{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:05:31Z","timestamp":1774541131269,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T00:00:00Z","timestamp":1625529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["2018R1D1A1B07048575"],"award-info":[{"award-number":["2018R1D1A1B07048575"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Minstry of Trade, Industry and Energy, South Korea","award":["20006386"],"award-info":[{"award-number":["20006386"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different processing conditions (normalization, equalization, increase in the number of training data, and additional training with external data). Three-axis acceleration and angular velocity data were obtained from 30 healthy male subjects by attaching an IMU to the middle of the left and right anterior superior iliac spines (ASIS). Internal and external tests were performed using our lab dataset and SisFall public dataset, respectively. The results showed that ANN and SVM were suitable for the time-series and discrete data, respectively. The classification performance generally decreased, and thus, specific feature vectors from the raw data were necessary when untrained motions were tested using a public dataset. Normalization made SVM and ANN more and less effective, respectively. Equalization increased the sensitivity, even though it did not improve the overall performance. The increase in the number of training data also improved the classification performance. Machine learning was vulnerable to untrained motions, and data of various movements were needed for the training.<\/jats:p>","DOI":"10.3390\/s21144638","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T11:36:44Z","timestamp":1625571404000},"page":"4638","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4681-6318","authenticated-orcid":false,"given":"Bummo","family":"Koo","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2053-8994","authenticated-orcid":false,"given":"Jongman","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea"}]},{"given":"Yejin","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7531-802X","authenticated-orcid":false,"given":"Youngho","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,6]]},"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":"Yoshida-Intern, S. (2007). A Global Report on Falls Prevention Epidemiology of Falls, WHO."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1023\/B:JOHE.0000016717.22032.03","article-title":"Falls and fall-related injuries among the elderly: A survey of residential-care facilities in a Swedish municipality","volume":"29","author":"Sadigh","year":"2004","journal-title":"J. Community Health"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1093\/ageing\/afm169","article-title":"Fear of falling: Measurement strategy, prevalence, risk factors and consequences among older persons","volume":"37","author":"Scheffer","year":"2008","journal-title":"Age Ageing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"145","DOI":"10.2147\/CIA.S191832","article-title":"Effects of a fall prevention program in elderly: A pragmatic observational study in two orthopedic departments","volume":"14","author":"Lydersen","year":"2019","journal-title":"Clin. Interv. Aging"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100820","DOI":"10.1016\/j.ijotn.2020.100820","article-title":"The effectiveness of a recurrent fall prevention program applied to elderly people undergoing fracture treatment","volume":"40","author":"Bayraktar","year":"2021","journal-title":"Int. J. Orthop. Trauma Nurs."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Palestra, G., Rebiai, M., Courtial, E., and Koutsouris, D. (2019). Evaluation of a rehabilitation system for the elderly in a day care center. Information, 10.","DOI":"10.3390\/info10010003"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Baldewijns, G., Debard, G., Mertes, G., Croonenborghs, T., and Vanrumste, B. (2017, January 11\u201315). Improving the accuracy of existing camera based fall detection algorithms through late fusion. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju, Korea.","DOI":"10.1109\/EMBC.2017.8037406"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1093\/ageing\/16.3.189","article-title":"Prospective study of restriction of acitivty in old people after falls","volume":"16","author":"Vellas","year":"1987","journal-title":"Age Ageing"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lord, S.R., Sherrington, C., and Menz, H.B. (2003). Falls in Older People: Risk Factors and Strategies for Prevention, Cambridge University Press.","DOI":"10.1007\/978-0-85729-402-9_12"},{"key":"ref_11","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_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11517-016-1504-y","article-title":"A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials","volume":"55","author":"Aziz","year":"2017","journal-title":"Med Biol. Eng. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jung, H., Koo, B., Kim, J., Kim, T., Nam, Y., and Kim, Y. (2020). Enhanced algorithm for the detection of preimpact fall for wearable airbags. Sensors, 20.","DOI":"10.3390\/s20051277"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vallabh, P., Malekian, R., Ye, N., and Bogatinoska, D.C. (2016, January 22\u201324). Fall detection using machine learning algorithms. Proceedings of the 2016 24th International Conference on Software, Telecommunications and Computer Networks, Split, Croatia.","DOI":"10.1109\/SOFTCOM.2016.7772142"},{"key":"ref_17","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":"Barshan","year":"2014","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.asoc.2015.10.062","article-title":"Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic","volume":"39","author":"Gibson","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yodpijit, N., Sittiwanchai, T., and Jongprasithporn, M. (2017, January 24\u201326). The development of artificial neural networks (ANN) for falls detection. Proceedings of the 2017 3rd International Conference on Control, Automation and Robotics, Nagoya, Japan.","DOI":"10.1109\/ICCAR.2017.7942757"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1847979018787905","DOI":"10.1177\/1847979018787905","article-title":"An artificial neural network\u2013based fall detection","volume":"10","author":"Yoo","year":"2018","journal-title":"Int. J. Eng. Bus. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cao, L., Liu, Z., and Huang, T.S. (2010, January 13\u201318). Cross-dataset action detection. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539875"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105265","DOI":"10.1016\/j.cmpb.2019.105265","article-title":"A cross-dataset deep learning-based classifier for people fall detection and identification","volume":"184","author":"Castro","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., L\u00f3pez, J.D., and Vargas-Bonilla, J.F. (2017). SisFall: A fall and movement dataset. Sensors, 17.","DOI":"10.3390\/s17010198"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Putra, I.P.E.S., Brusey, J., Gaura, E., and Vesilo, R. (2018). An event-triggered machine learning approach for accelerometer-based fall detection. Sensors, 18.","DOI":"10.3390\/s18010020"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3303","DOI":"10.1109\/JSEN.2019.2955141","article-title":"An analysis of segmentation approaches and window sizes in wearable-based critical fall detection systems with machine learning models","volume":"20","author":"Liu","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"29","DOI":"10.4236\/ojab.2014.34004","article-title":"An efficient and robust fall detection system using wireless gait analysis sensor with artificial neural network (ANN) and support vector machine (SVM) algorithms","volume":"3","author":"Nukala","year":"2015","journal-title":"Open J. Appl. Biosens."},{"key":"ref_27","unstructured":"Japkowicz, N. (2020, January 26\u201329). The class imbalance problem: Significance and strategies. Proceedings of the 2000 International Conference on Artificial Intelligence, Las Vegas, NV, USA."},{"key":"ref_28","first-page":"179","article-title":"Addressing the curse of imbalanced training sets: One-sided selection","volume":"97","author":"Kubat","year":"1997","journal-title":"ICML"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lewis, D.D., and Catlett, J. (1994). Heterogeneous uncertainty sampling for supervised learning. Mach. Learn. Proc., 148\u2013156.","DOI":"10.1016\/B978-1-55860-335-6.50026-X"},{"key":"ref_30","first-page":"73","article-title":"Data mining for direct marketing: Problems and solutions","volume":"98","author":"Ling","year":"1998","journal-title":"Proc. Fourth Int. Conf. Knowl. Discov. Data Min."},{"key":"ref_31","first-page":"012102","article-title":"An accurate fall detection system for the elderly people using smartphone inertial sensors","volume":"1530","author":"Kadhum","year":"2020","journal-title":"J. Phys."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TSMC.2016.2562509","article-title":"Physical activity recognition from smartphone accelerometer data for user context awareness sensing","volume":"47","author":"Wannenburg","year":"2016","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_33","first-page":"25","article-title":"A novel class imbalance learning using intelligent under-sampling","volume":"5","author":"Satuluri","year":"2012","journal-title":"Int. J. Database Theory Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Khojasteh, S.B., Villar, J.R., Chira, C., Gonz\u00e1lez, V.M., and De la Cal, E. (2018). Improving fall detection using an on-wrist wearable accelerometer. Sensors, 18.","DOI":"10.3390\/s18051350"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, G., Li, Q., Wang, L., Zhang, Y., and Liu, Z. (2019). Elderly fall detection with an accelerometer using lightweight neural networks. Electronics, 8.","DOI":"10.3390\/electronics8111354"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1007\/s12541-019-00195-w","article-title":"Development of an armband EMG module and a pattern recognition algorithm for the 5-finger myoelectric hand prosthesis","volume":"20","author":"Kim","year":"2019","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1007\/s12541-020-00398-6","article-title":"Post-fall detection using ANN based on ranking algorithms","volume":"21","author":"Koo","year":"2020","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Musci, M., De Martini, D., Blago, N., Facchinetti, T., and Piastra, M. (2020). Online fall detection using recurrent neural networks on smart wearable devices. IEEE Trans. Emerg. Top. Comput., 3027454.","DOI":"10.1109\/TETC.2020.3027454"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"63","DOI":"10.3389\/fbioe.2020.00063","article-title":"A novel hybrid deep neural network to predict pre-impact fall for older people based on wearable inertial sensors","volume":"8","author":"Yu","year":"2020","journal-title":"Front. Bioeng. Biotechnol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4638\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:26:46Z","timestamp":1760164006000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4638"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,6]]},"references-count":39,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21144638"],"URL":"https:\/\/doi.org\/10.3390\/s21144638","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,6]]}}}