{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T10:03:50Z","timestamp":1775124230996,"version":"3.50.1"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T00:00:00Z","timestamp":1604102400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National High Technology Research and Development Program of China","award":["2013AA01A215"],"award-info":[{"award-number":["2013AA01A215"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2020,10,31]]},"abstract":"<jats:p>Falling is a common phenomenon in the life of the elderly, and it is also one of the 10 main causes of serious health injuries and death of the elderly. In order to prevent falling of the elderly, a real-time fall prediction system is installed on the wearable intelligent device, which can timely trigger the alarm and reduce the accidental injury caused by falls. At present, most algorithms based on single-sensor data cannot accurately describe the fall state, while the fall detection algorithm based on multisensor data integration can improve the sensitivity and specificity of prediction. In this study, we design a fall detection system based on multisensor data fusion and analyze the four stages of falls using the data of 100 volunteers simulating falls and daily activities. In this paper, data fusion method is used to extract three characteristic parameters representing human body acceleration and posture change, and the effectiveness of the multisensor data fusion algorithm is verified. The sensitivity is 96.67%, and the specificity is 97%. It is found that the recognition rate is the highest when the training set contains the largest number of samples in the training set. Therefore, after training the model based on a large amount of effective data, its recognition ability can be improved, and the prevention of fall possibility will gradually increase. In order to compare the applicability of random forest and support vector machine (SVM) in the development of wearable intelligent devices, two fall posture recognition models were established, respectively, and the training time and recognition time of the models are compared. The results show that SVM is more suitable for the development of wearable intelligent devices.<\/jats:p>","DOI":"10.1155\/2020\/8826088","type":"journal-article","created":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T01:50:06Z","timestamp":1604195406000},"page":"1-9","source":"Crossref","is-referenced-by-count":32,"title":["Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2004-6762","authenticated-orcid":true,"given":"Daohua","family":"Pan","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China"},{"name":"Department of Electronic and Information Engineering, Heilongjiang Vocational College for Nationalities, Harbin 150066, Heilongjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9215-7173","authenticated-orcid":true,"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China"}]},{"given":"Dongming","family":"Qu","sequence":"additional","affiliation":[{"name":"Department of Financial Technology, China Construction Bank, Harbin 150001, Heilongjiang, China"}]},{"given":"Zhan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/tmc.2016.2557795"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/2308183"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-018-3496-4"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cvi.2017.0119"},{"issue":"7","key":"5","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.3390\/s17071622","article-title":"Novel flexible wearable sensor materials and signal processing for vital sign and human activity monitoring","volume":"17","author":"S. Amir","year":"2017","journal-title":"Sensors"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2970469"},{"issue":"7","key":"7","first-page":"362","article-title":"Mechanical properties of flexible materials","volume":"66","author":"A. Shishido","year":"2017","journal-title":"Kobunshi"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/jsen.2016.2515101"},{"issue":"6","key":"9","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TII.2016.2585643","article-title":"A wearable sensor device for unobtrusive gait monitoring in daily life","volume":"12","author":"F. Lin","year":"2017","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"1","key":"10","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1186\/s12859-018-2264-5","article-title":"Random forest versus logistic regression: a large-scale benchmark experiment","volume":"19","author":"C. Raphael","year":"2018","journal-title":"Bmc Bioinformatics"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.03.056"},{"issue":"12","key":"12","doi-asserted-by":"crossref","first-page":"2048","DOI":"10.3390\/s16122048","article-title":"Wearable sensor-based human activity recognition method with multi-features extracted from hilbert-huang transform","volume":"16","author":"X. Huile","year":"2016","journal-title":"Sensors"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1136\/bmjgh-2016-000070"},{"issue":"4","key":"14","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.apmr.2016.11.003","article-title":"Wearable sensor-based biofeedback training for balance and gait in Parkinson\u2019s disease: a pilot randomized controlled trial","volume":"98","author":"I. Carpinella","year":"2016","journal-title":"Archives of Physical Medicine & Rehabilitation"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.3390\/s16020202"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.3390\/s16040445"},{"issue":"2","key":"17","doi-asserted-by":"crossref","first-page":"e9","DOI":"10.2196\/rehab.7259","article-title":"Mobile app to streamline the development of wearable sensor-based exercise biofeedback systems: system development and evaluation","volume":"4","author":"O. Martin","year":"2017","journal-title":"Jmir Rehabilitation & Assistive Technologies"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0187108"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2015.2494502"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1056\/nejmp1606181"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2017.2690524"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-1920-1"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-015-0394-x"},{"issue":"3","key":"24","doi-asserted-by":"crossref","first-page":"611","DOI":"10.3934\/jimo.2012.8.611","article-title":"Canonical duality solution for alternating support vector machine","volume":"8","author":"Y. Yuan","year":"2017","journal-title":"Journal of Industrial & Management Optimization"}],"container-title":["Mobile Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2020\/8826088.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2020\/8826088.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2020\/8826088.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T01:50:21Z","timestamp":1604195421000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/misy\/2020\/8826088\/"}},"subtitle":[],"editor":[{"given":"Jianhui","family":"Lv","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020,10,31]]},"references-count":24,"alternative-id":["8826088","8826088"],"URL":"https:\/\/doi.org\/10.1155\/2020\/8826088","relation":{},"ISSN":["1875-905X","1574-017X"],"issn-type":[{"value":"1875-905X","type":"electronic"},{"value":"1574-017X","type":"print"}],"subject":[],"published":{"date-parts":[[2020,10,31]]}}}