 XF62 Cork, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Scheurer, S., Tedesco, S., Brown, K.N., and O\u2019Flynn, B. (2019, January 16\u201317). Subject-dependent and-independent human activity recognition with person-specific and-independent models. Proceedings of the 6th international Workshop on Sensor-based Activity Recognition and Interaction, Rostock, Germany.","DOI":"10.1145\/3361684.3361689"},{"key":"ref_2","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."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S.T., Tr\u00f6ster, G., Mill\u00e1n, J.d.R., and Roggen, D. (2013). The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern Recognit. Lett., 34.","DOI":"10.1016\/j.patrec.2012.12.014"},{"key":"ref_4","unstructured":"Jiang, W., and Yin, Z. (, January October). Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks. Proceedings of the 23rd ACM International Conference on Multimedia, New York, NY, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wu, X., and Luo, D. (2015, January 9\u201311). Recognizing Human Activities from Raw Accelerometer Data Using Deep Neural Networks. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.","DOI":"10.1109\/ICMLA.2015.48"},{"key":"ref_6","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_7","unstructured":"Hammerla, N.Y., Halloran, S., and Pl\u00f6tz, T. (2016). Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., and Havinga, P.J.M. (2014). Fusion of Smartphone Motion Sensors for Physical Activity Recognition. Sensors, 14.","DOI":"10.3390\/s140610146"},{"key":"ref_9","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_10","unstructured":"Jordao, A., Nazare, A.C., Sena, J., and Schwartz, W.R. (2019). Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art. arXiv."},{"key":"ref_11","unstructured":"Pecchia, L., Chen, L.L., Nugent, C., and Bravo, J. (2014). mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications. International Workshop on Ambient Assisted Living, Springer."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1016\/j.asoc.2015.01.025","article-title":"On the Use of Ensemble of Classifiers for Accelerometer-Based Activity Recognition","volume":"37","author":"Catal","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Xue, Y. (2015, January 9\u201312). A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer. Proceedings of the 2015 IEEE International Conference on Systems, Kowloon, China.","DOI":"10.1109\/SMC.2015.263"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Abdu-Aguye, M.G., and Gomaa, W. (2019, January 14\u201319). Competitive Feature Extraction for Activity Recognition based on Wavelet Transforms and Adaptive Pooling. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852299"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sztyler, T., and Stuckenschmidt, H. (2016, January 14\u201319). On-body localization of wearable devices: An investigation of position-aware activity recognition. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), Sydney, Australia.","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"ref_17","unstructured":"Vakili, M., Ghamsari, M., and Rezaei, M. (2020). Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"\u00d6zdemir, A.T., and Barshan, B. (2014). Detecting Falls with Wearable Sensors Using Machine Learning Techniques. Sensors, 14.","DOI":"10.3390\/s140610691"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Alharbi, F., and Farrahi, K. (2018, January 17\u201320). A Convolutional Neural Network for Smoking Activity Recognition. Proceedings of the 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), Ostrava, Czech Republic.","DOI":"10.1109\/HealthCom.2018.8531148"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shoaib, M., Scholten, H., Havinga, P.J.M., and Incel, O.D. (2016, January 14\u201316). A hierarchical lazy smoking detection algorithm using smartwatch sensors. Proceedings of the 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), Munich, Germany.","DOI":"10.1109\/HealthCom.2016.7749439"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. (2004). Activity Recognition from User-Annotated Acceleration Data. International Conference on Pervasive Computing, Springer.","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"ref_22","unstructured":"Weiss, G.M., and Lockhart, J.W. (2012). The Impact of Personalization on Smartphone-Based Activity Recognition. Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bulling, A., Blanke, U., and Schiele, B. (2014). A Tutorial on Human Activity Recognition Using Body-worn Inertial. Sensors, 46.","DOI":"10.1145\/2499621"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Scheurer, S., Tedesco, S., Brown, K.N., and O\u2019Flynn, B. (2017, January 9\u201312). Human Activity Recognition for Emergency First Responders via Body-Worn Inertial Sensors. Proceedings of the 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Eindhoven, The Netherlands.","DOI":"10.1109\/BSN.2017.7935994"},{"key":"ref_25","unstructured":"Jones, E., Oliphant, T., and Peterson, P. (2020, May 27). SciPy: Open Source Scientific Tools for Python. Available online: https:\/\/www.scipy.org."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCSE.2011.37","article-title":"The NumPy Array: A Structure for Efficient Numerical Computation","volume":"13","author":"Colbert","year":"2011","journal-title":"Comput. Sci. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"McKinney, W. (2010, January June). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA.","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"ref_28","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_29","first-page":"42","article-title":"GNU Parallel\u2014The Command-Line Power Tool","volume":"36","author":"Tange","year":"2011","journal-title":"Login Usenix Mag."},{"key":"ref_30","unstructured":"R Core Team (2020, May 20). R: A Language and Environment for Statistical Computing. Available online: https:\/\/www.r-project.org."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bates, D., M\u00e4chler, M., Bolker, B., and Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw., 67.","DOI":"10.18637\/jss.v067.i01"},{"key":"ref_32","unstructured":"Lenth, R. (2020, May 10). emmeans: Estimated Marginal Means, aka Least-Squares Means. Available online: https:\/\/cran.r-project.org\/package=emmeans."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITB.2005.856864","article-title":"Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring","volume":"10","author":"Karantonis","year":"2006","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gelman, A., and Hill, J. (2006). Data Analysis Using Regression and Multilevel\/Hierarchical Models, Cambridge University Press.","DOI":"10.1017\/CBO9780511790942"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/13\/3647\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:44:36Z","timestamp":1760175876000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/13\/3647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,29]]},"references-count":34,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20133647"],"URL":"https:\/\/doi.org\/10.3390\/s20133647","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,29]]}}}