{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:43:26Z","timestamp":1778345006764,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,4,26]],"date-time":"2018-04-26T00:00:00Z","timestamp":1524700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms.<\/jats:p>","DOI":"10.3390\/s18051350","type":"journal-article","created":{"date-parts":[[2018,4,27]],"date-time":"2018-04-27T06:52:23Z","timestamp":1524811943000},"page":"1350","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":126,"title":["Improving Fall Detection Using an On-Wrist Wearable Accelerometer"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0385-7494","authenticated-orcid":false,"given":"Samad Barri","family":"Khojasteh","sequence":"first","affiliation":[{"name":"Sakarya University, 54050 Sakarya, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6024-9527","authenticated-orcid":false,"given":"Jos\u00e9 R.","family":"Villar","sequence":"additional","affiliation":[{"name":"Electric, Electronic, Computers and Systems Engineering Department, University of Oviedo, 33003 Oviedo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Camelia","family":"Chira","sequence":"additional","affiliation":[{"name":"Computer SCience Department, Babes-Bolyai University, 400084 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0937-1882","authenticated-orcid":false,"given":"V\u00edctor M.","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Electric, Electronic, Computers and Systems Engineering Department, University of Oviedo, 33003 Oviedo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrique","family":"De la Cal","sequence":"additional","affiliation":[{"name":"Electric, Electronic, Computers and Systems Engineering Department, University of Oviedo, 33003 Oviedo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1093\/ageing\/afl084","article-title":"Falls in older people: Epidemiology, risk factors and strategies for prevention","volume":"35","author":"Rubenstein","year":"2006","journal-title":"Age Ageing"},{"key":"ref_2","unstructured":"Purch.com (2018, April 25). Top Ten Reviews for Fall Detection of Seniors. Available online: http:\/\/www.toptenreviews.com\/health\/senior-care\/best-fall-detection-sensors\/."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bagala, F., Becker, C., Cappello, A., Chiari, L., Aminian, K., Hausdorff, J.M., Zijlstra, W., and Klenk, J. (2012). Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0037062"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"24","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":"BioMed. Eng. OnLine"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.medengphy.2016.10.014","article-title":"Review of fall detection techniques: A data availability perspective","volume":"39","author":"Khan","year":"2017","journal-title":"Med. Eng. Phys."},{"key":"ref_6","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":"J. Healthc. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.bios.2016.12.001","article-title":"Increasing trend of wearables and multimodal interface for human activity monitoring: A review","volume":"90","author":"Kumari","year":"2017","journal-title":"Biosens. Bioelectron."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1109\/TNSRE.2015.2460373","article-title":"Prior-to- and post-impact fall detection using inertial and barometric altimeter measurements","volume":"24","author":"Sabatini","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sorvala, A., Alasaarela, E., Sorvoja, H., and Myllyla, R. (2012, January 25\u201329). A two-threshold fall detection algorithm for reducing false alarms. Proceedings of the 6th International Symposium on Medical Information and Communication Technology (ISMICT), La Jolla, CA, USA.","DOI":"10.1109\/ISMICT.2012.6203028"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1109\/JSEN.2016.2625099","article-title":"Elder tracking and fall detection system using smart tiles","volume":"17","author":"Daher","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1109\/TNSRE.2010.2070807","article-title":"Barometric pressure and triaxial accelerometry-based falls event detection","volume":"18","author":"Bianchi","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_12","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":"Volume 345","author":"Huang","year":"2006","journal-title":"Intelligent Computing in Signal Processing and Pattern Recognition"},{"key":"ref_13","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 Comput. Sci."},{"key":"ref_14","first-page":"11","article-title":"Development of a wearable-sensor-based fall detection system","volume":"2015","author":"Wu","year":"2015","journal-title":"Int. J. Telemed. Appl."},{"key":"ref_15","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 Posture"},{"key":"ref_16","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":"J. Sens."},{"key":"ref_17","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 Posture"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1519\/JPT.0b013e3182abe779","article-title":"Fall detection devices and their use with older adults: A systematic review","volume":"37","author":"Chaudhuri","year":"2014","journal-title":"J. Geriatr. Phys. Ther."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jatesiktat, P., and Ang, W.T. (2017, January 11\u201315). An elderly fall detection using a wrist-worn accelerometer and barometer. Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, South Korea.","DOI":"10.1109\/EMBC.2017.8036778"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Abbate, S., Avvenuti, M., Corsini, P., Light, J., and Vecchio, A. (2010). Monitoring of human movements for fall detection and activities recognition in elderly care using wireless sensor network: A survey. Wireless Sensor Networks: Application\u2014Centric Design, Intech.","DOI":"10.5772\/13802"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1016\/j.jbiomech.2010.07.005","article-title":"Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities","volume":"43","author":"Bourke","year":"2010","journal-title":"J. Biomech."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bourke, A.K., Klenk, J., Schwickert, L., Aminian, K., Ihlen, E.A.F., Mellone, S., Helbostad, J.L., Chiari, L., and Becker, C. (2016, January 16\u201320). Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: A machine learning approach. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591534"},{"key":"ref_23","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":"ref_24","doi-asserted-by":"crossref","unstructured":"Medrano, C., Plaza, I., Igual, R., S\u00e1nchez, \u00c1., and Castro, M. (2016). The effect of personalization on smartphone-based fall detectors. Sensors, 16.","DOI":"10.3390\/s16010117"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1016\/j.medengphy.2015.06.009","article-title":"A comparison of public datasets for acceleration-based fall detection","volume":"37","author":"Igual","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_26","first-page":"81","article-title":"Fall detection using smartwatch sensor data with accessor architecture","volume":"Volume 10347","author":"Chen","year":"2017","journal-title":"Lecture Notes in Computer Science, Proceedings of the International Conference on Smart Health ICSH, Hong Kong, China, 26\u201327 June 2017"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kostopoulos, P., Nunes, T., Salvi, K., Deriaz, M., and Torrent, J. (2015, January 14\u201317). F2D: A fall detection system tested with real data from daily life of elderly people. Proceedings of the 17th International Conference on E-health Networking, Application Services (HealthCom), Boston, MA, USA.","DOI":"10.1109\/HealthCom.2015.7454533"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tasoulis, S.K., Doukas, C.N., Maglogiannis, I., and Plagianakos, V.P. (September, January 30). Statistical data mining of streaming motion data for fall detection in assistive environments. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6090632"},{"key":"ref_29","first-page":"259","article-title":"A smartwatch-based assistance system for the elderly performing fall detection, unusual inactivity recognition and medication reminding","volume":"Volume 223","author":"Deutsch","year":"2016","journal-title":"Studies in Health Technology and Informatics"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Casilari, E., and Oviedo-Jim\u00e9nez, M.A. (2015). Automatic fall detection system based on the combined use of a smartphone and a smartwatch. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0140929"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Vilarinho, T., Farshchian, B., Bajer, D.G., Dahl, O.H., Egge, I., Hegdal, S.S., L\u00f8nes, A., Slettevold, J.N., and Weggersen, S.M. (2015, January 26\u201328). A Combined Smartphone and Smartwatch Fall Detection System. Proceedings of the IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, UK.","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.216"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gjoreski, H., Bizjak, J., and Gams, M. (2016, January 14\u201316). Using Smartwatch as Telecare and Fall Detection Device. Proceedings of the 12th International Conference on Intelligent Environments (IE), London, UK.","DOI":"10.1109\/IE.2016.55"},{"key":"ref_33","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 Mob. Comput."},{"key":"ref_34","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 Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Leutheuser, H., Schuldhaus, D., and Eskofier, B.M. (2013). Hierarchical, multi-sensor based classification of daily life activities: Comparison with state-of-the-art algorithms using a benchmark dataset. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0075196"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1142\/S0129065716500374","article-title":"Generalized models for the classification of abnormal movements in daily life and its applicability to epilepsy convulsion recognition","volume":"26","author":"Villar","year":"2016","journal-title":"Int. J. Neural Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artific. Intell. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1155\/2017\/6043069","article-title":"An IoT platform for epilepsy monitoring and supervising","volume":"2017","author":"Vergara","year":"2017","journal-title":"J. Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1450036","DOI":"10.1142\/S0129065714500361","article-title":"Improving human activity recognition and its application in early stroke diagnosis","volume":"25","author":"Villar","year":"2015","journal-title":"Int. J. Neural Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"18209","DOI":"10.3390\/s150818209","article-title":"Distance-constraint k-nearest neighbor searching in mobile sensor networks","volume":"15","author":"Han","year":"2015","journal-title":"Sensors"},{"key":"ref_41","unstructured":"R Development Core Team (2008). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_42","unstructured":"Kuhn, M. (2018, January 15). The Caret Package. Available online: http:\/\/topepo.github.io\/caret\/index.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1350\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:02:20Z","timestamp":1760194940000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1350"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,26]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["s18051350"],"URL":"https:\/\/doi.org\/10.3390\/s18051350","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,26]]}}}