{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T20:19:20Z","timestamp":1781036360232,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,12,22]],"date-time":"2017-12-22T00:00:00Z","timestamp":1513900800000},"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 fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k-nearest neighbor (k-NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.<\/jats:p>","DOI":"10.3390\/s18010020","type":"journal-article","created":{"date-parts":[[2017,12,22]],"date-time":"2017-12-22T05:50:19Z","timestamp":1513921819000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection"],"prefix":"10.3390","volume":"18","author":[{"given":"I","family":"Putra","sequence":"first","affiliation":[{"name":"School of Engineering, Macquarie University, Sydney 2109, Australia"},{"name":"Faculty of Engineering, Environment &amp; Computing, Coventry University, CV1 5FB Coventry, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2710-6927","authenticated-orcid":false,"given":"James","family":"Brusey","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Environment &amp; Computing, Coventry University, CV1 5FB Coventry, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2943-2037","authenticated-orcid":false,"given":"Elena","family":"Gaura","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Environment &amp; Computing, Coventry University, CV1 5FB Coventry, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rein","family":"Vesilo","sequence":"additional","affiliation":[{"name":"School of Engineering, Macquarie University, Sydney 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,22]]},"reference":[{"key":"ref_1","unstructured":"Age UK (2017, April 19). Stop Falling: Start Saving Lives and Money. Available online: http:\/\/www.ageuk.org.uk\/documents\/en-gb\/campaigns\/stop_falling_report_web.pdf?dtrk=true."},{"key":"ref_2","unstructured":"WHO (2017, February 01). Falls. Available online: http:\/\/www.who.int\/mediacentre\/factsheets\/fs344\/en\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1093\/ageing\/afh028","article-title":"Consequences of falling in older men and women and risk factors for health service use and functional decline","volume":"33","author":"Stel","year":"2004","journal-title":"Age Ageing"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fleming, J., and Brayne, C. (2008). Inability to get up after falling, subsequent time on floor, and summoning help: Prospective cohort study in people over 90. BMJ, 337.","DOI":"10.1136\/bmj.a2227"},{"key":"ref_5","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":"Biomed. Eng. OnLine"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bourke, A.K., O\u2019Donovan, K.J., Nelson, J., and OLaighin, G.M. (2008, January 20\u201325). Fall-detection through vertical velocity thresholding using a tri-axial accelerometer characterized using an optical motion-capture system. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada.","DOI":"10.1109\/IEMBS.2008.4649792"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1109\/TNSRE.2014.2357806","article-title":"Inertial Sensing-Based Pre-Impact Detection of Falls Involving Near-Fall Scenarios","volume":"23","author":"Lee","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Choi, Y., Ralhan, A.S., and Ko, S. (2011, January 26\u201329). A study on machine learning algorithms for fall detection and movement classification. Proceedings of the 2011 International Conference on Information Science and Applications, ICISA 2011, Jeju Island, Korea.","DOI":"10.1109\/ICISA.2011.5772404"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Diep, N.N., Pham, C., and Phuong, T.M. (2013, January 15\u201318). A classifier based approach to real-time fall detection using low-cost wearable sensors. Proceedings of the International Conference of Soft Computing and Pattern Recognition (SoCPaR), Hanoi, Vietnam.","DOI":"10.1109\/SOCPAR.2013.7054110"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dinh, C., and Struck, M. (2009, January 24\u201326). A new real-time fall detection approach using fuzzy logic and a neural network. Proceedings of the 6th International Workshop on Wearable Micro and Nano Technologies for Personalized Health (pHealth), Oslo, Norway.","DOI":"10.1109\/PHEALTH.2009.5754822"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1145\/2600617.2600621","article-title":"A multi-sensor approach for fall risk prediction and prevention in elderly","volume":"14","author":"Majumder","year":"2014","journal-title":"ACM SIGAPP Appl. Comput. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ojetola, O., Gaura, E.I., and Brusey, J. (2011, January 25\u201328). Fall detection with wearable sensors\u2014Safe (SmArt Fall dEtection). Proceedings of the 7th International Conference on Intelligent Environments (IE), Nottingham, UK.","DOI":"10.1109\/IE.2011.38"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Putra, I.P.E.S., Brusey, J., and Gaura, E. (2015, January 14\u201316). A Cascade-Classifier Approach for Fall Detection. Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare, MOBIHEALTH\u201915, London, UK.","DOI":"10.4108\/eai.14-10-2015.2261619"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Vallejo, M., Isaza, C., and Lopez, J. (2013, January 3\u20137). Artificial Neural Networks as an alternative to traditional fall detection methods. Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6609833"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bagal\u00e0, 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_17","doi-asserted-by":"crossref","unstructured":"Hossain, F., Ali, M.L., Islam, M.Z., and Mustafa, H. (2016, January 17\u201318). A direction-sensitive fall detection system using single 3D accelerometer and learning classifier. Proceedings of the 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec), Dhaka, Bangladesh.","DOI":"10.1109\/MEDITEC.2016.7835372"},{"key":"ref_18","unstructured":"Ojetola, O. (2013). Detection of Human Falls using Wearable Sensors. [Ph.D. Thesis, Coventry University]."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3281","DOI":"10.1049\/iet-com.2011.0228","article-title":"A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data","volume":"6","author":"Erdogan","year":"2012","journal-title":"IET Commun."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"253","DOI":"10.3176\/proc.2014.3.08","article-title":"Fall detection in the older people: From laboratory to real-life","volume":"63","author":"Jamsa","year":"2014","journal-title":"Proc. Estonian Acad. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/JBHI.2014.2328593","article-title":"A Smart Phone-Based Pocket Fall Accident Detection, Positioning, and Rescue System","volume":"19","author":"Kau","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.gaitpost.2006.09.012","article-title":"Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm","volume":"26","author":"Bourke","year":"2007","journal-title":"Gait Posture"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kangas, M., Konttila, A., Winblad, I., and J\u00e4ms\u00e4, T. (2007, January 22\u201326). Determination of simple thresholds for accelerometry-based parameters for fall detection. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4352552"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Noury, N., Fleury, A., Rumeau, P., Bourke, A.K., Laighin, G.O., Rialle, V., and Lundy, J.E. (2007, January 22\u201326). Fall detection\u2014Principles and Methods. Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4352627"},{"key":"ref_26","unstructured":"Wang, S., Yang, J., Chen, N., Chen, X., and Zhang, Q. (2005, January 13\u201315). Human activity recognition with user-free accelerometers in the sensor networks. Proceedings of the International Conference on Neural Networks and Brain, Beijing, China."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/0957-0233\/20\/7\/075204","article-title":"Postural activity monitoring for increasing safety in bomb disposal missions","volume":"20","author":"Brusey","year":"2009","journal-title":"Meas. Sci. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gjoreski, H., Lustrek, M., and Gams, M. (2011, January 25\u201328). Accelerometer Placement for Posture Recognition and Fall Detection. Proceedings of the 7th International Conference on Intelligent Environments (IE), Nottingham, UK.","DOI":"10.1109\/IE.2011.11"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITB.2005.856864","article-title":"Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring","volume":"10","author":"Karantonis","year":"2006","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ojetola, O., Gaura, E., and Brusey, J. (2015, January 18\u201320). Data Set for Fall Events and Daily Activities from Inertial Sensors. Proceedings of the 6th ACM Multimedia Systems Conference, MMSys \u201915, Portland, OR, USA.","DOI":"10.1145\/2713168.2713198"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., Lopez, J.D., and Vargas-Bonilla, J.F. (2017). SisFall: A Fall and Movement Dataset. Sensors, 17.","DOI":"10.3390\/s17010198"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.gaitpost.2011.11.016","article-title":"Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects","volume":"35","author":"Kangas","year":"2012","journal-title":"Gait Posture"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1109\/JSEN.2010.2045498","article-title":"SHIMMER\u2014A Wireless Sensor Platform for Noninvasive Biomedical Research","volume":"10","author":"Burns","year":"2010","journal-title":"IEEE Sens. J."},{"key":"ref_35","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1109\/TBCAS.2013.2254485","article-title":"Leveraging knowledge from physiological data: On-body heat stress risk prediction with sensor networks","volume":"7","author":"Gaura","year":"2013","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_37","unstructured":"Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., and Garnett, R. (2015). Precision-Recall-Gain Curves: PR Analysis Done Right. Advances in Neural Information Processing Systems 28, Curran Associates, Inc."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dunkels, A., Osterlind, F., Tsiftes, N., and He, Z. (2007, January 25\u201326). Software-based On-line Energy Estimation for Sensor Nodes. Proceedings of the 4th Workshop on Embedded Networked Sensors, EmNets \u201907, Cork, UK.","DOI":"10.1145\/1278972.1278979"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Putra, I.P.E.S., and Vesilo, R. (2017, January 13\u201315). Window-size impact on detection rate of wearable-sensor-based fall detection using supervised machine learning. Proceedings of the 1st IEEE Life Sciences Conference, Sydney, Australia.","DOI":"10.1109\/LSC.2017.8268134"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, J., Kwong, K., Chang, D., Luk, J., and Bajcsy, R. (2005, January 17\u201318). Wearable sensors for reliable fall detection. Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China.","DOI":"10.1109\/IEMBS.2005.1617246"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.3390\/s140406474","article-title":"Window size impact in human activity recognition","volume":"14","author":"Banos","year":"2014","journal-title":"Sensors"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1109\/JBHI.2015.2432454","article-title":"Posture and Activity Recognition and Energy Expenditure Estimation in a Wearable Platform","volume":"19","author":"Sazonov","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cippitelli, E., Gasparrini, S., Gambi, E., and Spinsante, S. (2016, January 1\u20133). An Integrated Approach to Fall Detection and Fall Risk Estimation Based on RGB-Depth and Inertial Sensors. Proceedings of the 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion, DSAI 2016, Vila Real, Portugal.","DOI":"10.1145\/3019943.3019979"},{"key":"ref_44","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/1\/20\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:55:04Z","timestamp":1760208904000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/1\/20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,22]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["s18010020"],"URL":"https:\/\/doi.org\/10.3390\/s18010020","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,22]]}}}