By simply attaching sensor nodes to physical objects with no information about the objects, the method proposed in this article infers the type of the physical indoor objects and the states they are in. Assuming that an object has its own states that have transitions represented by a state transition diagram, we prepare the state transition diagrams for such indoor objects as a door, a drawer, a chair, and a locker. The method determines the presumed state transition diagram from prepared diagrams that matches sensor data collected from people\u2019s daily living for a certain period. A 2 week experiment shows that the method achieves high accuracy of inferring objects to which sensor nodes are attached. The method allows us to introduce ubiquitous sensor environments by simply attaching sensor nodes to physical objects around us.<\/p>","DOI":"10.4018\/jssci.2010101906","type":"journal-article","created":{"date-parts":[[2010,4,16]],"date-time":"2010-04-16T18:23:58Z","timestamp":1271442238000},"page":"86-101","source":"Crossref","is-referenced-by-count":1,"title":["Estimating which Object Type a Sensor Node is Attached to in Ubiquitous Sensor Environment"],"prefix":"10.4018","volume":"2","author":[{"given":"Takuya","family":"Maekawa","sequence":"first","affiliation":[{"name":"NTT Communication Science Laboratories, Japan"}]},{"given":"Yutaka","family":"Yanagisawa","sequence":"additional","affiliation":[{"name":"NTT Communication Science Laboratories, Japan"}]},{"given":"Takeshi","family":"Okadome","sequence":"additional","affiliation":[{"name":"NTT Communication Science Laboratories, Japan"}]}],"member":"2432","reference":[{"key":"jssci.2010101906-0","doi-asserted-by":"publisher","DOI":"10.1145\/182.358434"},{"key":"jssci.2010101906-1","first-page":"1","article-title":"Activity recognition from user-annotated acceleration data. In","volume":"2004","author":"L.Bao","year":"2004","journal-title":"Proceedings of PERVASIVE"},{"key":"jssci.2010101906-2","first-page":"107","article-title":"Some assembly required: Supporting end-user sensor installation in domestic ubiquitous computing environments. In","volume":"2004","author":"C.Beckmann","year":"2004","journal-title":"Proceedings of Ubicomp"},{"key":"jssci.2010101906-3","unstructured":"Bilmes, J. A. (1998). A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models (Tech. Rep. TR-97-021). Berkeley, CA: International Computer Science Institute and Computer Science Division, University of California Berkeley."},{"key":"jssci.2010101906-4","unstructured":"Freksa, C. (1991). Conceptual neighborhood and its role in temporal and spatial reasoning. In Proceedings of the IMACS Workshop on Decision Support Systems and Qualitative Reasoning (pp. 181-187)."},{"key":"jssci.2010101906-5","doi-asserted-by":"crossref","unstructured":"Intille, S. S., Rondoni, J., Kukla, C., Anacona, I., & Bao, L. (2003). A context-aware experience sampling tool. In Proceedings of CHI2003: Extended Abstracts. ACM Publishing.","DOI":"10.1145\/765891.766101"},{"key":"jssci.2010101906-6","first-page":"157","article-title":"Tools for studying behavior and technology in natural settings. In","volume":"2003","author":"S. S.Intille","year":"2003","journal-title":"Proceedings of UbiComp"},{"key":"jssci.2010101906-7","doi-asserted-by":"crossref","unstructured":"Kidd, C., Orr, R., Abowd, G. D., Atkeson, C. G., Essa, I. A., MacIntyre, B., et al. (1999). The aware home: A living laboratory for ubiquitous computing research. In CoBuild99 (LNCS 1670, pp. 191-198).","DOI":"10.1007\/10705432_17"},{"key":"jssci.2010101906-8","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2004.7"},{"key":"jssci.2010101906-9","doi-asserted-by":"publisher","DOI":"10.1109\/97.736233"},{"key":"jssci.2010101906-10","first-page":"158","article-title":"Activity recognition in the home using simple and ubiquitous sensors. In","volume":"2004","author":"E. M.Tapia","year":"2004","journal-title":"Proceedings of Pervasive"}],"container-title":["International Journal of Software Science and Computational Intelligence"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=39107","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T04:11:50Z","timestamp":1654143110000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/jssci.2010101906"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2010,1,1]]},"references-count":11,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2010,1]]}},"URL":"https:\/\/doi.org\/10.4018\/jssci.2010101906","relation":{},"ISSN":["1942-9045","1942-9037"],"issn-type":[{"value":"1942-9045","type":"print"},{"value":"1942-9037","type":"electronic"}],"subject":[],"published":{"date-parts":[[2010,1,1]]}}}