{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T03:49:39Z","timestamp":1793504979962,"version":"4.5.0"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Existing activity tracker datasets for human activity recognition are typically obtained by having participants perform predefined activities in an enclosed environment under supervision. This results in small datasets with a limited number of activities and heterogeneity, lacking the mixed and nuanced movements normally found in free-living scenarios. As such, models trained on laboratory-style datasets may not generalise out of sample. To address this problem, we introduce a new dataset involving wrist-worn accelerometers, wearable cameras, and sleep diaries, enabling data collection for over 24 hours in a free-living setting. The result is CAPTURE-24, a large activity tracker dataset collected in the wild from 151 participants, amounting to 3883 hours of accelerometer data, of which 2562 hours are annotated. CAPTURE-24 is two to three orders of magnitude larger than existing publicly available datasets, which is critical to developing accurate human activity recognition models.<\/jats:p>","DOI":"10.1038\/s41597-024-03960-3","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T03:02:19Z","timestamp":1729047739000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition"],"prefix":"10.1038","volume":"11","author":[{"given":"Shing","family":"Chan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5944-1925","authenticated-orcid":false,"given":"Yuan","family":"Hang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7570-4801","authenticated-orcid":false,"given":"Catherine","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aidan","family":"Acquah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abram","family":"Schonfeldt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Gershuny","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aiden","family":"Doherty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"3960_CR1","doi-asserted-by":"crossref","unstructured":"Creagh, A. P. et al. Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis. MedRxiv 2022\u201311 (2022).","DOI":"10.1101\/2022.11.18.22282305"},{"key":"3960_CR2","doi-asserted-by":"crossref","unstructured":"Schalkamp, A.-K., Peall, K. J., Harrison, N. A. & Sandor, C. Wearable movement-tracking data identify parkinson\u2019s disease years before clinical diagnosis. Nature Medicine 1\u20139 (2023).","DOI":"10.1101\/2022.11.28.22282809"},{"key":"3960_CR3","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-40917-3","volume":"14","author":"AS Gupta","year":"2023","unstructured":"Gupta, A. S., Patel, S., Premasiri, A. & Vieira, F. At-home wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis. Nature Communications 14, 5080 (2023).","journal-title":"Nature Communications"},{"key":"3960_CR4","doi-asserted-by":"publisher","first-page":"2301","DOI":"10.1038\/s41591-022-02012-w","volume":"28","author":"H Master","year":"2022","unstructured":"Master, H. et al. Association of step counts over time with the risk of chronic disease in the all of us research program. Nature medicine 28, 2301\u20132308 (2022).","journal-title":"Nature medicine"},{"key":"3960_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-26174-1","volume":"8","author":"M Willetts","year":"2018","unstructured":"Willetts, M., Hollowell, S., Aslett, L., Holmes, C. & Doherty, A. Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 uk biobank participants. Scientific reports 8, 1\u201310 (2018).","journal-title":"Scientific reports"},{"key":"3960_CR6","doi-asserted-by":"crossref","unstructured":"Walmsley, R. et al. Reallocating time from machine-learned sleep, sedentary behaviour or light physical activity to moderate-to-vigorous physical activity is associated with lower cardiovascular disease risk. medRxiv (2020).","DOI":"10.1101\/2020.11.10.20227769"},{"key":"3960_CR7","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1177\/0081175019884591","volume":"50","author":"J Gershuny","year":"2020","unstructured":"Gershuny, J. et al. Testing self-report time-use diaries against objective instruments in real time. Sociological Methodology 50, 318\u2013349 (2020).","journal-title":"Sociological Methodology"},{"key":"3960_CR8","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1038\/s41562-022-01291-0","volume":"6","author":"SS Jonasdottir","year":"2022","unstructured":"Jonasdottir, S. S., Bagrow, J. & Lehmann, S. Sleep during travel balances individual sleep needs. Nature Human Behaviour 6, 691\u2013699 (2022).","journal-title":"Nature Human Behaviour"},{"key":"3960_CR9","doi-asserted-by":"publisher","DOI":"10.1186\/s12966-023-01537-8","volume":"20","author":"Y Chen","year":"2023","unstructured":"Chen, Y. et al. Device-measured movement behaviours in over 20,000 china kadoorie biobank participants. International Journal of Behavioral Nutrition and Physical Activity 20, 138 (2023).","journal-title":"International Journal of Behavioral Nutrition and Physical Activity"},{"key":"3960_CR10","doi-asserted-by":"publisher","first-page":"e0169649","DOI":"10.1371\/journal.pone.0169649","volume":"12","author":"A Doherty","year":"2017","unstructured":"Doherty, A. et al. Large scale population assessment of physical activity using wrist worn accelerometers: The uk biobank study. PloS one 12, e0169649 (2017).","journal-title":"PloS one"},{"key":"3960_CR11","doi-asserted-by":"publisher","first-page":"e0142198","DOI":"10.1371\/journal.pone.0142198","volume":"10","author":"P Kelly","year":"2015","unstructured":"Kelly, P. et al. Developing a method to test the validity of 24 hour time use diaries using wearable cameras: a feasibility pilot. PLoS One 10, e0142198 (2015).","journal-title":"PLoS One"},{"key":"3960_CR12","unstructured":"of the European Commission, S. O. et al. Harmonised european time use surveys, 2008 guidelines. Office for Official Publications of the European Communities (2009)."},{"key":"3960_CR13","doi-asserted-by":"publisher","first-page":"2333","DOI":"10.1038\/s41366-019-0352-x","volume":"43","author":"T White","year":"2019","unstructured":"White, T. et al. Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study. International journal of obesity 43, 2333\u20132342 (2019).","journal-title":"International journal of obesity"},{"key":"3960_CR14","unstructured":"Ladha, C., Ladha, K., Jackson, D. & Olivier, P. Shaker table validation of openmovement ax3 accelerometer. In Ahmerst (ICAMPAM 2013 AMHERST): In 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement, 69\u201370 (2013)."},{"key":"3960_CR15","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.amepre.2012.11.008","volume":"44","author":"AR Doherty","year":"2013","unstructured":"Doherty, A. R. et al. Wearable cameras in health: the state of the art and future possibilities. American journal of preventive medicine 44, 320\u2013323 (2013).","journal-title":"American journal of preventive medicine"},{"key":"3960_CR16","doi-asserted-by":"crossref","unstructured":"Hodges, S. et al. Sensecam: A retrospective memory aid. In International Conference on Ubiquitous Computing, 177\u2013193 (Springer, 2006).","DOI":"10.1007\/11853565_11"},{"key":"3960_CR17","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1123\/jmpb.2020-0038","volume":"4","author":"J Martinez","year":"2021","unstructured":"Martinez, J. et al. Validation of wearable camera still images to assess posture in free-living conditions. Journal for the measurement of physical behaviour 4, 47\u201352 (2021).","journal-title":"Journal for the measurement of physical behaviour"},{"key":"3960_CR18","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.amepre.2012.11.006","volume":"44","author":"P Kelly","year":"2013","unstructured":"Kelly, P. et al. Ethics of using wearable cameras devices in health behaviour research. Am J Prev Med 44, 314\u2013319 (2013).","journal-title":"Am J Prev Med"},{"key":"3960_CR19","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1249\/MSS.0b013e31821ece12","volume":"43","author":"BE Ainsworth","year":"2011","unstructured":"Ainsworth, B. E. et al. 2011 compendium of physical activities: a second update of codes and met values. Med Sci Sports Exerc 43, 1575\u20131581 (2011).","journal-title":"Med Sci Sports Exerc"},{"key":"3960_CR20","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1080\/09658211.2010.509732","volume":"19","author":"AR Doherty","year":"2011","unstructured":"Doherty, A. R., Moulin, C. J. & Smeaton, A. F. Automatically assisting human memory: A sensecam browser. Memory 19, 785\u2013795 (2011).","journal-title":"Memory"},{"key":"3960_CR21","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1152\/japplphysiol.00421.2014","volume":"117","author":"VT Van Hees","year":"2014","unstructured":"Van Hees, V. T. et al. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. Journal of applied physiology 117, 738\u2013744 (2014).","journal-title":"Journal of applied physiology"},{"key":"3960_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2499621","volume":"46","author":"A Bulling","year":"2014","unstructured":"Bulling, A., Blanke, U. & Schiele, B. A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Surveys (CSUR) 46, 1\u201333 (2014).","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"3960_CR23","unstructured":"Chen, C., Liaw, A. & Breiman, L. Using random forest to learn imbalanced data. Tech. Rep. 666, University of California, Berkeley (2004)."},{"key":"3960_CR24","doi-asserted-by":"crossref","unstructured":"Chen, T. & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785\u2013794 (2016).","DOI":"10.1145\/2939672.2939785"},{"key":"3960_CR25","unstructured":"Bergstra, J., Yamins, D. & Cox, D. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International conference on machine learning, 115\u2013123 (PMLR, 2013)."},{"key":"3960_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Identity mappings in deep residual networks. In ECCV, 630\u2013645 (Springer, 2016).","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"3960_CR27","unstructured":"Zhang, R. Making convolutional networks shift-invariant again. In International conference on machine learning, 7324\u20137334 (PMLR, 2019)."},{"key":"3960_CR28","unstructured":"Li, L. et al. A system for massively parallel hyperparameter tuning. Proceedings of Machine Learning and Systems 2 (MLSys 2020)."},{"key":"3960_CR29","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural computation 9, 1735\u20131780 (1997).","journal-title":"Neural computation"},{"key":"3960_CR30","doi-asserted-by":"crossref","unstructured":"Twomey, N. et al. A comprehensive study of activity recognition using accelerometers. In Informatics, vol. 5, 27 (Multidisciplinary Digital Publishing Institute, 2018).","DOI":"10.3390\/informatics5020027"},{"key":"3960_CR31","doi-asserted-by":"publisher","first-page":"579","DOI":"10.2307\/2340126","volume":"75","author":"GU Yule","year":"1912","unstructured":"Yule, G. U. On the methods of measuring association between two attributes. Journal of the Royal Statistical Society 75, 579\u2013652 (1912).","journal-title":"Journal of the Royal Statistical Society"},{"key":"3960_CR32","unstructured":"Cram\u00e9r, H.Mathematical Methods of Statistics (PMS-9), Volume 9 (Princeton university press, 2016)."},{"key":"3960_CR33","doi-asserted-by":"crossref","unstructured":"Efron, B.The jackknife, the bootstrap and other resampling plans (SIAM, 1982).","DOI":"10.1137\/1.9781611970319"},{"key":"3960_CR34","unstructured":"Loshchilov, I. & Hutter, F. Sgdr: Stochastic gradient descent with warm restarts. ICLR (2017)."},{"key":"3960_CR35","doi-asserted-by":"crossref","unstructured":"Smith, L. N. Cyclical learning rates for training neural networks. In 2017 IEEE winter conference on applications of computer vision (WACV), 464\u2013472 (IEEE, 2017).","DOI":"10.1109\/WACV.2017.58"},{"key":"3960_CR36","doi-asserted-by":"crossref","unstructured":"Um, T. T. et al. Data augmentation of wearable sensor data for parkinson\u2019s disease monitoring using convolutional neural networks. In Proceedings of the 19th ACM International Conference on Multimodal Interaction, 216\u2013220 (2017).","DOI":"10.1145\/3136755.3136817"},{"key":"3960_CR37","doi-asserted-by":"publisher","unstructured":"Chan Chang, S. et al. Capture-24: Activity tracker dataset for human activity recognition, https:\/\/doi.org\/10.5287\/bodleian:NGx0JOMP5 (2021).","DOI":"10.5287\/bodleian:NGx0JOMP5"},{"key":"3960_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-018-07743-4","volume":"9","author":"A Doherty","year":"2018","unstructured":"Doherty, A. et al. Gwas identifies 14 loci for device-measured physical activity and sleep duration. Nature communications 9, 1\u20138 (2018).","journal-title":"Nature communications"},{"key":"3960_CR39","doi-asserted-by":"crossref","unstructured":"Walmsley, R. et al. Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease. British journal of sports medicine (2021).","DOI":"10.1136\/bjsports-2021-104050"},{"key":"3960_CR40","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/s16010115","volume":"16","author":"FJ Ord\u00f3\u00f1ez","year":"2016","unstructured":"Ord\u00f3\u00f1ez, F. J. & Roggen, D. Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16, 115 (2016).","journal-title":"Sensors"},{"key":"3960_CR41","doi-asserted-by":"crossref","unstructured":"Yuan, H. et al. Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality. medRxiv (2023).","DOI":"10.2139\/ssrn.4508431"},{"key":"3960_CR42","doi-asserted-by":"crossref","unstructured":"Haresamudram, H. et al. Masked reconstruction based self-supervision for human activity recognition. In Proceedings of the 2020 ACM International Symposium on Wearable Computers, 45\u201349 (2020).","DOI":"10.1145\/3410531.3414306"},{"key":"3960_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3328932","volume":"3","author":"A Saeed","year":"2019","unstructured":"Saeed, A., Ozcelebi, T. & Lukkien, J. Multi-task self-supervised learning for human activity detection. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 1\u201330 (2019).","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"3960_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3550299","volume":"6","author":"H Haresamudram","year":"2022","unstructured":"Haresamudram, H., Essa, I. & Pl\u00f6tz, T. Assessing the state of self-supervised human activity recognition using wearables. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 1\u201347 (2022).","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"3960_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3517246","volume":"6","author":"Y Jain","year":"2022","unstructured":"Jain, Y., Tang, C. I., Min, C., Kawsar, F. & Mathur, A. Collossl: Collaborative self-supervised learning for human activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 1\u201328 (2022).","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"3960_CR46","doi-asserted-by":"publisher","unstructured":"Yuan, H. et al. Self-supervised learning for human activity recognition using 700,000 person-days of wearable data. npj Digital Medicine, https:\/\/doi.org\/10.1038\/s41746-024-01062-3 (2024)","DOI":"10.1038\/s41746-024-01062-3"},{"key":"3960_CR47","doi-asserted-by":"crossref","unstructured":"Tong, C., Tailor, S. A. & Lane, N. D. Are accelerometers for activity recognition a dead-end? In Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications, 39\u201344 (2020).","DOI":"10.1145\/3376897.3377867"},{"key":"3960_CR48","doi-asserted-by":"publisher","first-page":"133190","DOI":"10.1109\/ACCESS.2019.2940729","volume":"7","author":"GM Weiss","year":"2019","unstructured":"Weiss, G. M., Yoneda, K. & Hayajneh, T. Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living. IEEE Access 7, 133190\u2013133202, https:\/\/doi.org\/10.1109\/ACCESS.2019.2940729 (2019).","journal-title":"IEEE Access"},{"key":"3960_CR49","doi-asserted-by":"publisher","unstructured":"Ba\u00f1os, O. et al. A benchmark dataset to evaluate sensor displacement in activity recognition. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 1026\u20131035, https:\/\/doi.org\/10.1145\/2370216.2370437 (ACM, Pittsburgh Pennsylvania, 2012).","DOI":"10.1145\/2370216.2370437"},{"key":"3960_CR50","doi-asserted-by":"publisher","unstructured":"Small, S. R. et al. Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank. MSSE, https:\/\/doi.org\/10.1249\/mss.0000000000003478 (2024).","DOI":"10.1249\/mss.0000000000003478"},{"key":"3960_CR51","doi-asserted-by":"publisher","unstructured":"Hoelzemann, A., Romero, J. L., Bock, M., Laerhoven, K. V. & Lv, Q. Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors. Sensors 23, https:\/\/doi.org\/10.3390\/s23135879 (2023).","DOI":"10.3390\/s23135879"},{"key":"3960_CR52","doi-asserted-by":"publisher","unstructured":"Berlin, E. & Van Laerhoven, K. Detecting leisure activities with dense motif discovery. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 250\u2013259, https:\/\/doi.org\/10.1145\/2370216.2370257 (ACM, Pittsburgh Pennsylvania, 2012).","DOI":"10.1145\/2370216.2370257"},{"key":"3960_CR53","doi-asserted-by":"publisher","unstructured":"Mattfeld, R., Jesch, E. & Hoover, A. A new dataset for evaluating pedometer performance. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 865\u2013869, https:\/\/doi.org\/10.1109\/BIBM.2017.8217769 (IEEE, Kansas City, MO, 2017).","DOI":"10.1109\/BIBM.2017.8217769"},{"key":"3960_CR54","doi-asserted-by":"publisher","unstructured":"Scholl, P. M., Wille, M. & Van Laerhoven, K. Wearables in the wet lab: A laboratory system for capturing and guiding experiments. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 589\u2013599, https:\/\/doi.org\/10.1145\/2750858.2807547 (ACM, Osaka Japan, 2015).","DOI":"10.1145\/2750858.2807547"},{"key":"3960_CR55","doi-asserted-by":"publisher","unstructured":"Sztyler, T. & Stuckenschmidt, H. On-body localization of wearable devices: An investigation of position-aware activity recognition. In 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), 1\u20139, https:\/\/doi.org\/10.1109\/PERCOM.2016.7456521 (IEEE, Sydney, Australia, 2016).","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"3960_CR56","doi-asserted-by":"publisher","unstructured":"Brunner, G., Melnyk, D., Sigf\u00fasson, B. & Wattenhofer, R. Swimming style recognition and lap counting using a smartwatch and deep learning. In Proceedings of the 23rd International Symposium on Wearable Computers, 23\u201331, https:\/\/doi.org\/10.1145\/3341163.3347719 (ACM, London United Kingdom, 2019).","DOI":"10.1145\/3341163.3347719"},{"key":"3960_CR57","doi-asserted-by":"publisher","unstructured":"Bock, M., Kuehne, H., Van Laerhoven, K. & Moeller, M. WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition, https:\/\/doi.org\/10.48550\/ARXIV.2304.05088 (2023).","DOI":"10.48550\/ARXIV.2304.05088"},{"key":"3960_CR58","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1109\/THMS.2023.3275774","volume":"53","author":"Y Yan","year":"2023","unstructured":"Yan, Y. et al. Topological Nonlinear Analysis of Dynamical Systems in Wearable Sensor-Based Human Physical Activity Inference. IEEE Transactions on Human-Machine Systems 53, 792\u2013801, https:\/\/doi.org\/10.1109\/THMS.2023.3275774 (2023).","journal-title":"IEEE Transactions on Human-Machine Systems"},{"key":"3960_CR59","doi-asserted-by":"publisher","first-page":"792065","DOI":"10.3389\/fcomp.2021.792065","volume":"3","author":"M Ciliberto","year":"2021","unstructured":"Ciliberto, M., Fortes Rey, V., Calatroni, A., Lukowicz, P. & Roggen, D. Opportunity++: A Multimodal Dataset for Video- and Wearable, Object and Ambient Sensors-Based Human Activity Recognition. Frontiers in Computer Science 3, 792065, https:\/\/doi.org\/10.3389\/fcomp.2021.792065 (2021).","journal-title":"Frontiers in Computer Science"},{"key":"3960_CR60","doi-asserted-by":"publisher","unstructured":"Roggen, D. et al. Collecting complex activity datasets in highly rich networked sensor environments. In 2010 Seventh International Conference on Networked Sensing Systems (INSS), 233\u2013240, https:\/\/doi.org\/10.1109\/INSS.2010.5573462 (IEEE, Kassel, Germany, 2010).","DOI":"10.1109\/INSS.2010.5573462"},{"key":"3960_CR61","unstructured":"Yang, A. Y., Kuryloski, P. & Bajcsy, R. WARD: A Wearable Action Recognition Database (2009)."},{"key":"3960_CR62","doi-asserted-by":"publisher","unstructured":"Reiss, A. & Stricker, D. Introducing a New Benchmarked Dataset for Activity Monitoring. In 2012 16th International Symposium on Wearable Computers, 108\u2013109, https:\/\/doi.org\/10.1109\/ISWC.2012.13 (IEEE, Newcastle, United Kingdom, 2012).","DOI":"10.1109\/ISWC.2012.13"},{"key":"3960_CR63","unstructured":"Frade, F. D. l. T. et al. Guide to the Carnegie Mellon University Multimodal Activity (CMU-MMAC) Database. Tech. Rep. CMU-RI-TR-08-22, Carnegie Mellon University, Pittsburgh, PA (2008)."},{"key":"3960_CR64","doi-asserted-by":"publisher","unstructured":"Zappi, P. et al. Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection. In Verdone, R. (ed.) Wireless Sensor Networks, vol. 4913, 17\u201333, https:\/\/doi.org\/10.1007\/978-3-540-77690-1_2 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2008).","DOI":"10.1007\/978-3-540-77690-1_2"},{"key":"3960_CR65","doi-asserted-by":"publisher","unstructured":"Bruno, B., Mastrogiovanni, F., Sgorbissa, A., Vernazza, T. & Zaccaria, R. Analysis of human behavior recognition algorithms based on acceleration data. In 2013 IEEE International Conference on Robotics and Automation, 1602\u20131607, https:\/\/doi.org\/10.1109\/ICRA.2013.6630784 (IEEE, Karlsruhe, Germany, 2013).","DOI":"10.1109\/ICRA.2013.6630784"},{"key":"3960_CR66","doi-asserted-by":"publisher","unstructured":"Banos, O. et al. mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications. In Pecchia, L., Chen, L. L., Nugent, C. & Bravo, J. (eds.) Ambient Assisted Living and Daily Activities, vol. 8868, 91\u201398, https:\/\/doi.org\/10.1007\/978-3-319-13105-4_14 (Springer International Publishing, Cham, 2014).","DOI":"10.1007\/978-3-319-13105-4_14"},{"key":"3960_CR67","doi-asserted-by":"publisher","unstructured":"Chen, C., Jafari, R. & Kehtarnavaz, N. UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In 2015 IEEE International Conference on Image Processing (ICIP), 168\u2013172, https:\/\/doi.org\/10.1109\/ICIP.2015.7350781 (IEEE, Quebec City, QC, Canada, 2015).","DOI":"10.1109\/ICIP.2015.7350781"},{"key":"3960_CR68","doi-asserted-by":"publisher","unstructured":"Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R. & Bajcsy, R. Berkeley MHAD: A comprehensive Multimodal Human Action Database. In 2013 IEEE Workshop on Applications of Computer Vision (WACV), 53\u201360, https:\/\/doi.org\/10.1109\/WACV.2013.6474999 (IEEE, Clearwater Beach, FL, USA, 2013).","DOI":"10.1109\/WACV.2013.6474999"},{"key":"3960_CR69","doi-asserted-by":"publisher","first-page":"3605","DOI":"10.1016\/j.patcog.2010.04.019","volume":"43","author":"K Altun","year":"2010","unstructured":"Altun, K., Barshan, B. & Tun\u00e7el, O. Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognition 43, 3605\u20133620, https:\/\/doi.org\/10.1016\/j.patcog.2010.04.019 (2010).","journal-title":"Pattern Recognition"},{"key":"3960_CR70","unstructured":"Chen, C., Jafari, R. & Kehtarnavaz, N. UTD Multimodal Human Action Dataset (UTD-MHAD) Kinect V2 (2015)."}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41597-024-03960-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-024-03960-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-024-03960-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T03:04:44Z","timestamp":1729047884000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41597-024-03960-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["3960"],"URL":"https:\/\/doi.org\/10.1038\/s41597-024-03960-3","relation":{},"ISSN":["2052-4463"],"issn-type":[{"value":"2052-4463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]},"assertion":[{"value":"1 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1135"}}