{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:22:08Z","timestamp":1751516528489,"version":"3.41.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030781101"},{"type":"electronic","value":"9783030781118"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78111-8_33","type":"book-chapter","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T23:20:19Z","timestamp":1625268019000},"page":"478-495","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data Cleaning of Binary Sensor Events in Activity Recognition by Cluster-Based Methods"],"prefix":"10.1007","author":[{"given":"Chunyang","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Xia","family":"Que","sequence":"additional","affiliation":[]},{"given":"Yue","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Xiaoman","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Jiaoyun","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Ning","family":"An","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,3]]},"reference":[{"key":"33_CR1","doi-asserted-by":"publisher","unstructured":"Chen, F., Liu, G.: Population aging in China. In: Uhlenberg, P. (ed.) International Handbook of Population Aging, pp. 157\u2013172. Springer, Dordrecht (2009) https:\/\/doi.org\/10.1007\/978-1-4020-8356-3_8","DOI":"10.1007\/978-1-4020-8356-3_8"},{"issue":"3","key":"33_CR2","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","volume":"7","author":"MS Albert","year":"2011","unstructured":"Albert, M.S., et al.: The diagnosis of mild cognitive impairment due to Alzheimer\u2019s disease: recommendations from the national institute on aging-Alzheimer\u2019s association workgroups on diagnostic guidelines for Alzheimer\u2019s disease. Alzheimers Dement. 7(3), 270\u2013279 (2011)","journal-title":"Alzheimers Dement."},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association: 2019 Alzheimer's disease facts and figures. Alzheimer's Dementia 15(3), 321\u2013387 (2019)","DOI":"10.1016\/j.jalz.2019.01.010"},{"issue":"12","key":"33_CR4","doi-asserted-by":"publisher","first-page":"2273","DOI":"10.1111\/j.1532-5415.2011.03732.x","volume":"59","author":"SA Sikkes","year":"2011","unstructured":"Sikkes, S.A., et al.: Do instrumental activities of daily living predict dementia at 1-and 2-year follow-up? Findings from the development of screening guidelines and diagnostic criteria for predementia Alzheimer\u2019s disease study. J. Am. Geriatr. Soc. 59(12), 2273\u20132281 (2011)","journal-title":"J. Am. Geriatr. Soc."},{"issue":"1","key":"33_CR5","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/s12955-016-0493-8","volume":"14","author":"G Torisson","year":"2016","unstructured":"Torisson, G., Stavenow, L., Minthon, L., Londos, E.: Reliability, validity and clinical correlates of the Quality of Life in Alzheimer\u2019s disease (QoL-AD) scale in medical inpatients. Health Qual. Life Outcomes 14(1), 90 (2016)","journal-title":"Health Qual. Life Outcomes"},{"issue":"1","key":"33_CR6","doi-asserted-by":"publisher","first-page":"2517","DOI":"10.1007\/s10586-018-2329-2","volume":"22","author":"I Chandra","year":"2018","unstructured":"Chandra, I., Sivakumar, N., Gokulnath, C.B., Parthasarathy, P.: IoT based fall detection and ambient assisted system for the elderly. Clust. Comput. 22(1), 2517\u20132525 (2018). https:\/\/doi.org\/10.1007\/s10586-018-2329-2","journal-title":"Clust. Comput."},{"key":"33_CR7","series-title":"IFMBE Proceedings","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1007\/978-3-319-03005-0_70","volume-title":"The International Conference on Health Informatics","author":"S Pedro","year":"2014","unstructured":"Pedro, S., Quintas, J., Menezes, P.: Sensor-based detection of Alzheimer\u2019s disease-related behaviors. In: Zhang, Y.-T. (ed.) The International Conference on Health Informatics. IP, vol. 42, pp. 276\u2013279. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-03005-0_70"},{"issue":"3","key":"33_CR8","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1177\/1420326X19891225","volume":"29","author":"J Yu","year":"2020","unstructured":"Yu, J., Hassan, M.T., Bai, Y., An, N., Tam, V.W.: A pilot study monitoring the thermal comfort of the elderly living in nursing homes in Hefei, China, using wireless sensor networks, site measurements and a survey. Indoor Built Environ. 29(3), 449\u2013464 (2020)","journal-title":"Indoor Built Environ."},{"issue":"3","key":"33_CR9","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1109\/THMS.2016.2641388","volume":"47","author":"J Rafferty","year":"2017","unstructured":"Rafferty, J., Nugent, C.D., Liu, J., Chen, L.: From activity recognition to intention recognition for assisted living within smart homes. IEEE Trans. Hum. Mach. Syst. 47(3), 368\u2013379 (2017)","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"33_CR10","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.artmed.2019.01.005","volume":"94","author":"D Arifoglu","year":"2019","unstructured":"Arifoglu, D., Bouchachia, A.: Detection of abnormal behaviour for dementia sufferers using convolutional neural networks. Artif. Intell. Med. 94, 88\u201395 (2019)","journal-title":"Artif. Intell. Med."},{"issue":"3","key":"33_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1525856.1525863","volume":"5","author":"K Ni","year":"2009","unstructured":"Ni, K., et al.: Sensor network data fault types. ACM Trans. Sens. Netw. (TOSN) 5(3), 1\u201329 (2009)","journal-title":"ACM Trans. Sens. Netw. (TOSN)"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Hnat, T.W., Srinivasan, V., Lu, J., Sookoor, T.I., Dawson, R., Stankovic, J., Whitehouse, K.: The hitchhiker\u2019s guide to successful residential sensing deployments. In: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems 2011, pp. 232\u2013245 (2011)","DOI":"10.1145\/2070942.2070966"},{"issue":"7","key":"33_CR13","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/MC.2012.328","volume":"46","author":"DJ Cook","year":"2012","unstructured":"Cook, D.J., Crandall, A.S., Thomas, B.L., Krishnan, N.C.: CASAS: a smart home in a box. Computer 46(7), 62\u201369 (2012)","journal-title":"Computer"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Van Kasteren, T., Noulas, A., Englebienne, G., Kr\u00f6se, B.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing 2008, pp. 1\u20139 (2008)","DOI":"10.1145\/1409635.1409637"},{"key":"33_CR15","doi-asserted-by":"publisher","unstructured":"van Kasteren, T.L., Englebienne, G., Kr\u00f6se, B.J.: Human activity recognition from wireless sensor network data: Benchmark and software. In: Chen, L., Nugent, C., Biswas, J., Hoey, J. (eds.) Activity Recognition in Pervasive Intelligent Environments, pp. 165\u2013186. Springer, Dordrecht (2011) https:\/\/doi.org\/10.2991\/978-94-91216-05-3_8","DOI":"10.2991\/978-94-91216-05-3_8"},{"key":"33_CR16","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.jnca.2016.08.002","volume":"73","author":"A Karkouch","year":"2016","unstructured":"Karkouch, A., Mousannif, H., Al Moatassime, H., Noel, T.: Data quality in internet of things: a state-of-the-art survey. J. Netw. Comput. Appl. 73, 57\u201381 (2016)","journal-title":"J. Netw. Comput. Appl."},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Elnahrawy, E., Nath, B.: Cleaning and querying noisy sensors. In: Proceedings of the 2nd ACM International Conference on Wireless Sensor Networks and Applications 2003, pp. 78\u201387 (2003)","DOI":"10.1145\/941350.941362"},{"issue":"2","key":"33_CR18","doi-asserted-by":"publisher","first-page":"69","DOI":"10.2478\/bsrj-2018-0020","volume":"9","author":"K Kenda","year":"2018","unstructured":"Kenda, K., Mladeni\u0107, D.: Autonomous sensor data cleaning in stream mining setting. Bus. Syst. Res. J. 9(2), 69\u201379 (2018)","journal-title":"Bus. Syst. Res. J."},{"key":"33_CR19","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.enbuild.2015.11.066","volume":"112","author":"Y Hu","year":"2016","unstructured":"Hu, Y., Chen, H., Li, G., Li, H., Xu, R., Li, J.: A statistical training data cleaning strategy for the PCA-based chiller sensor fault detection, diagnosis and data reconstruction method. Energy Build. 112, 270\u2013278 (2016)","journal-title":"Energy Build."},{"issue":"6","key":"33_CR20","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1016\/j.measurement.2010.02.014","volume":"43","author":"J Qu","year":"2010","unstructured":"Qu, J., Zuo, M.J.: Support vector machine based data processing algorithm for wear degree classification of slurry pump systems. Measurement 43(6), 781\u2013791 (2010)","journal-title":"Measurement"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Wang, S., Li, Z., Zhang, X.: Bootstrap sampling based data cleaning and maximum entropy SVMs for large datasets. In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence 2012, pp. 1151\u20131156. IEEE (2012)","DOI":"10.1109\/ICTAI.2012.164"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Jeffery, S.R., Alonso, G., Franklin, M.J., Hong, W., Widom, J.: A pipelined framework for online cleaning of sensor data streams. In: 22nd International Conference on Data Engineering (ICDE 2006), p. 140. IEEE (2006)","DOI":"10.1109\/ICDE.2006.8"},{"key":"33_CR23","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1007\/978-3-030-16681-6_18","volume-title":"Innovations in Bio-Inspired Computing and Applications","author":"P Narkhede","year":"2019","unstructured":"Narkhede, P., Deshpande, S., Walambe, R.: Sensor data cleaning using particle swarm optimization. In: Abraham, A., Gandhi, N., Pant, M. (eds.) IBICA 2018. AISC, vol. 939, pp. 182\u2013191. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-16681-6_18"},{"issue":"4","key":"33_CR24","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1080\/10798587.2016.1152769","volume":"22","author":"J Lei","year":"2016","unstructured":"Lei, J., Bi, H., Xia, Y., Huang, J., Bae, H.: An in-network data cleaning approach for wireless sensor networks. Intell. Autom. Soft Comput. 22(4), 599\u2013604 (2016)","journal-title":"Intell. Autom. Soft Comput."},{"key":"33_CR25","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/978-3-030-32388-2_20","volume-title":"Machine Learning and Intelligent Communications","author":"B Shao","year":"2019","unstructured":"Shao, B., Song, C., Wang, Z., Li, Z., Yu, S., Zeng, P.: Data cleaning based on multi-sensor spatiotemporal correlation. In: Zhai, X.B., Chen, B., Zhu, K. (eds.) MLICOM 2019. LNICSSITE, vol. 294, pp. 235\u2013243. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32388-2_20"},{"key":"33_CR26","first-page":"167","volume":"40","author":"G Wu","year":"2019","unstructured":"Wu, G., Zhang, J.-L., Yuan, D.: Automatically Obtaining K value based on K-means elbow method. Comput. Eng. Softw 40, 167\u2013170 (2019). (in Chinese)","journal-title":"Comput. Eng. Softw"},{"issue":"9\u201310","key":"33_CR27","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.1016\/S0167-8655(03)00003-5","volume":"24","author":"Z He","year":"2003","unstructured":"He, Z., Xu, X., Deng, S.: Discovering cluster-based local outliers. Pattern Recogn. Lett. 24(9\u201310), 1641\u20131650 (2003)","journal-title":"Pattern Recogn. Lett."},{"key":"33_CR28","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd 1996, vol. 34, pp. 226\u2013231 (1996)"},{"key":"33_CR29","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.pmcj.2012.07.003","volume":"10","author":"NC Krishnan","year":"2014","unstructured":"Krishnan, N.C., Cook, D.J.: Activity recognition on streaming sensor data. Pervasive Mob. Comput. 10, 138\u2013154 (2014)","journal-title":"Pervasive Mob. Comput."}],"container-title":["Lecture Notes in Computer Science","Human Aspects of IT for the Aged Population. Supporting Everyday Life Activities"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78111-8_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T22:34:38Z","timestamp":1751495678000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78111-8_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030781101","9783030781118"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78111-8_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"3 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2021.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}