{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T10:23:46Z","timestamp":1768559026011,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2013,12,10]],"date-time":"2013-12-10T00:00:00Z","timestamp":1386633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accidental falls are the major cause of serious injuries in toddlers, with most of these falls happening at home. Instead of providing immediate fall detection based on short-term observations, this paper proposes an early-warning childcare system to monitor fall-prone behaviors of toddlers at home. Using 3D human skeleton tracking and floor plane detection based on depth images captured by a Kinect system, eight fall-prone behavioral modules of toddlers are developed and organized according to four essential criteria: posture, motion, balance, and altitude. The final fall risk assessment is generated by a multi-modal fusion using either a weighted mean thresholding or a support vector machine (SVM) classification. Optimizations are performed to determine local parameter in each module and global parameters of the multi-modal fusion. Experimental results show that the proposed system can assess fall risks and trigger alarms with an accuracy rate of 92% at a speed of 20 frames per second.<\/jats:p>","DOI":"10.3390\/s131216985","type":"journal-article","created":{"date-parts":[[2013,12,10]],"date-time":"2013-12-10T12:32:12Z","timestamp":1386678732000},"page":"16985-17005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home"],"prefix":"10.3390","volume":"13","author":[{"given":"Mau-Tsuen","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Computer Science & Information Engineering, National Dong-Hwa University, No. 1, Sec. 2, Da-Hsueh Rd., Shoufeng, Hualien 974, Taiwan"}]},{"given":"Min-Wen","family":"Chuang","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Information Engineering, National Dong-Hwa University, No. 1, Sec. 2, Da-Hsueh Rd., Shoufeng, Hualien 974, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2013,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1093\/jpepsy\/jsh046","article-title":"Understanding toddlers' in-home injuries: I. Context, correlates, and determinants","volume":"29","author":"Morrongiello","year":"2004","journal-title":"J. Pediatr. Psychol."},{"key":"ref_2","unstructured":"Purwar, A., Jeong, D., and Chung, W. (2007, January 17\u201320). Activity Monitoring from Real-Time Triaxial Accelerometer Data Using Sensor Network. Seoul, Korea."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.medengphy.2006.12.001","article-title":"A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor","volume":"30","author":"Bourke","year":"2008","journal-title":"Med. Eng. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MPRV.2004.1316817","article-title":"A smart sensor to detect the falls of the elderly","volume":"3","author":"Sixsmith","year":"2004","journal-title":"IEEE Pervasive Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"16920","DOI":"10.3390\/s121216920","article-title":"Privacy-preserved behavior analysis and fall detection by an infrared ceiling sensor network","volume":"12","author":"Tao","year":"2012","journal-title":"Sensors"},{"key":"ref_6","first-page":"875","article-title":"Fall detection from human shape and motion history using video surveillance","volume":"2","author":"Rougier","year":"2007","journal-title":"Int. Conf. Adv. Inf. Netw. Appl. Workshops"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Diraco, G., Leone, A., and Siciliano, P. (2010, January 8\u201312). An Active Vision System for Fall Detection and Posture Recognition in Elderly Healthcare. Dresden, Germany.","DOI":"10.1109\/DATE.2010.5457055"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1109\/JSEN.2013.2245231","article-title":"HMM based human fall detection and prediction method using tri-axial accelerometer","volume":"13","author":"Tong","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1109\/JSEN.2008.2012212","article-title":"Mobile human airbag system for fall protection using MEMS sensors and embedded SVM classifier","volume":"9","author":"Shi","year":"2009","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/j.medengphy.2007.12.003","article-title":"The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls","volume":"30","author":"Bourke","year":"2008","journal-title":"Med. Eng. Phys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3475","DOI":"10.1016\/j.jbiomech.2008.08.009","article-title":"A wearable system for pre-impact fall detection","volume":"41","author":"Nyan","year":"2008","journal-title":"J. Biomech."},{"key":"ref_12","unstructured":"Microsoft Corp Kinect for Xbox 360. Available online: http:\/\/www.xbox.com\/en-GB\/kinect."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MPRV.2010.19","article-title":"Detecting fall risk factors for toddlers","volume":"10","author":"Na","year":"2011","journal-title":"IEEE Pervasive Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nomori, K., Nishida, Y., Motomura, Y., Yamanaka, T., and Komatsubara, A. (2009, January 1\u20132). Developing a Control Model of Infant Climbing Behavior for Injury Prevention. Bangkok, Thailand.","DOI":"10.1109\/ICTKE.2009.5397337"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"573","DOI":"10.3390\/s120100573","article-title":"Visual sensor based abnormal event detection with moving shadow removal in home healthcare applications","volume":"12","author":"Lee","year":"2012","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ni, B., Dat, N., and Moulin, P. (2012, January 25\u201330). RGBD-Camera Based Get-Up Event Detection for Hospital Fall Prevention. Kyoto, Japan.","DOI":"10.1109\/ICASSP.2012.6287947"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6695","DOI":"10.3390\/s120506695","article-title":"Categorization of indoor places using the kinect sensor","volume":"12","author":"Mozos","year":"2012","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. (2011, January 20\u201325). Real-Time Human Pose Recognition in Parts from Single Depth Images. Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995316"},{"key":"ref_20","unstructured":"Platt, J. (1999). Advances in Large Margin Classifiers, MIT Press."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1214\/aoms\/1177699147","article-title":"Statistical inference for probabilistic functions of finite state markov chains","volume":"37","author":"Baum","year":"1966","journal-title":"Ann. Math. Stat."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1002\/ima.20046","article-title":"A multi-modal fusion system for people detection and tracking","volume":"15","author":"Yang","year":"2005","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_25","unstructured":"Intel Corp Open Source Computer Vision. Available online: http:\/\/opencv.org."},{"key":"ref_26","unstructured":"Obdrzalek, S., Kurillo, G., Ofli, F., Bajcsy, R., Seto, E., Jimison, H., and Pavel, M. (September, January 28). Accuracy and Robustness of Kinect Pose Estimation in the Context of Coaching of Elderly Population. San Diego, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/12\/16985\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:51:14Z","timestamp":1760219474000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/12\/16985"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,12,10]]},"references-count":26,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2013,12]]}},"alternative-id":["s131216985"],"URL":"https:\/\/doi.org\/10.3390\/s131216985","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,12,10]]}}}