{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T11:41:34Z","timestamp":1782387694534,"version":"3.54.5"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,5]],"date-time":"2018-04-05T00:00:00Z","timestamp":1522886400000},"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>The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people often stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches have not been tested with the target population or cannot be feasibly implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We tested our approach with the SisFall dataset achieving 99.4% of accuracy. We then validated it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.<\/jats:p>","DOI":"10.3390\/s18041101","type":"journal-article","created":{"date-parts":[[2018,4,5]],"date-time":"2018-04-05T16:50:58Z","timestamp":1522947058000},"page":"1101","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Real-Life\/Real-Time Elderly Fall Detection with a Triaxial Accelerometer"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9143-6074","authenticated-orcid":false,"given":"Angela","family":"Sucerquia","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda, Instituci\u00f3n Universitaria ITM, Cra. 65, 98A-75 Medell\u00edn, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2213-1186","authenticated-orcid":false,"given":"Jos\u00e9","family":"L\u00f3pez","sequence":"additional","affiliation":[{"name":"SISTEMIC, Facultad de Ingenier\u00eda, Universidad de Antiquia UDEA, Calle 70, No. 52-21 Medell\u00edn, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8794-6429","authenticated-orcid":false,"given":"Jes\u00fas","family":"Vargas-Bonilla","sequence":"additional","affiliation":[{"name":"SISTEMIC, Facultad de Ingenier\u00eda, Universidad de Antiquia UDEA, Calle 70, No. 52-21 Medell\u00edn, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,5]]},"reference":[{"key":"ref_1","unstructured":"Masdeu, J., Sudarsky, L., and Wolfson, L. (1997). Gait Disorders of Aging. Falls and Therapeutic Strategies, Lippincot-Raven."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1093\/ageing\/26.3.189","article-title":"Fear of falling and restriction of mobility in elderly fallers","volume":"26","author":"Vellas","year":"1997","journal-title":"Age Ageing"},{"key":"ref_3","unstructured":"Lord, S., Sherrington, C., and Menz, H. (2001). Falls in Older People: Risk Factors and Strategies for Prevention, Cambridge University Press. [1st ed.]."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1093\/ageing\/afh106","article-title":"Fear-related avoidance of activities, falls and physical frailty. A prospective community-based cohort study","volume":"33","author":"Delbaere","year":"2004","journal-title":"Age Ageing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1111\/j.1753-6405.1993.tb00143.x","article-title":"An epidemiological study of falls in older community-dwelling women: The Randwick falls and fractures study","volume":"17","author":"Lord","year":"1993","journal-title":"Aust. J. Public Health"},{"key":"ref_6","first-page":"1221","article-title":"Consecuencias De Las Ca\u00eddas En Ancianos Institucionalizados","volume":"23","author":"Henao","year":"2009","journal-title":"Revista de la Asociaci\u00f3n Colombiana de Gerontolog\u00eda y Geriatr\u00eda"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","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_8","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1109\/JSEN.2011.2146246","article-title":"Sensors-Based Wearable Systems for Monitoring of Human Movement and Falls","volume":"12","author":"Shany","year":"2012","journal-title":"IEEE Sensors J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12900","DOI":"10.3390\/s140712900","article-title":"Automatic fall monitoring: A review","volume":"14","author":"Pannurat","year":"2014","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7181","DOI":"10.3390\/s140407181","article-title":"Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues","volume":"14","author":"Habib","year":"2014","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., L\u00f3pez, J., and Vargas-Bonilla, F. (2017). SisFall: A fall and movement dataset. Sensors, 17.","DOI":"10.3390\/s17010198"},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, J.D., Ocampo, C., Sucerquia, A., and Vargas-Bonilla, F. (2016, January 26\u201328). Analyzing multiple accelerometer configurations to detect falls and motion. Proceedings of the Latin American Congress on Biomedical Engineering, Santander, Colombia.","DOI":"10.1007\/978-981-10-4086-3_43"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6640","DOI":"10.1109\/JSEN.2015.2464774","article-title":"An On-Node Processing Approach for Anomaly Detection in Gait","volume":"15","author":"Cola","year":"2015","journal-title":"IEEE Sensors J."},{"key":"ref_16","unstructured":"Oner, M., Pulcifer-Stump, J.A., Seeling, P., and Kaya, T. (Sepember, January 28). Towards the Run and Walk Activity Classification through Step Detection\u2014An Android Application. Proceedings of the 34th Annual International Conference of the IEEE EMBS, San Diego, CA, USA."},{"key":"ref_17","first-page":"382","article-title":"Validity of a trunk-mounted accelerometer to assess peak accelerations during walking, jogging and running","volume":"2014","author":"Wundersitz","year":"2014","journal-title":"Eur. J. Sport Sci."},{"key":"ref_18","unstructured":"Clements, C.M., Buller, M.J., Welles, A.P., and Tharion, W.J. (Sepember, January 28). Real Time Gait Pattern Classification from Chest Worn Accelerometry During a Loaded Road March. Proceedings of the IEEE 34th Annual International Conference of the EMBS, San Diego, CA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, J.D., Sucerquia, A., Duque-Mu\u00f1oz, L., and Vargas-Bonilla, F. (2015, January 26\u201328). Walk and jog characterization using a triaxial accelerometer. Proceedings of the IEEE International Conference on Ubiquitous Computing and Communications (IUCC), Liverpool, UK.","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.210"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A New Approach to Linear Filtering and Prediction Problems","volume":"82","author":"Kalman","year":"1960","journal-title":"Trans. ASME J. Basic Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1109\/TNSRE.2012.2230189","article-title":"Quantitative Description of the Lie-to-Sit-to-Stand-to-Walk Transfer by a Single Body-Fixed Sensor","volume":"21","author":"Klenk","year":"2013","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_22","first-page":"S7","article-title":"Measuring balance in the elderly: Validation of an instrument","volume":"83","author":"Berg","year":"1992","journal-title":"Can. J. Public Health"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jnca.2016.03.013","article-title":"User context recognition using smartphone sensors and classification models","volume":"66","author":"Otebolaku","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1016\/j.medengphy.2013.07.003","article-title":"Automated detection of gait initiation and termination using wearable sensors","volume":"35","author":"Novak","year":"2013","journal-title":"Med. Eng. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yuan, X., Yu, S., Dan, Q., Wang, G., and Liu, S. (2015, January 11\u201314). Fall Detection Analysis with Wearable MEMS-based Sensors. Proceedings of the 16th International Conference on Electronic Packaging Technology (ICEPT), Changsha, China.","DOI":"10.1109\/ICEPT.2015.7236791"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, H., and Yang, Y.-L. (2016, January 27\u201329). Research of elderly fall detection based on dynamic time warping algorithm. Proceedings of the 35th Chinese Control Conference, Chengdu, China.","DOI":"10.1109\/ChiCC.2016.7554161"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, J., Bai, S., and Wang, X. (2017). An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier. Sensors, 17.","DOI":"10.3390\/s17061393"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mao, A., Ma, X., He, Y., and Luo, J. (2017). Highly Portable, Sensor-Based System for Human Fall Monitoring. Sensors, 17.","DOI":"10.3390\/s17092096"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.jnca.2017.02.006","article-title":"An inferential real-time falling posture reconstruction for Internet of healthcare things","volume":"89","author":"Zhang","year":"2017","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1109\/TAC.1971.1099826","article-title":"An Introduction to Observers","volume":"16","author":"Luenberger","year":"1971","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.irbm.2008.08.002","article-title":"A proposal for the classification and evaluation of fall detectors","volume":"29","author":"Noury","year":"2008","journal-title":"IRBM"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.medengphy.2010.11.003","article-title":"Comparison of acceleration signals of simulated and real-world backward falls","volume":"33","author":"Klenk","year":"2011","journal-title":"Med. Eng. Phys."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Noury, N., Fleury, A., Rumeau, P., Bourke, A., Laighin, G., Rialle, V., and Lundy, J. (2007, January 22\u201326). Fall detection\u2014Principles and Methods. Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4352627"},{"key":"ref_34","unstructured":"(2018, April 02). Sisfall 2: Real-Life\/Real-Time Elderly ADL. Available online: http:\/\/sistemic.udea.edu.co\/en\/research\/projects\/sucerquia2018\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1101\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:59:43Z","timestamp":1760194783000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/4\/1101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,5]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["s18041101"],"URL":"https:\/\/doi.org\/10.3390\/s18041101","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,5]]}}}