{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:01:08Z","timestamp":1760241668230,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,6,24]],"date-time":"2018-06-24T00:00:00Z","timestamp":1529798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Being aware of a personal context is a promising task for various applications, such as biometry, human-computer interactions, telemonitoring, remote care, mobile marketing and security. The task can be formally defined as the classification of a person being considered into one of predefined labels, which may correspond to his\/her identity, gender, physical properties, the activity that he\/she performs or any other attribute related to the environment being involved. Here, we offer a solution to the problem with a set of multiple motion sensors worn on the wrist. We first provide an annotated and publicly accessible benchmark set for context-awareness through wrist-worn sensors, namely, accelerometers, magnetometers and gyroscopes. Second, we present an evaluation of recent computational methods for two relevant tasks: activity recognition and person identification from hand movements. Finally, we show that fusion of two motion sensors (i.e., accelerometers and magnetometers), leads to higher accuracy for both tasks, compared with the individual use of each sensor type.<\/jats:p>","DOI":"10.3390\/data3030024","type":"journal-article","created":{"date-parts":[[2018,6,25]],"date-time":"2018-06-25T11:03:25Z","timestamp":1529924605000},"page":"24","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["HANDY: A Benchmark Dataset for Context-Awareness via Wrist-Worn Motion Sensors"],"prefix":"10.3390","volume":"3","author":[{"given":"Koray","family":"A\u00e7\u0131c\u0131","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Ba\u015fkent University, Ba\u011fl\u0131ca Kamp\u00fcs\u00fc, Fatih Sultan Mahallesi Eski\u015fehir Yolu 18 Km, Ankara 06790, Turkey"}]},{"given":"\u00c7a\u011fatay Berke","family":"Erda\u015f","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Ba\u015fkent University, Ba\u011fl\u0131ca Kamp\u00fcs\u00fc, Fatih Sultan Mahallesi Eski\u015fehir Yolu 18 Km, Ankara 06790, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4153-0764","authenticated-orcid":false,"given":"Tun\u00e7","family":"A\u015furo\u011flu","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Ba\u015fkent University, Ba\u011fl\u0131ca Kamp\u00fcs\u00fc, Fatih Sultan Mahallesi Eski\u015fehir Yolu 18 Km, Ankara 06790, Turkey"}]},{"given":"Hasan","family":"O\u011ful","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Ba\u015fkent University, Ba\u011fl\u0131ca Kamp\u00fcs\u00fc, Fatih Sultan Mahallesi Eski\u015fehir Yolu 18 Km, Ankara 06790, Turkey"},{"name":"Faculty of Computer Science, \u00d8stfold University College, P.O. 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Auditory context awareness via wearable computing. Proceedings of the 1998 Workshop on Perceptual User Interfaces, San Francisco, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"88","DOI":"10.3390\/computers2020088","article-title":"A review on video-based human activity recognition","volume":"2","author":"Ke","year":"2013","journal-title":"Computers"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.cviu.2006.08.002","article-title":"A survey of advances in vision-based human motion capture and analysis","volume":"104","author":"Moeslund","year":"2006","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.cviu.2006.07.006","article-title":"A general method for human activity recognition in video","volume":"104","author":"Robertson","year":"2006","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3389\/frobt.2015.00028","article-title":"A review of human activity recognition methods","volume":"2","author":"Vrigkas","year":"2015","journal-title":"Front. Robot. AI"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MPRV.2004.7","article-title":"Inferring activities from interactions with objects","volume":"3","author":"Philipose","year":"2004","journal-title":"IEEE Pervasive Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fogarty, J., Au, C., and Hudson, S.E. (2006, January 15\u201318). Sensing from the basement: A feasibility study of unobtrusive and low-cost home activity recognition. Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology, Montreux, Switzerland.","DOI":"10.1145\/1166253.1166269"},{"key":"ref_11","unstructured":"Patel, S.N., Robertson, T., Kientz, J.A., Reynolds, M.S., and Abowd, G. (2007, January 16\u201319). At the flick of a switch: Detecting and classifying unique electrical events on the residential power line. Proceedings of the International Conference on Ubiquitous Computing, Innsbruck, Austria."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cohn, G., Gupta, S., Froehlich, J., Larson, E., and Patel, S.N. (2010, January 17\u201320). GasSense: Appliance-level, single-point sensing of gas activity in the home. Proceedings of the International Conference on Pervasive Computing, Newcastle, UK.","DOI":"10.1007\/978-3-642-12654-3_16"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Casale, P., Pujol, O., and Radeva, P. (2011, January 8\u201310). Human activity recognition from accelerometer data using a wearable device. Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis, Las Palmas de Gran Canaria, Spain.","DOI":"10.1007\/978-3-642-21257-4_36"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/TSMCC.2012.2198883","article-title":"Sensor-based activity recognition","volume":"42","author":"Chen","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/TBME.2008.2006190","article-title":"A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data","volume":"56","author":"Preece","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","unstructured":"Ravi, N., Dandekar, N., Mysore, P., and Littman, M.L. (2005, January 9\u201313). Activity Recognition from Accelerometer Data. Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence, Pittsburgh, PA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/COMST.2014.2381246","article-title":"Context-awareness for mobile sensing: A survey and future directions","volume":"18","author":"Liu","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_18","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":"2010","journal-title":"ACM SigKDD Explor. Newsl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.3390\/s100201154","article-title":"Machine learning methods for classifying human physical activity from on-body accelerometers","volume":"10","author":"Mannini","year":"2010","journal-title":"Sensors"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5317","DOI":"10.3390\/s130405317","article-title":"Classification of sporting activities using smartphone accelerometers","volume":"13","author":"Mitchell","year":"2013","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shoaib, M., Scholten, H., and Havinga, P.J.M. (2013, January 18\u201321). Towards physical activity recognition using smartphone sensors. Proceedings of the IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 10th International Conference on Autonomic and Trusted Computing, Vietri sul Mere, Italy.","DOI":"10.1109\/UIC-ATC.2013.43"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.pmcj.2016.09.003","article-title":"Advancing Android activity recognition service with Markov smoother: Practical solutions","volume":"38","author":"Zhong","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1007\/s00779-012-0556-8","article-title":"Activity recognition with hand-worn magnetic sensors","volume":"17","author":"Maekawa","year":"2013","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bruno, B., Mastrogiovanni, F., Sgorbissa, A., Vernazza, T., and Zaccaria, R. (2013, January 6\u201310). Analysis of human behavior recognition algorithms based on acceleration data. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630784"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ugulino, W., Cardador, D., Vega, K., Velloso, E., Milidiu, R., and Fuks, H. (2012, January 20\u201325). Wearable computing: Accelerometers\u2019 data classification of body postures and movements. Proceedings of the 21st Brazilian Symposium on Artificial Intelligence, Curitiba, Brazil.","DOI":"10.1007\/978-3-642-34459-6_6"},{"key":"ref_27","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A public domain dataset for human activity recognition using smartphones. Proceedings of the 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium."},{"key":"ref_28","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 16th IEEE International Symposium on Wearable Computers (ISWC), Newcastle, UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.procs.2016.09.070","article-title":"Integrating features for accelerometer-based activity recognition","volume":"98","author":"Atasoy","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1016\/j.patcog.2015.03.009","article-title":"Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation","volume":"48","author":"Wong","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Caruana, R., and Niculescu-Mizil, A. (2006, January 25\u201329). An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143865"},{"key":"ref_32","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer. [1st ed.]."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/3\/3\/24\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:09:57Z","timestamp":1760195397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/3\/3\/24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,24]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["data3030024"],"URL":"https:\/\/doi.org\/10.3390\/data3030024","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2018,6,24]]}}}