{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T06:11:49Z","timestamp":1759385509570,"version":"3.37.3"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T00:00:00Z","timestamp":1607644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Spanish Ministry of Science and Innovation","doi-asserted-by":"publisher","award":["MINECO-TIN2017-84804-R"],"award-info":[{"award-number":["MINECO-TIN2017-84804-R"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Asturias Regional Government","award":["Grant FCGRUPIN-IDI\/2018\/000226"],"award-info":[{"award-number":["Grant FCGRUPIN-IDI\/2018\/000226"]}]},{"name":"Instituto para la Competitividad Empresarial de Castilla y Le\u00f3n","award":["CCTT2\/18\/BU\/0002"],"award-info":[{"award-number":["CCTT2\/18\/BU\/0002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Fall detection (FD) is a challenging task that has received the attention of the research community in the recent years. This study focuses on FD using data gathered from wearable devices with tri-axial accelerometers (3DACC), developing a solution centered in elderly people living autonomously. This research includes three different ways to improve a FD method: (i) an analysis of the event detection stage, comparing several alternatives, (ii) an evaluation of features to extract for each detected event and (iii) an appraisal of up to 6 different clustering scenarios to split the samples in subsets that might enhance the classification. For each clustering scenario, a specific classification stage is defined. The experimentation includes publicly available simulated fall data sets. Results show the guidelines for defining a more robust and efficient FD method for on-wrist 3DACC wearable devices.<\/jats:p>","DOI":"10.1093\/jigpal\/jzaa064","type":"journal-article","created":{"date-parts":[[2020,11,20]],"date-time":"2020-11-20T02:54:01Z","timestamp":1605840841000},"page":"314-325","source":"Crossref","is-referenced-by-count":4,"title":["Improving wearable-based fall detection with unsupervised learning"],"prefix":"10.1093","volume":"30","author":[{"given":"Mirko","family":"F\u00e1\u00f1ez","sequence":"first","affiliation":[{"name":"Applied Electronics and Artificial Intelligence Group, Instituto Tecnol\u00f3gico de Castilla y Le\u00f3n, Burgos, 09001, Spain"}]},{"given":"Jos\u00e9 R","family":"Villar","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Oviedo, Oviedo, 33005, Spain"}]},{"given":"Enrique","family":"de la Cal","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Oviedo, Oviedo, Asturias, 33005, Spain"}]},{"given":"V\u00edctor M","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University of Oviedo, Gij\u00f3n, 33204, Spain"}]},{"given":"Javier","family":"Sedano","sequence":"additional","affiliation":[{"name":"Applied Electronics and Artificial Intelligence Group, Instituto Tecnol\u00f3gico de Castilla y Le\u00f3n, Burgos, 09001, Spain"}]}],"member":"286","published-online":{"date-parts":[[2020,12,11]]},"reference":[{"key":"2022032112132411900_ref1","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.pmcj.2012.08.003","article-title":"A smartphone-based fall detection system","volume":"8","author":"Abbate","year":"2012","journal-title":"Pervasive and Mobile Computing"},{"key":"2022032112132411900_ref2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.5772\/13802","article-title":"Monitoring of human movements for fall detection and activities recognition in elderly care using wireless sensor network: a survey","volume-title":"Wireless Sensor Networks: Application\u2014Centric Design","author":"Abbate","year":"2010"},{"key":"2022032112132411900_ref3","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.gaitpost.2006.09.012","article-title":"Evaluation of a threshold-based triaxial accelerometer fall detection algorithm","volume":"26","author":"Bourke","year":"2007","journal-title":"Gait and Posture"},{"key":"2022032112132411900_ref4","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.procs.2017.06.110","article-title":"Umafall: a multisensor dataset for the research on automatic fall detection","volume":"110","author":"Casilari","year":"2017","journal-title":"Procedia Computer Science"},{"key":"2022032112132411900_ref5","doi-asserted-by":"crossref","first-page":"4324","DOI":"10.3390\/s17071513","article-title":"Analysis of public datasets for wearable fall detection systems","volume":"17","author":"Casilari","year":"2017","journal-title":"Sensors"},{"key":"2022032112132411900_ref6","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"Smote: synthetic minority over-sampling technique","author":"Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"key":"2022032112132411900_ref7","doi-asserted-by":"crossref","first-page":"19806","DOI":"10.3390\/s141019806","article-title":"Survey on fall detection and fall prevention using wearable and external sensors","volume":"14","author":"Delahoz","year":"2014","journal-title":"Sensors"},{"key":"2022032112132411900_ref8","article-title":"Anomaly intelligent fall detection: mixing user-centered and generalized models","volume-title":"Evaluation for Neurocomputing","author":"Fa\u00f1ez","year":"2019"},{"key":"2022032112132411900_ref9","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.proeng.2014.10.539","article-title":"A smartphone-based detection of fall portents for construction workers","volume":"85","author":"Fang","year":"2014","journal-title":"Procedia Engineering"},{"key":"2022032112132411900_ref10","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.autcon.2017.09.015","article-title":"Accelerometer-based fall-portent detection algorithm for construction tiling operation","volume":"84","author":"Fang","year":"2017","journal-title":"Automation in Construction"},{"key":"2022032112132411900_ref11","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.procs.2017.01.188","article-title":"Smartphone based data mining for fall detection: analysis and design","volume":"105","author":"Hakim","year":"2017","journal-title":"Procedia Computer Science"},{"key":"2022032112132411900_ref12","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1155\/2015\/452078","article-title":"Optimization of an accelerometer and gyroscope-based fall detection algorithm","volume":"2015,","author":"Huynh","year":"2015","journal-title":"Journal of Sensors"},{"key":"2022032112132411900_ref13","doi-asserted-by":"crossref","first-page":"66,","DOI":"10.1186\/1475-925X-12-66","article-title":"Challenges, issues and trends in fall detection systems","volume":"12","author":"Igual","year":"2013","journal-title":"BioMedical Engineering OnLine"},{"key":"2022032112132411900_ref14","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.gaitpost.2008.01.003","article-title":"Comparison of low-complexity fall detection algorithms for body attached accelerometers","volume":"28","author":"Kangas","year":"2008","journal-title":"Gait and Posture"},{"key":"2022032112132411900_ref15","doi-asserted-by":"crossref","first-page":"1350,","DOI":"10.3390\/s18051350","article-title":"Improving fall detection using an on-wrist wearable accelerometer","volume":"18","author":"Khojasteh","year":"2018","journal-title":"Sensors"},{"key":"2022032112132411900_ref16","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-92639-1_31","article-title":"Evaluation of a wrist-based wearable fall detection method","volume-title":"Hybrid Artificial Intelligent Systems. Vol. 10870 of Lecture Notes in Computer Science,","author":"Khojasteh","year":"2018"},{"article-title":"Probability Theory Group (Formerly: E1071), TU Wien - Package \u2019e1071\u2019","year":"2019","author":"Meyer","key":"2022032112132411900_ref17"},{"key":"2022032112132411900_ref18","doi-asserted-by":"crossref","first-page":"10691","DOI":"10.3390\/s140610691","article-title":"Detecting falls with wearable sensors using machine learning techniques","volume":"14","author":"Ozdemir","year":"2014","journal-title":"Sensors"},{"article-title":"Top ten reviews for fall detection of seniors","year":"2018","author":"Purch.com","key":"2022032112132411900_ref19"},{"article-title":"K-Means Clustering in R Stats Package","year":"2019","author":"R Core Team and contributors","key":"2022032112132411900_ref20"},{"article-title":"Functions for Classification - Package \u2018class\u2019","year":"2019","author":"Ripley","key":"2022032112132411900_ref21"},{"key":"2022032112132411900_ref22","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s13748-019-00190-2","article-title":"User-centered fall detection using supervised, on-line learning and transfer learning","volume":"8","author":"Villar","year":"2019","journal-title":"Progress in Artificial Intelligence"},{"key":"2022032112132411900_ref23","first-page":"2015","article-title":"Development of a wearable-sensor-based fall detection system","volume":"11","author":"Wu","year":"2015","journal-title":"International Journal of Telemedicine and Applications"},{"key":"2022032112132411900_ref24","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1155\/2017\/3090343","article-title":"A review on human activity recognition using vision-based method","volume":"2017","author":"Zhang","year":"2017","journal-title":"Journal of Healthcare Engineering"},{"key":"2022032112132411900_ref25","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1007\/978-3-540-37258-5_104","article-title":"Fall detection by wearable sensor and one-class SVM algorithm","volume-title":"Intelligent Computing in Signal Processing and Pattern Recognition","author":"Zhang","year":"2006"}],"container-title":["Logic Journal of the IGPL"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jigpal\/article-pdf\/30\/2\/314\/42901438\/jzaa064.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jigpal\/article-pdf\/30\/2\/314\/42901438\/jzaa064.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T12:18:09Z","timestamp":1647865089000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jigpal\/article\/30\/2\/314\/6030102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,11]]},"references-count":25,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,12,11]]},"published-print":{"date-parts":[[2022,3,25]]}},"URL":"https:\/\/doi.org\/10.1093\/jigpal\/jzaa064","relation":{},"ISSN":["1367-0751","1368-9894"],"issn-type":[{"type":"print","value":"1367-0751"},{"type":"electronic","value":"1368-9894"}],"subject":[],"published-other":{"date-parts":[[2022,4]]},"published":{"date-parts":[[2020,12,11]]}}}